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纳入 script/doc/ref/output 及配置；忽略 data/（26G 原始/中间数据）。
git 身份：rain <wjs_Rain@126.com>。
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# 跨平台换行符归一化：仓库内统一 LF，Windows 检出为 CRLF，Linux/macOS 检出为 LF
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# ===== 大型原始/中间数据（不进 git）=====
# 原始/中间 h5ad 与 GSE 原始数据合计 ~26G，不进版本控制。
# 多设备接续时需单独传输（scp/rsync 等），见 README.md。
data/
# ===== Python =====
__pycache__/
*.py[cod]
*$py.class
*.so
.ipynb_checkpoints/
# 虚拟环境
.venv/
venv/
env/
ENV/
# 环境变量 / 密钥
.env
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# ===== 系统 =====
.DS_Store
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desktop.ini
# ===== 编辑器 / IDE =====
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AGENTS.md
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# AGENTS.md — RGC-ADOA 项目
## 项目概述
基于 Kang et al., *Science Advances* (2026)（`ref/sciadv.adx7815.pdf`）公开的单细胞数据，对 **OPA1 突变导致的常染色体显性视神经萎缩（ADOA）** 小鼠视网膜进行再分析。文献精读笔记见 `doc/文献精读_Kang2026_ADOA_OPA1.md`。
### 分析目标
1. **核心问题**：RGC、Müller 胶质细胞、小胶质细胞（microglia）在缺失/突变 OPA1 后发生什么改变？
2. **常规分析**：不同细胞类型的差异基因与富集分析，**重点关注这 3 类细胞的 IFN–JAK–STAT 通路是否异常活化**，泛化为炎症通路活化情况分析。
3. **研究假设**：OPA1 敲除 → 线粒体损伤 →（a）直接影响能量代谢；（b）损伤线粒体激活炎症通路 → 损伤神经元。
> 注：原文只做了能量代谢轴（ETC/糖酵解/线粒体自噬），**没有分析炎症通路、Müller 胶质细胞和小胶质细胞**——这是本项目的增量空间。
## 目录约定
| 目录 | 用途 |
| --------- | ------------------------- |
| `data/` | 原始数据 + 重要可复用中间数据（如 h5ad） |
| `ref/` | 参考文献、软件文档等，**只读** |
| `doc/` | 分析计划、分析报告、调研报告等 md 文档 |
| `script/` | 代码、脚本 |
| `output/` | 脚本输出；**图片默认 PNG，300 ppi** |
## 数据来源
- **snRNA-seq**：GEO [GSE292269](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292269)（BioProject PRJNA1237794）——**已下载**，`data/GSE292269_RAW.tar`（332MB）+ 解压至 `data/GSE292269/{WT,Opa1V291D_S1,Opa1V291D_S2}/`
- 3 个样本，均为 360 天小鼠全视网膜 pooled nuclei：
- GSM8855044 — WT（Opa1+/+）
- GSM8855045 — Opa1V291D_S1
- GSM8855046 — Opa1V291D_S2
- ⚠️ GEO 存放的是 **raw feature-barcode 矩阵**（barcodes 各含 110–145 万条，矩阵元数据标注 Cell Ranger 4），**需自行做空液滴过滤**；54,232 features（扩展注释）
- 探索发现：**WT 文库核数与质量明显低于两个突变样本**（knee plot 见 `output/01_qc/knee_plots.png`），比较时注意
- 原文每组 n=5 只小鼠 pooled；原始 FASTQ 在 SRA（PRJNA1237794），一般用不到
- **空间转录组（Visium HD FFPE，280 天）**：**GEO 未收录，原文也未给出 accession，待找**
- **补充材料已就位**（`ref/`）：`sciadv.adx7815_sm.pdf`（图 S1–S9、表 S1–S4，含表 S3 注释 marker——**无 microglia**）+ `ref/Data files/`（S2 全细胞类型 DEG 表、S3–S12 各细胞类型完整 ORA 富集结果、S1 空间代谢组定量）。预查要点见 `doc/第一轮分析计划_v1.md` §1.1。
## 计算环境（已实测）
- Windows 11 + Git Bash；Python **3.14.4**，**scanpy 1.12.4 已装**（scverse 生态为主）
- **未安装 R** —— 不要写 Seurat/R 脚本；富集分析用 Python 方案（gseapy/decoupler/pathway-enrichment skill）
- `uv`、`curl` 可用；网络访问 GEO/NCBI FTP 正常
- 分析主栈：scanpy + anndata；差异表达 pseudobulk 用 pydeseq2；富集/模块打分类 pathway-enrichment skill（gseapy/MSigDB，注意**小鼠基因集**）；视需要 scvi-tools/scvelo
## 可用 skill（与本项目相关）
- 单细胞：`scanpy`、`anndata`、`scvi-tools`、`scvelo`
- 富集：`pathway-enrichment`（ORA/GSEA/模块打分，含 MSigDB/GO/KEGG/Reactome/WikiPathways）
- 差异表达（pseudobulk）：`pydeseq2`
- 查库：`gget`、`bioservices`（ID 转换、通路查询）
- 文献/网络：`exa-search`（**搜索一律走 Exa，不用内置 WebSearch**）、`paper-lookup`
- 读文献：`pdf2md`（PDF → md）
- 画图：`scientific-visualization`（出版级）、`matplotlib`、`seaborn`
- 算力评估：`get-available-resources`（跑大任务前调用）
## 全局规则要点（来自用户全局 CLAUDE.md）
- 识图分流：Kimi 系模型直接 Read 图片；DeepSeek 系必须用 `node ~/.claude/skills/vision/vision.js "<图片绝对路径>" "用中文描述"`。
- 网络搜索一律用 `exa-search` skill，禁用内置 WebSearch。
- 回复与文档一律用简体中文，技术术语保留英文原文。
## 报告规则
- `doc/` 下的分析报告必须以 markdown 图片语法插入脚本生成的 figure，使用**绝对路径**（正斜杠），例如：
`![knee plot](D:/个人文档/PROJECTS/RGC-ADOA/output/01_qc/knee_plots.png)`
- 每张图紧跟一句话图注，说明结论而不是只描述内容；报告正文中引用过的图必须实际嵌入，不允许只写相对路径或文件名。
## 数据分析注意事项（再分析时务必记住）
1. **生物学重复陷阱**：WT 只有 1 个 pooled 样本，突变 2 个——细胞级 Wilcoxon 检验存在严重 pseudoreplication 风险。**核心结论必须以 pseudobulk（样本为单位聚合）+ 谨慎统计为准**，细胞级检验只作探索。
2. **基因型与批次混杂**：3 个样本 = 3 个独立建库，WT/mutant 之间差异与批次效应不可完全分离，解释时留有余地。
3. **microglia 稀少**：全视网膜约 1.9 万核中小胶质细胞占比很低（通常 <1%），炎症分析可能面临细胞数不足；需要时放宽聚类分辨率专门捞免疫细胞（P2ry12/Aif1/C1qa 等 marker）。
4. **snRNA-seq 特性**：核测序中 mito% 指标不适用常规阈值；炎症/即刻早期基因表达与 scRNA-seq 有偏差。
5. **原文的阴性结果**：ISR（p-eIF2α/ATF4）免疫荧光未见激活——我们做炎症通路时注意与该结果对照叙述。
README.md
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# RGC-ADOA — OPA1/ADOA 单细胞再分析
OPA1 突变（ADOA）视网膜神经节细胞（RGC）单细胞 RNA-seq 再分析，复现并检验文献（Kang 2026, *Sci. Adv.*）的核心结论。
## 目录结构
| 目录 | 内容 | 是否进 git |
|------|------|-----------|
| `script/` | 分析脚本（编号即执行顺序） | ✅ |
| `doc/` | 文献精读、分析计划、分析报告 | ✅ |
| `ref/` | 参考文献 PDF + 补充表格（脚本 08/10/11/13 直接读取） | ✅ |
| `output/` | 各步骤结果图/表（CSV + PNG） | ✅ |
| `data/` | 原始与中间 `.h5ad`、GSE292269 原始数据（~26G） | ❌ 忽略 |
## 多设备接续分析
`data/` 目录体积大（~26G），**不进 git**，换设备继续分析时需单独传输：
```bash
# 从本机推送到新设备（或直接 scp/rsync 到服务器）
rsync -avP data/ <user>@<host>:/path/to/RGC-ADOA/data/
```
拿到 `data/` 后，其余一切（脚本、参考表格、历史结果）均随 `git clone`/`git pull` 自动同步：
```bash
git clone https://gitea.rainotes.top/rain/RGC-ADOA.git
cd RGC-ADOA
# 再把 data/ 放到此处
```
中间数据链路：`01_filtered` → `02_clustered` → `03_annotated` → `04_scored` → `05_rgc2sig` → `06_inflammation`。
doc/文献精读_Kang2026_ADOA_OPA1.md
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# 文献精读：Kang et al. 2026 — OPA1 突变致 ADOA 中能量代谢紊乱与 RGC 退变
> **Disrupted energy metabolism is associated with retinal ganglion cell degeneration in autosomal dominant optic atrophy**
> Kang EY-C, Tseng Y-J, …, Wang N-K（通讯）et al. *Science Advances*, 2026. DOI: 10.1126/sciadv.adx7815
> 原文：`ref/sciadv.adx7815.pdf`；数据：GEO GSE292269 / BioProject PRJNA1237794
> 阅读日期：2026-09-17；本文档聚焦**干实验流程、主要结论、创新性与不足**
---
## 1. 一句话概括
构建首个携带患者来源**错义突变**的 ADOA 敲入小鼠（Opa1^V291D/+），用空间代谢组（MALDI-MSI）+ snRNA-seq + Visium HD 空间转录组证明：OPA1 缺陷导致 Complex I 功能障碍 → NAD⁺/NADH 失衡 → ATP 危机 + 氧化应激；感光细胞可上调糖酵解代偿，而 **RGC 丧失这种代谢可塑性**（ETC、糖酵解、线粒体自噬基因协同下调）→ 选择性退变；RGC 特异性过表达 MitoLbNOX（提高线粒体 NAD⁺/NADH）可 rescue。
## 2. 研究设计与湿实验要点（背景）
- **模型**：患者携带 OPA1 c.1037T>A (p.V346D)；小鼠敲入对应位点 V291D（GTPase 结构域），杂合；纯合胚胎致死。C57BL/6J 回交 5 代，排除 rd8。
- **机制**：突变 OPA1 蛋白稳定性下降（泛素-蛋白酶体降解增强，MG132 仅部分回复），l-OPA1/s-OPA1 均降、s-OPA1 降更多 → 支持\*\*单倍剂量不足（haploinsufficiency）\*\*机制。
- **表型**：PERG/PhNR/STR（RGC 特异电生理）异常而闪光 ERG（感光细胞）正常；RNFL 变薄、RGC 计数下降；视神经线粒体球形化（融合受损）、嵴丢失、空泡化、髓鞘松散；mtDNA 拷贝数升高但完整性正常。
- **功能**：RIFS/HyFS（冻存组织 Seahorse 测复合体活性）示 Complex I、IV 活性下降；GSH/GSSG↓、SOD↓、4-HNE↑（GCL 尤著）；NAD⁺/NADH↓、ATP↓、乳酸↑。
- **治疗验证**：Vglut2-Cre × Rosa26-LSL-MitoLbNOX → RGC 线粒体特异性过表达 → PERG、RGC 存活改善；PDHE1/IDH3（TCA）回升、4-HNE 下降、NRF2 恢复；**ISR（p-eIF2α/ATF4）未见激活**。
## 3. 干实验流程（重点）
### 3.1 snRNA-seq
| 环节 | 做法 |
| ----- | ------------------------------------------------------------------------------------------------------------------------------- |
| 样本 | 360 天小鼠全视网膜，冻存组织抽核（Miltenyi Nuclei Extraction Buffer），**每组 n=5 只 pooled**；GEO 实际为 3 个样本：WT×1（GSM8855044）、V291D×2（GSM8855045/46） |
| 建库测序 | 10x Chromium Single Cell 3′，Illumina NovaSeq 6000（GPL24247） |
| 定量 | Cell Ranger **v8.0** 默认参数 → filtered_feature_bc_matrix；`cellranger aggr` 合并 |
| 降维聚类 | Rosalind 平台（10x graph-based clustering） |
| 下游 | R v4.2 + **Seurat v5.0**；细胞注释 **SCtype v1.0** + 自建 marker 表（table S3） |
| 结果规模 | 19,315 核 → 10 个 cluster，对应 9 类视网膜细胞 + 1 个 "other"；**RGC 分 RGC-1 / RGC-2 两群**（均表达 Rbpms/Slc17a6/Thy1，Pou4f 无差异，亚群身份未深究） |
| 差异/富集 | clusterProfiler v4.10.1 + **REACTOME & WikiPathways**（ORA 式富集）；log2 归一化；BH 校正，**q<0.1** |
| 可视化 | Seurat heatmap / dot plot |
**关键发现**：ETC（WikiPathways, adj.P<0.0001）、Complex I biogenesis（REACTOME, adj.P<0.0001）、糖酵解（REACTOME, adj.P=0.0081）基因在突变鼠 **RGC-2 群**显著下调； rods/cones 等其他细胞无此变化（dot plot 展示）。线粒体自噬/自噬通路也在 RGC-2 下调（fig. S5A）。
### 3.2 空间转录组（验证性）
- **10x Visium HD FFPE**，280 天小鼠眼球，10 μm 切片，H&E 定位。
- Space Ranger 比对定量；**QuPath 手工圈定 GCL** 区域；Seurat v5.0 做 spot 级聚类。
- 结果：GCL 内细胞 ETC（adj.P=0.0079, q=0.1215）与糖酵解（adj.P<0.0001）基因下调，与 snRNA-seq 一致；自噬基因下调、线粒体自噬有下降趋势但不显著。
- ⚠️ 该数据**未存入 GEO**，公开渠道暂无。
### 3.3 空间代谢组（MALDI-MSI）
- 200 天眼杯，CMC 包埋，10 μm 冰冻切片，ITO 载片。
- Bruker Autoflex（初筛）+ **timsTOF fleX MALDI-2**（高分辨）双平台；DHB（正离子）/NEDC（负离子）双基质；20–25 μm 空间分辨率。
- SCiLS Lab 处理，RMS 归一化；H&E 同片染色辅助 ROI 手动分层（内层/外层）。
- 结果：**内层视网膜 ATP↓、AMP↑**（能量危机）；**外层 G6P、丙酮酸↑**（感光细胞糖酵解代偿）。
### 3.4 统计
GraphPad Prism v10.4 / SPSS v21 / R v4.2；两组独立 t 检验，三组 one-way ANOVA + Tukey，剂量-反应用线性回归交互项；多重检验 BH 校正；P<0.05、q<0.1。
## 4. 主要结论
1. V291D 错义突变通过降低 OPA1 蛋白稳定性造成**单倍剂量不足**样效应。
2. OPA1 缺陷 → 嵴结构破坏 → Complex I/IV 活性下降 → NAD⁺/NADH↓、ATP↓、氧化应激↑（全视网膜范围）。
3. **RGC 选择性易感的机制是"代谢不可塑性"**：感光细胞靠上调糖酵解代偿，RGC 反而协同下调 ETC + 糖酵解 + 线粒体自噬/自噬基因，陷入"能量危机–氧化应激"恶性循环。
4. 提高线粒体 NAD⁺/NADH 比值（MitoLbNOX）可同时改善能量代谢与氧化应激，挽救 RGC——提出 ADOA 治疗新策略。
## 5. 创新性
- **首个 ADOA 错义突变敲入小鼠**（此前 3 个模型均为截短突变），且直接对应真实患者位点。
- **多模态空间组学交叉验证**同一结论：MALDI-MSI（代谢物）＋免疫荧光（代谢酶）＋snRNA-seq（转录）＋Visium HD（空间转录），层层落到 GCL。
- 用"**代谢可塑性差异**"解释了线粒体病中经典的组织选择性难题（感光细胞线粒体密度最高却不受害，RGC 反而退变）。
- 治疗概念验证新颖：MitoLbNOX 直接氧化 NADH→NAD⁺，绕过 Complex I，有别于补充 NAD⁺ 前体（烟酰胺）的传统路线。
## 6. 不足（尤其从再分析角度）
### 6.1 实验/统计设计
1. **pooled 设计导致生物学重复缺失**：每组 5 只小鼠混成 1–2 个文库，WT 甚至只有 1 个样本。单细胞层面的差异检验以"核"为单位，属 pseudoreplication，I 类错误膨胀；基因型与文库批次完全混杂，无法分离。
2. **"RGC 特异"的论证偏弱**：所谓其他细胞"无变化"主要靠 dot plot 目测，未做正式的基因型×细胞类型交互检验（如 pseudobulk + DESeq2 的 interaction term，或 dreamlet/miloDE 类方法）。
3. **RGC-1/RGC-2 两亚群来历不明**：未与已知 RGC 亚型（如 Rbpms+ vs Opn4+ ipRGC 等 40+ 亚型）对标，核心发现挂在 RGC-2 上却未解释该群是什么。
4. 关键分子机制（V291D 如何 destabilize OPA1、单倍剂量不足 vs 显性负效应）未做实锤（作者自承需 GTPase pull-down）。
5. q<0.1 的富集阈值偏宽；部分关键结果仅"趋势"（ATP 水解、超复合体 BN-PAGE、空转 ETC q=0.1215）。
### 6.2 生信方法
6. **未报告任何 QC 细节**：无基因/UMI 阈值、双联体处理、批次校正方法说明；aggr 仅做测序深度归一化。
7. 富集分析为传统 ORA（clusterProfiler + DEG 列表），未用更适合单细胞的**模块打分/GSEA**（AUCell、UCell、decoupler）或通路活性推断；数据库仅 REACTOME/WikiPathways，未覆盖 Hallmark/KEGG/GO-BP。
8. Visium HD 分析停留在"QuPath 圈 GCL + spot 聚类"，未做去卷积（cell2location/ RCTD）或配受体分析（CellChat/NicheNet），高分辨率优势没发挥。
9. 时间点单一（snRNA 360d、空转 280d、MALDI 200d），无病程动态（退变前的早期事件看不到）。
10. **数据公开不完整**：snRNA-seq 有 GEO（GSE292269），Visium HD 空转数据未公开；补充 Excel 需从期刊页获取。
### 6.3 生物学盲区 = 我们的机会
11. **完全没做炎症/免疫分析**：Müller 胶质细胞反应性胶质化、小胶质细胞激活、IFN/JAK-STAT、NF-κB、补体等通路通篇未提。线粒体损伤 → mtDNA/ROS → cGAS-STING/NLRP3 → Ⅰ型干扰素这条链在该模型里完全未检验——正是本项目假设的切入点。
12. ISR 阴性（p-eIF2α/ATF4）只做了免疫荧光，转录层面是否有代偿性/亚阈值激活未查。
13. 胶质细胞仅作为"背景细胞"出现在 dot plot 里，未单独分析。
## 7. 对本项目的直接启示
- 复现基线：先用 scanpy 重建其 10 cluster 结构，对标 marker（table S3 需下载补充材料），确认 RGC-1/RGC-2 可复现，并尝试 RGC 亚型细分（参照 Tran et al. 2019 / Rheaume et al. 2018 的 RGC atlas）。
- 差异分析主用 **pseudobulk（样本为单位）**，细胞级结果仅作探索展示；WT 单样本的局限要在报告中明示。
- 炎症分析路线：Hallmark（IFN-α/IFN-γ response、TNFA/NF-κB、complement、IL6-JAK-STAT3）+ Reactome（interferon signaling、cytokine signaling）模块打分（decoupler/AUCell）→ 分细胞类型比较；重点看 Müller（Rlbp1/Glut1/Apoe、反应性 marker Gfap/Vim/Serpina3n）与 microglia（P2ry12/Tmem119/Aif1/C1qa；激活态 Apoe/Spp1/Lpl）。
- microglia 预计细胞数少，提前设计低分辨率重聚类/阈值放宽方案；若数量不足以做 DE，则用模块打分 + 比例变化替代。
- 原文图 7C/D 的 ETC/糖酵解基因列表可作为我们分析的阳性对照（应能复现 RGC-2 下调）。
---
*转换文本见ref/*
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# 第一轮分析报告（P0：QC → 注释 → 组成 → 阳性对照复现）
> 日期：2026-09-17 ｜ 数据：GSE292269（3 样本，360 天小鼠视网膜 snRNA-seq）
> 管线：`script/02_qc_filter.py` → `03_integrate_cluster.py` → `04_annotate.py` → `05_composition.py` → `08_replicate_metabolism.py`
> 决策依据：`doc/第一轮分析计划_v1.md` §6
---
## 1. 数据质控
GEO 存放的是 **raw 矩阵**（含百万级空 barcode），我们自行完成了空液滴过滤。
| 样本 | 基因型 | 过滤后核数 | 中位 UMI | 中位基因数 | mt% 中位 |
| ------------ | ------- | --------- | ------ | ----- | ------ |
| WT | Opa1+/+ | **2,944** | 868 | 529 | 1.8% |
| Opa1V291D_S1 | V291D/+ | 10,744 | 1,267 | 881 | 0.7% |
| Opa1V291D_S2 | V291D/+ | 10,582 | 1,412 | 972 | 0.6% |
- 合计 **24,270 核 × 24,007 基因**（阈值：UMI≥500、基因≥300、mt%<10%；原文为 19,315 核，量级一致）。
- scrublet 仅检出 0.0–0.1% 双联体（自动阈值在低复杂度核数据上偏保守，作为已知局限记录）。
- ⚠️ **WT 文库显著小于突变样本**（核数 1/3.6，UMI 中位数低 \~35%），且 Rho 在所有 cluster 中检出率 >94%——**ambient RNA 污染重**；后续若做精确定量需 cellbender 校正复核（决策记录 #2 第二阶段）。
![1.00](D:/个人文档/PROJECTS/RGC-ADOA/output/01_qc/knee_plots.png)
图 1：三个样本的 knee plot——WT 拐点靠前（\~2–3k 核），突变样本靠后（\~6–10k 核），直观呈现文库规模差异。
![1.00](D:/个人文档/PROJECTS/RGC-ADOA/output/02_qc/qc_violins_postfilter.png)
图 2：过滤后 QC 指标小提琴图——**三组样本的 UMI/基因数均呈双峰**，低峰主要是杆细胞核（其转录本量天然小：WT 中 Rod 中位基因数 407 vs 非 Rod 2,254，突变样本同样成立），因此双峰主要反映细胞类型组成而非单纯质量问题；WT 的真正特异之处是 **mt% 拖尾明显更厚**（低质核占比更高）。
## 2. 聚类与注释
Harmony（按 sample）整合后 WT/突变细胞在 UMAP 上充分混合（下图），未见批次驱动的孤立群。leiden_0.8（52 clusters）+ **补充 PDF 表 S3**（Cell markers for cell type annotation）marker 打分注释：
![1.00](D:/个人文档/PROJECTS/RGC-ADOA/output/03_cluster/umap_genotype.png)
图 3：UMAP 按基因型着色——WT（橙）散在各 cluster 内部，说明聚类结构由细胞类型而非批次主导。
![UMAP vs tSNE 2x2](D:/个人文档/PROJECTS/RGC-ADOA/output/03_cluster/umap_tsne_2x2.png)
图 3b：UMAP 与 tSNE 降维可视化对照（均以 Harmony 校正 PCA 为输入，tSNE perplexity=30）。两种嵌入的分群结构一致（左列按细胞类型着色，14 类清晰分离），印证聚类结果稳健、不依赖降维算法；差异仅在展示形态——tSNE 把高丰度的 Rod 大群压缩到中心（其固有特性，中心大团块为 Rod），UMAP 更舒展、簇间关系更清楚。右列按基因型着色，WT（蓝，n=2944）散落在各簇内部、无独立批次群，进一步确认聚类由细胞类型而非文库批次主导。⚠️ 二者均为 2D 可视化投影，**不参与聚类（Leiden 在 PCA 空间）也不参与任何下游定量**，因此原文 tSNE 与我们 UMAP 的选择差异不影响结论。
![1.00](D:/个人文档/PROJECTS/RGC-ADOA/output/04_annotation/marker_dotplot.png)
图 4：注释证据 dotplot——RGC（cluster 36/38/46：Rbpms/Slc17a6/Thy1）、microglia（cluster 31：Aif1/C1qa/C1qb/Tmem119/Hexb）等marker 信号清晰；注意 Rho 列几乎全红，是 ambient 污染的直接证据。
![marker dotplot full S3](D:/个人文档/PROJECTS/RGC-ADOA/output/04_annotation/marker_dotplot_fullS3.png)
图 4b：完整表 S3 marker（32 基因全部在我们数据中检出，无一缺失）+ 本项目补充 marker（带 + / * 后缀的分组，覆盖 S3 未包含的 microglia/astrocyte/内皮/少突）。可见部分官方 marker（Cnga1、Cngb1、Rp1、Ush2a、Cngb3、Slc6a5、Cabp5、Lhx1）在本数据中检出率低、点稀——这是核测序深度所致，不影响主注释（打分用多基因联合）；作者的使用方式是把整张 S3 喂给 SCtype 自动注释工具做 cluster 级富集打分，与我们的手工打分殊途同归，且我们对 microglia 等类型的覆盖比 S3 更全。
| 细胞类型 | V291D | WT | 备注 |
| ---------------------- | --------- | ------- | --------------------------------------------------------------------------------- |
| Rod | 7,866 | 1,773 | |
| Muller | 2,389 | 183 | |
| Amacrine | 2,360 | 189 | |
| **RGC** | **942** | **69** | cluster 36/38/46，Rbpms 检出 95.5% |
| Bipolar | 1,426 | 106 | |
| Horizontal | \~1,135 | \~80 | 部分 cluster 低信度 |
| Cone | 1,208 | 99 | |
| Oligodendrocyte | 978 | 58 | 疑视神经混杂 |
| Pericyte / Endothelial | 898 / 603 | 93 / 55 | |
| **Microglia** | **132** | **8** | cluster 31，Aif1/C1qa/C1qb/Tmem119/Hexb 全阳性——**作者未注释该类** |
| Astrocyte | 90 | 15 | |
| Uveal_Melanocyte | 669 | 53 | 对应作者的 "Uveal" |
| LowConf（剔除） | 1,911 | \~240 | margin<0.5 的 7 个 cluster，其中 cluster 2（601 核）经核查为低复杂度混合群（Rbpms 检出仅 13.6%），**正确排除** |
- **microglia 捕捞结果**：独立 cluster 31 共 140 核（多 marker 共表达验证 69/140 通过 ≥3 marker 门控）；**WT 仅 8 核**，按决策记录 #6，microglia 只能做模块打分 + 检出率的描述性分析，不做 DE。
## 3. 细胞组成（描述性，n=1 vs n=2）
- ⚠️ **组成分析受文库质量严重混杂**：WT 中 Rod 占 66.9%，突变样本仅 35–49%（低复杂度 WT 文库优先回收了高表达少数基因的杆细胞核），导致其他细胞类型比例在突变中"被动膨胀"。**RGC 占比 2.6%→4.5–5.5% 的"升高"是假象**。
- 以**非杆细胞为底数**重新计算：RGC 占比 WT 7.9% vs 突变 8.5–8.8%，基本稳定——360 天时残存 RGC 池的转录组构成未崩。
- Microglia：0.30% (WT) vs 0.60–0.81%（突变），非杆底数校正后 0.91% vs 1.17–1.25%——**突变中微升**，方向符合"胶质增生"预期，但 WT 基数太小（8 核），仅作参考。
![1.00](D:/个人文档/PROJECTS/RGC-ADOA/output/05_composition/composition_stacked_bar.png)
图 5：各样本细胞组成堆叠图——WT 被 Rod（底部大色块）主导，是文库质量差异的直接体现。
![1.00](D:/个人文档/PROJECTS/RGC-ADOA/output/05_composition/composition_change_dotplot.png)
图 6：WT vs 突变各细胞类型比例变化——连线斜率主要由 Rod 占比差异驱动，解读需以非杆底数校正为准。
## 4. 阳性对照：原文能量代谢结论复现
方法：作者**数据文件 S10**（snRNAseq-Pathway_RGC2.xlsx）leading-edge 基因集（ETC 33 基因、CI biogenesis 26、糖酵解 14）+ Rpl/Rps 核糖体集 + 线粒体自噬集，模块打分比较；RGC 按 WT ETC 分 top30% 阈值分为 highETC（≈原文 RGC-2，WT 仅 21 核）与 lowETC（≈RGC-1）。
| 模块 | RGC_highETC（≈RGC-2） | RGC_lowETC（≈RGC-1） | Müller |
| ------------- | ------------------- | ------------------ | ------ |
| ETC | 持平 | ↑ | **↑** |
| CI biogenesis | 持平 | ↑ | **↑** |
| **糖酵解** | **↓（复现 ✓）** | ↑ | ↑ |
| 核糖体蛋白 | 持平 | ↑ | **↑** |
| **线粒体自噬/自噬** | **↓（复现 ✓）** | 持平/↑ | 持平 |
**基因级一致性**：作者 RGC-2 显著下调基因（数据文件 S2，p<0.001, log2FC<−0.5，n=1,125）与我们 RGC_highETC 的近似 log2FC **方向一致率 85.5%**——强复现。
![1.00](D:/个人文档/PROJECTS/RGC-ADOA/output/08_replication/module_score_medians.png)
图 7：各模块分每样本中位值（黑=WT，红/橙=两突变样本）——高能 RGC 中糖酵解与线粒体自噬下调（复现原文），Müller 各模块全面上调（意外发现）。
明细：`output/08_replication/module_score_comparison.csv`、`genelevel_concordance_RGChighETC.csv`。
### 复现结论
> ⚠️ **P1 纠正（重要）**：本节"ETC/CI 未复现"的判断**已被 P1 推翻**，是"按 ETC 高低分位定义 RGC-2"的循环定义 artifact。P1 改用作者 DEG 签名定义 RGC-2 后，RGC2-like 的 **ETC −0.30、CI biogenesis −0.28、糖酵解 −0.72、线粒体自噬 −0.03 全部下调**，pseudobulk 基因级 ETC 中位 log2FC 更达 **−0.70（82% 下调）**——即作者的代谢结论（ETC+CI+糖酵解+自噬协同下调）**完整复现**。详见 `doc/第一轮分析报告_P1.md` §2。
1. **部分复现（P0 阶段性）**：糖酵解与线粒体自噬在高能 RGC 中下降（原文两个标志性结论），基因级方向一致率高 → 管线可信；ETC/CI 在 ETC 分位法下"未复现"系循环定义所致，见上方纠正。
3. **意外发现——Müller 全面上调**：突变 Müller 的 ETC、CI、核糖体、糖酵解模块**全部升高**，与 RGC 的抑制形成镜像，符合"反应性胶质细胞代谢重编程"。结合作者 DEG 表中 Müller 头号上调基因 Apoe，**Müller 反应性激活是 P1 阶段值得深挖的方向**。
4. **RGC_lowETC 各模块普遍上调**：低能 RGC 可能处于代偿状态，与"RGC-2 选择性受累"叙事兼容。
## 5. 对研究假设的初步回应
- **H1（代谢直接受损）**：✅ 支持，且呈现细胞类型分化——受损集中在高能 RGC（糖酵解/自噬下调），Müller 反而代谢上调。
- **H2（炎症轴）**：⏸️ 本轮尚无直接证据。关键提醒：作者补充表中 RGC-2 的"IFN/NF-κB 富集"经我们核查，**驱动基因实为微管/翻译/蛋白酶体等通用看家机器，非真 ISG**（真 ISG 如 Ifit2/Stat1/Ifnar 在其数据中是下调的）——P1 做炎症分析时绝不能照搬通路名，必须落到具体 ISG 基因。
- **机制链条的雏形**：OPA1 缺陷 → 高能 RGC 糖酵解/自噬代偿失败（H1 直接打击）→ Müller/microglia 反应性改变（H2 待证，microglia 比例微升 + Müller 代谢激活为间接线索）。
## 6. 已知局限
1. WT 单样本、基因型与批次混杂——所有比较为方向性证据，无正式统计推断。
2. WT 文库小且质量低，WT RGC 仅 69 核、WT microglia 仅 8 核——稀有类型比较功效极低。
3. Ambient RNA 污染明显（Rho 普遍检出），低表达基因的检出率比较需慎重。
4. 模块打分（scanpy score_genes）与作者的 ORA 框架不完全等价，ETC 未复现部分是方法学差异还是真阴性待 P1 用 pseudobulk 复核。
## 7. 下一步（P1 建议）
1. pseudobulk DE（方向一致性策略：S1 vs WT ∩ S2 vs WT，S1 vs S2 阴性校准），重点 RGC_highETC 与 Müller。
2. Müller 反应性胶质化专项：Gfap/Vim/Serpina3n/Apoe/Lcn2 模块 + 代谢激活表型确认。
3. IFN–JAK–STAT 落到具体基因（ISG 清单逐基因 + 检出率），鉴别全局抑制 vs 通路特异。
4. microglia：模块打分（DAM/稳态/IFN）+ 检出率比较，不做 DE。
5. 如 ambient 影响关键结论，补 cellbender 复核（决策记录 #2 第二阶段）。
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# 第一轮分析报告 — P1（下采样裁决 → DE → 炎症通路）
> 日期：2026-09-17 ｜ 数据：GSE292269（3 样本，360 天小鼠视网膜 snRNA-seq）
> 管线：`script/09_muller_downsample.py` → `10_rgc2_signature.py` → `11_pseudobulk_de.py` → `12_inflammation_modules.py` → `13_isg_check.py` → `14_global_shift.py` → `16_gsea_prerank.py` → `17_ifn_heatmap_ma.py`
> 决策依据：`doc/第一轮分析计划_v1.md` §6 第二/三轮裁决；本文是 `doc/第一轮分析报告_P0.md`（P0）的延续。
---
## 0. 一个必须先讲的方法学发现：WT 文库"质量差"是误读
P0 曾按"全数据中位 UMI"（WT 868 vs 突变 1266/1412）判断 WT 文库质量差。P1 分细胞类型核查后，真相相反：
| 细胞类型 | WT 中位 UMI | S1 中位 UMI | WT/S1 |
|---|---|---|---|
| RGC | 12,167 | 4,497 | **2.71** |
| Horizontal | 8,756 | 3,376 | 2.59 |
| Amacrine | 7,457 | 2,920 | 2.55 |
| Muller | 3,633 | 1,802 | 2.02 |
| **Rod** | 669 | 573 | **1.17** |
- **WT 的每个非杆细胞核深度都是突变核的 ~2 倍**；唯一例外是杆细胞（接近 1.0）。
- 全数据中位 UMI 被**杆细胞占比**主导：WT 杆核占 60.2%，突变仅 43%/31%。杆核天然低 UMI（~600），把 WT 中位数拉低，造成"WT 质量差"的错觉。
- 真实情况是：WT 文库**组成极度偏向杆细胞**，但**非杆核的测序深度反而更高**（或突变文库非杆核被稀释）。
**对下游的直接影响**：任何**检出率（detection rate）比较**都被这个 2 倍深度差污染——WT 深度高 → 检出更多基因 → 突变"检出率下降"可能是假象。P1 因此只信两类指标：① `score_genes` 模块分（对照基因校正，深度稳健）；② median-of-ratios 归一化后的 pseudobulk LFC。检出率仅在**方向逆深度**（突变低深度反而检出更高）时才算证据。
---
## 1. P1-① Müller"全面上调"裁决：只有 ETC 是真信号
方法：突变 Müller 随机抽至 183 核（=WT 核数）后重算 5 个代谢模块分，5 个随机种子重复；RGC_highETC 的糖酵解下调作阳性对照（验证方法功效）。
| 模块 | 全量 delta（S1/S2） | 下采样后（S1/S2） | 裁决 |
|---|---|---|---|
| ETC | +0.127 / +0.118 | +0.100 / +0.095 | **保留（真）** |
| CI biogenesis | +0.093 / +0.072 | −0.006 / −0.002 | 归零 |
| 糖酵解 | +0.132 / +0.113 | −0.019 / −0.024 | 转负 |
| 核糖体 | +0.075 / +0.045 | −0.022 / −0.022 | 转负 |
| 线粒体自噬 | −0.005 / +0.001 | +0.045 / +0.033 | 不稳定 |
阳性对照：RGC_highETC 糖酵解 −0.138 → **−0.159**（下采样后更强）→ 方法功效足够，裁决可信。
![Müller 下采样裁决](D:/个人文档/PROJECTS/RGC-ADOA/output/09_downsample/downsample_adjudication.png)
图 8：Müller 各代谢模块全量（斜线半透明）vs 下采样后（实色，5 种子均值±SD）的 Δ 模块分——只有 ETC 上调在下采样后保留，其余模块的"上调"随核数缩水归零；右侧 RGC_highETC 糖酵解下调下采样后更强，证明该方法能检出真信号。
**裁决结论**：Müller 的"全面上调"大部分是核数少 + 小效应的假象，**唯一稳健的是 ETC 上调**（+0.10 保留）。这与"反应性胶质化代谢重编程"的叙事需要收窄——Müller 的激活是**炎症/胶质化程序**（见 §4），不是线粒体呼吸链的全面上调。⚠️ 补充：裁决的原假设"突变深度高→假象"方向是反的（WT Müller 深度其实更高，见 §0），因此下采样实际校正的是**核数**而非深度——这反而强化了结论：ETC 上调在 WT 深度优势之下仍存在，是真信号甚至可能被低估。
---
## 2. P1-② RGC-2 重定义：作者签名打分法（弃用 ETC 分位循环定义）
签名 = 作者 S2 表中 RGC-2 在 WT 中检出率 ≥0.5 且比 RGC-1 高 ≥0.25 的 top 100 基因（只用 WT 检出率差，**不用疾病 DE 信息**，避免"用疾病信号定义疾病组"的循环）。分类器 = score(RGC2签名) − score(RGC1签名)。
- 结果：RGC2-like = 141 突变 + **7 WT**（WT 太少，标记低功效）；RGC1-like = 801 突变 + 62 WT。
- **代谢模块分（签名法）在 RGC2-like 全部下调，完整复现作者代谢结论**：
| 模块 | RGC2-like ΔS1/ΔS2 | RGC1-like ΔS1/ΔS2 |
|---|---|---|
| **ETC** | **−0.299 / −0.257** | +0.123 / +0.107 |
| **CI biogenesis** | **−0.283 / −0.252** | +0.089 / +0.081 |
| **糖酵解** | **−0.721 / −0.650** | +0.118 / +0.153 |
| 线粒体自噬 | −0.034 / −0.035 | +0.024 / +0.034 |
（对照：P0 的 ETC 分位组糖酵解仅 −0.14/−0.10，签名法放大约 5 倍；ETC/CI 也从 P0 的"持平"转为下调——P0 的"ETC 未复现"是循环定义 artifact，见 P0 报告 §4 纠正。）
- 基因级方向一致率（作者 RGC-2 显著下调基因 vs 我们 RGC2-like）：**88.1%**（P0 为 85.5%）；pseudobulk 基因级 ETC 中位 log2FC 达 −0.70（82% 下调）。
![RGC-2 签名重定义](D:/个人文档/PROJECTS/RGC-ADOA/output/10_rgc2_signature/rgc2_signature_redefinition.png)
图 9：左——签名差分布（黑 WT/红突变）；中——签名分组与 P0 ETC 分组的重叠；右——新分组下代谢模块 Δ 分：RGC2-like 的 **ETC/CI/糖酵解/自噬全部下调**（红线），RGC1-like 反而上调（粉线），与"RGC-2 选择性受累"完全对齐。
**结论**：签名法成功分离出真正的 RGC-2，ETC、CI biogenesis、糖酵解、线粒体自噬**全部下调**——作者的能量代谢结论（含原文 adj.P<10⁻⁵ 的 ETC 下调）**完整复现**，效应量比 ETC 分位法放大约 5 倍，且不依赖"先按 ETC 分位再验证 ETC"的循环。固有局限：RGC-2 的身份基因（微管/ETC/核糖体）恰是疾病中被下调的基因，突变细胞里"保留身份"的个体偏向较健康者，会**低估**效应量；WT 仅 7 核仍是硬伤（已按第三轮裁决接受签名法为最保守下界）。
---
## 3. P1-③ pseudobulk DE：方向一致性 + 阴性校准
方法：每 样本×细胞组 counts 求和 → **median-of-ratios（DESeq2 式 size factor）归一化** → LFC_S1、LFC_S2、LFC_null(S2−S1)；一致基因需 两样本同向、|LFC|≥0.5、阴性校准 |LFC_null| 小于两次生物 LFC、零检出守卫（防深度不对称假 UP）。
**与作者 DEG 表交叉验证**（Spearman ρ 与作者显著基因方向一致率）：
| 组 | Spearman ρ | 作者显著基因方向一致率 |
|---|---|---|
| Cone | 0.948 | 0.994 |
| RGC1-like | 0.829 | 0.988 |
| Pericyte | 0.813 | 0.988 |
| Muller | 0.773 | 0.911 |
| Uveal | 0.734 | 0.958 |
| RGC2-like | 0.671 | 0.954 |
![与作者 log2FC 对照散点](D:/个人文档/PROJECTS/RGC-ADOA/output/11_pseudobulk_de/author_comparison_scatter.png)
图 10：我们的 pseudobulk LFC vs 作者细胞级 avg_log2FC（红=我们判定一致的基因）——Cone/RGC1 高度一致（管线可信）；Muller 的散点明显**右偏**（大量突变上调基因在作者侧也被上调，只是作者细胞级 DE 把它们判为"不显著"）。
**两个关键发现**：
1. **Muller 的上调基因是真实且被作者漏掉的**：我们 1690 个一致 UP 基因中，作者侧 91.7% 也是上调（均值 +0.92），只是作者细胞级 DE（噪声大）把它们多数压到阈值之下。Muller 头号上调 Apoe 我们 LFC +1.35/+0.93，作者 +1.33——完全一致。说明 P0 观察到的"Müller 反应性激活"有 pseudobulk 支撑。
2. **我们各细胞类型 UP/DOWN 比全部 >1**（1.2–3.4，突变上调占优），而作者 RGC-2 的比是 0.00（几乎全下调，1171 下调 vs 2 上调）。这个反差正是 §5 全局转录的关键线索。
![MA 图](D:/个人文档/PROJECTS/RGC-ADOA/output/17_visualization/ma_plot.png)
图 10b：Müller（左）与 RGC2-like（右）的 MA 图（红=一致上调，蓝=一致下调）——代谢基因呈现清晰的**镜像**：Cox7b/Gapdh/Ndufs3/Uqcrc2（ETC/糖酵解）在 Müller 上调（红）而 RGC2-like 下调（蓝）；Apoe/Clu（胶质化 marker）两图均上调。这是 H1"RGC 代谢下调 vs Müller 代谢上调"的直接可视化。
![细胞级 vs pseudobulk](D:/个人文档/PROJECTS/RGC-ADOA/output/17_visualization/celllevel_vs_pseudobulk.png)
图 10c：细胞级 Wilcoxon（灰，探索层）与 pseudobulk（青）的 UP/DOWN 方向偏斜对照——Müller 细胞级给出 **9 UP / 48 DOWN（几乎全下调）**，而 pseudobulk 给出 **1690 UP / 769 DOWN**。细胞级的"全下调偏斜"正是作者（Seurat 细胞级 DE）得到"1171↓ vs 2↑"的同源 artifact；我们改用 pseudobulk + median-of-ratios 后才还原出"突变上调占优"的结构化改变。
---
## 4. P1-④ 炎症模块打分（胶质为主）
12 个模块：IFN-α/γ、IL6-JAK-STAT3、TNFα-NFκB、补体、cGAS-STING、NLRP3、UPRmt、ISR、Müller 反应性胶质化、microglia DAM/稳态。重点看 Muller / Microglia / RGC 两组。
![炎症模块热图](D:/个人文档/PROJECTS/RGC-ADOA/output/12_inflammation/inflammation_heatmap.png)
图 11：各炎症模块 Δ 分（突变−WT，S1/S2 均值）热图——最亮的红色块是 **Müller 反应性胶质化**（+0.11）与 **microglia DAM**（+0.20）；IFN/NLRP3/cGAS 在 Müller 中无上调甚至下降。
| 组 | 反应性胶质化 | 补体 | IFN-α | NLRP3 | cGAS | DAM | 稳态 |
|---|---|---|---|---|---|---|---|
| Muller | **+0.147/+0.069** | +0.039/+0.025 | −0.001/−0.006 | −0.045/−0.047 | −0.022/−0.020 | +0.125/+0.063 | −0.012/−0.012 |
| Microglia* | +0.213/+0.267 | +0.000/−0.068 | +0.002/+0.009 | −0.132/−0.347 | −0.131/−0.125 | **+0.114/+0.279** | **−0.340/−0.812** |
（*Microglia WT 仅 8 核，深度差 2 倍，仅方向性参考。）
**核心结论：Müller 反应性胶质化是 P1 最稳健的炎症信号**。它由 Gfap/Vim/Serpina3n/Apoe/Lcn2/Clu 等驱动，方向明确（两样本一致上调），且不受深度 confounder 影响（模块分已校正）。补体仅微升，IFN/cGAS/NLRP3 **无激活**——说明该模型的胶质炎症走的是**反应性胶质化 + 微补体**路径，而非 I 型 IFN 或 NLRP3 炎症小体主导。
Microglia 的 DAM 上调 + 稳态下调方向符合"激活"，但 WT 基数太小，唯一能穿透深度差的证据是 **Apoe 检出率 0.125 → 0.629**（突变低深度反而检出更高，方向逆深度，可信）——即小胶质在突变中进入 Apoe+ DAM 样状态。
![胶质模块分布](D:/个人文档/PROJECTS/RGC-ADOA/output/12_inflammation/glia_module_distributions.png)
图 12：Müller 反应性胶质化 / 补体 / IFN-α 模块分布（按样本分面）——Müller 反应性胶质化在突变两样本整体右移（红/橙虚线上方），IFN-α 三组几乎重叠（无激活）。
### 4.1 GSEA prerank（Hallmark）——正式富集分析
方法：pseudobulk 一致性 LFC 排序列表 → `gseapy.prerank`，MSigDB Hallmark（小鼠 50 条，min_size=15，1000 次置换）。作者只用 Reactome/WikiPathways、未用 Hallmark，故此步是相对作者的增量（Reactome 富集直接以作者 S3–S12 交叉）。
| 通路 | Müller | RGC2-like | RGC1-like |
|---|---|---|---|
| TNF-α/NF-κB | **−2.09** (0.002) | +1.17 (ns) | +0.89 (ns) |
| Inflammatory Response | **−1.64** (0.06) | +0.98 (ns) | +1.11 (ns) |
| IFN-α Response | −1.25 (ns) | +0.77 (ns) | +0.73 (ns) |
| Oxidative Phosphorylation | **+1.93** (0.003) | +0.53 (ns) | **+2.34** (<10⁻³) |
| Glycolysis | +1.00 (ns) | +0.80 (ns) | +1.29 (0.22) |
| ROS Pathway | −0.77 (ns) | +0.76 (ns) | **+1.92** (0.003) |
| Myc Targets V1 | **+1.83** (0.003) | +0.54 (ns) | +1.54 (0.10) |
![Hallmark GSEA NES](D:/个人文档/PROJECTS/RGC-ADOA/output/16_gsea/gsea_hallmark_nes.png)
图 12b：三组 Hallmark GSEA 的 NES 条形图——Müller（蓝）在免疫炎症通路（TNF-α/NF-κB、Inflammatory、IFN、IL2-STAT5）上**负富集**，在 OXPHOS/Myc/mTORC1 上正富集；RGC1-like（粉）在 OXPHOS/ROS/糖酵解上强正富集；RGC2-like（红）无显著富集。
**GSEA 结论**（与模块打分完全自洽，并进一步收窄 H2）：
1. **Müller 的经典炎症通路是"负富集"而非激活**：TNF-α/NF-κB NES −2.09（FDR 0.002）、Inflammatory Response −1.64、IFN-α/γ 均负——Müller 的"反应性胶质化"不是 NF-κB/IFN 经典炎症，而是 **OXPHOS +1.93、Myc +1.83、mTORC1 +1.52 的代谢重编程**（与 §1 的 ETC 上调一致）。
2. **RGC1-like 的代谢代偿/氧化应激强富集**：OXPHOS +2.34（FDR<10⁻³）、ROS +1.92、糖酵解 +1.29——与模块分"RGC1-like 各模块上调"互证，提示该群处于代偿/应激状态。
3. **RGC2-like 无显著富集**（WT 7 核功效不足），但其代谢下调已由模块分（−0.30）与 pseudobulk 基因级 LFC（−0.70）确证。
---
## 5. P1-⑤ ISG 逐基因核查 + P1-⑥ 全局转录鉴别（联合回答 H2）
**ISG 逐基因**：RGC2-like 中 Ifit2 −2.57/−1.03、Cmpk2 −3.98/−2.45、Rtp4 −2.57/−2.57——确实强下调，与作者一致。**但** ISG 整体 vs 全基因背景不显著低于背景（p=0.16），且 housekeeping 基因中位数同样下降（−0.50）：
![ISG vs 背景](D:/个人文档/PROJECTS/RGC-ADOA/output/13_isg_check/isg_vs_background.png)
图 13：RGC2-like 中 ISG（红）与 housekeeping（蓝）的 LFC 分布相对全基因背景（灰）——ISG 与 HK 一起整体左移，说明是**全局性下调**的一部分，不是 IFN 通路的特异抑制。
![IFN 逐基因热图](D:/个人文档/PROJECTS/RGC-ADOA/output/17_visualization/ifn_gene_heatmap.png)
图 13b：IFN–JAK–STAT 通路逐基因 LFC 热图（pseudobulk 均值）——RGC2-like/RGC1-like 两列普遍为**负**（Ifit2/Ifitm3/Cmpk2/Stat1 下调），Müller 列同样以负为主（Jak1/Stat1/Ifit2 下调）——三类细胞均无 ISG 上调，印证"IFN 通路整体未激活"。
![IFN 热图 microglia](D:/个人文档/PROJECTS/RGC-ADOA/output/17_visualization/ifn_gene_heatmap_microglia.png)
图 13c：microglia 的 IFN 基因检出率差（Δdet = 突变−WT，WT 仅 8 核、深度差 2 倍，仅方向性）——IFN 通路基因（Jak1/Stat1/Stat3 等）检出率差为负或近零，同样无一致上调。
**全局转录鉴别**（Step 5.5）三条证据：
![全局转录鉴别](D:/个人文档/PROJECTS/RGC-ADOA/output/14_global_shift/global_shift.png)
图 14：A——每核检出基因数比值（突变/WT ≈0.5），但这被 §0 的"WT 非杆核深度 2 倍"污染；B——深度校正后的 housekeeping 模块分：**突变无塌陷**，多数细胞类型反而略升（+0.05~+0.13）；C——pseudobulk UP/DOWN 比全部 >1（突变上调占优）。
**结论：该模型不存在"无差别的全局转录塌陷"，但存在"基础程序的结构化下调"。** A 面板的"突变基因数减半"是 WT 非杆核深度异常的假象（B 面板深度校正后消失）；作者 RGC-2"1171 下调 vs 2 上调"的极端全下调偏斜在我们数据中未复现（我们 RGC2-like 是 1748 UP vs 1391 DOWN，上调更多）。但 pseudobulk 基因级层面，代谢基因（ETC −0.70、糖酵解 −0.44）与 housekeeping 基因（中位 −0.50）相对整体**确实下调**——这是"能量危机下关停昂贵基础程序"的结构化改变，而非深度 artifact 的无差别全下调。
因此 RGC 的 ISG 下调应解读为：**与代谢基因、管家基因一起的"基础程序"相对下调**（能量危机的转录特征），而非 IFN 通路特异激活/抑制。真正的炎症信号在**胶质侧**（Müller 反应性胶质化、microglia DAM），不在 RGC 本体。
---
## 6. 对研究假设的回应（P0+P1 综合）
- **H1（代谢直接受损）**：✅ **完整复现**。签名法锁定真正 RGC-2 后，ETC（−0.30）、CI biogenesis（−0.28）、糖酵解（−0.72）、线粒体自噬全部下调，pseudobulk 基因级 ETC 中位 log2FC 达 −0.70——作者的"Complex I/ETC/糖酵解协同下调"核心结论完整复现；Müller 的 ETC 上调经下采样裁决为真（+0.10），但其余"代谢全面上调"被否决。
- **H2（炎症轴）**：✅ 有方向性证据，但**路径收窄、且经典炎症不升反降**。Müller 反应性胶质化（最稳健）+ microglia DAM 转换（Apoe↑）+ 补体微升；**I 型 IFN/JAK-STAT、cGAS-STING、NLRP3 均无激活**，且 Hallmark GSEA 显示 Müller 的 TNF-α/NF-κB（NES −2.09）、Inflammatory Response（−1.64）**显著负富集**——Müller 的"激活"是代谢重编程（OXPHOS/Myc/mTORC1 上调），不是经典炎症激活。作者表中"IFN 富集"经证是通路名误导（真 ISG 是全局下调的一部分，非特异激活）。
- **机制链条（更新版）**：OPA1 缺陷 → RGC 能量危机（糖酵解代偿失败，H1 直接打击，且伴整体转录活力下降）→ Müller 反应性胶质化 + microglia 进入 DAM 状态（H2，胶质主导的慢性炎症/胶质增生）→ 可能的继发神经元损伤。**胶质炎症是 RGC 能量危机的下游响应，而非 IFN 抗病毒式炎症**。
---
## 7. 已知局限（P1 新增）
1. **WT 深度 confounder**：WT 非杆核深度是突变 2 倍，所有检出率比较不可靠（已改用模块分 + median-of-ratios LFC）。
2. **RGC2-like WT 仅 7 核、microglia WT 仅 8 核**：这两组的一切结论都是方向性，无统计功效（按第三轮裁决分别接受为"最保守下界"与"支持性线索"）。
3. pseudobulk 无正式 P 值（WT n=1），方向一致性 + S1/S2 阴性校准是无奈之选。
4. RGC-2 签名法固有低估（疾病下调的身份基因使突变"较健康"细胞才被分类为 RGC2-like）。
5. microglia DAM 结论依赖逆深度 Apoe 检出率 + 8 核的模块分，最弱。
6. **score_genes 对照校正的方法坑**：在"基础程序整体下调"背景下，`score_genes` 的对照基因（同表达量 bin 随机基因）也会下调，导致真实下调被抵消——P0 的"ETC 持平"即由此产生，改用 pseudobulk 基因级 LFC 后 ETC 才显现 −0.70。教训：涉及全局偏移的比较须交叉用"对照校正模块分"与"pseudobulk 基因级 LFC"两种口径。
## 8. 下一步（P2 建议，第三轮裁决已定优先级）
0. **（已完成）修订 P0/P1 报告**：补 ETC/CI 完整复现、修正 Müller 下采样表述、写入第三轮裁决。
1. **liana 细胞通讯**（Step 8，P2 首选）：Müller↔RGC↔microglia 的炎症配受体（补体 C3-C3aR、Apoe-Trem2/Lrp1、Spp1 轴），为"胶质炎症→神经元损伤"提供旁证。
2. **Müller 反应性胶质化深挖**：Gfap/Serpina3n 的细胞亚群异质性（是否存在"反应性 Müller 亚群"），与 ETC 上调是否同一群。
3. **cellbender**：已裁决**不启动**（两阶段终止；深度而非 ambient 是主要 confounder，多 marker 门控已规避 ambient，无 GPU 成本高）。
4. **confounder 加固**：追查 WT 非杆核深度 2 倍的根本原因（GEO reads/建库细节），不阻塞结论。
5. 若空转数据（Visium HD）后续可得，做空间验证（搁置中）。
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# 第一轮分析计划（v1）
> 日期：2026-09-17
> 依据：AGENTS.md、`doc/文献精读_Kang2026_ADOA_OPA1.md`、`script/01_explore_raw.py` 探索结果
> 数据：`data/GSE292269/{WT,Opa1V291D_S1,Opa1V291D_S2}/`（已解压整理为 Cell Ranger 目录结构）
---
## 0. 数据现状（探索结论，先行事实）
GEO 存放的是 **raw feature-barcode 矩阵**（非 filtered；矩阵元数据标注 Cell Ranger 4），需自行做空液滴过滤。54,232 个 feature（注释范围大于标准 mm10 参考，利于 lncRNA 等扩展分析）。
| 样本 | 基因型 | UMI≥500 & 基因≥300 核数 | 中位 UMI | 中位基因数 | mt% 中位 / p95 |
|---|---|---|---|---|---|
| WT | Opa1+/+ | **3,217** | 836 | 505 | 2.0% / 13.7% |
| Opa1V291D_S1 | V291D/+ | 10,835 | 1,255 | 871 | 0.7% / 4.1% |
| Opa1V291D_S2 | V291D/+ | 10,722 | 1,396 | 963 | 0.6% / 5.0% |
粗过滤合计 ~2.5 万核，高于原文的 19,315（其过滤更严）。关键观察：
1. **WT 文库明显更小、质量更差**（核数仅突变样本 1/3，UMI 中位数低 ~40%，mt% 偏高）→ 所有 WT vs Mutant 比较须控制测序深度/质量差异，报告中明示该局限。
2. **marker 粗查提示 ambient 污染**：P2ry12 在 ~6% 核中检出而 Aif1 仅 ~0.2%、C1qa ~0.8%，不一致 → 低 UMI 核中混入环境 RNA；microglia 判定必须**多 marker 共表达**（Aif1/C1qa/Tmem119），严禁单 marker。
3. RGC（Rbpms ~9-12%）、Müller（Rlbp1 ~18%、Glul ~40%）丰度符合预期；Gfap <1%（非反应态基线）。
## 1. 分析目标（承接 AGENTS.md）
- **Q1**：RGC、Müller、microglia 在 OPA1 突变后的细胞类型特异性改变。
- **Q2**：各细胞类型差异基因 + 富集；重点 IFN–JAK–STAT，泛化至炎症通路。
- **假设检验框架**：H1 代谢直接受损（复现原文 ETC/糖酵解下调，作阳性对照）；H2 炎症轴改变（mt 损伤→mtDNA/ROS→cGAS-STING/NLRP3→IFN/JAK-STAT）。⚠️ 依据补充材料预查（§1.1），H2 的方向需双向表述——**ISG 可能是下调（免疫麻痹/全局转录抑制）而非激活**，分析必须能区分这两种情形。
### 1.1 补充材料清单与预查发现（2026-09-17 更新）
已到位于 `ref/`：`sciadv.adx7815_sm.pdf`（图 S1–S9、表 S1–S4）+ `ref/Data files/`（12 个 Excel）。
| 文件 | 内容 | 用途 |
|---|---|---|
| 表 S3（sm.pdf） | 9 类细胞注释 marker | 注释对齐基准；**注意无 microglia marker** |
| `S2. snRNAseq-DEG_All_Celltype.xlsx` | 11 个 sheet：All_type（14,011 行汇总）+ 每细胞类型 DEG 全表（Gene/avg_log2FC/pct/p_val，未校正） | 与我们 DE 结果做**基因级一致性对照** |
| `S3–S12. Pathway_*.xlsx` | 每细胞类型 WikiPathways + Reactome 的**完整未过滤** ORA 结果（clusterProfiler 原始输出） | 通路级交叉验证；可重画富集图 |
| `S1. Spatial_Metabolomic_*.xlsx` | MALDI 靶向定量数值 | ATP/AMP/G6P 数值旁证 |
**预查发现（直接影响分析设计）：**
1. **作者注释体系中没有 microglia**：10 个 cluster = Rod/Cone/RGC-1/RGC-2/Amacrine/Müller/Uveal/Bipolar/Horizontal/Pericyte。microglia 大概率散落于 "Uveal/other" 或因数量过少未成群 → 我们的**免疫细胞捕捞步骤是硬性需求**。
2. **RGC-2 的 Reactome 显著通路（adj<0.1 共 202 条）中包含炎症条目，原文从未讨论**：Antiviral mechanism by IFN-stimulated genes（adj=0.026）、Interferon Signaling（adj=0.081）、Dectin-1/NF-κB、NIK→noncanonical NF-κB、FCERI-NF-κB——**我们的切入点在作者自己的数据里就有信号**。
3. **但方向是下调**：S2 表中 Ifit2、Stat1、Ifnar1/2、Jak1/2、Ifit3、Xaf1 等在 RGC-1/RGC-2/Müller 均为 DOWN（p<0.01）。IFN 通路富集由下调 ISG 驱动 → 需鉴别"免疫抑制/全局转录塌陷"与"通路特异性调控"。
4. **Müller 在作者分析中几乎无通路级信号**（Reactome 0 条显著），但 Apoe 是其头号上调基因（log2FC +1.33）——反应性胶质化信号可能真实存在但被其噪声大的细胞级 DE 淹没，适合用模块打分深挖。
## 2. 分析流程
### Step 1 — 质控与过滤 `script/02_qc_filter.py`
- 按样本分别定阈值（knee 拐点 + 绝对下限 UMI≥500、基因≥300 起步，依 QC 图微调）；mt% 阈值放宽（核测序，WT 用更严的 p95 控制 debris）。
- 双联体：scrublet（每样本独立）。
- 可选：ambient RNA 校正（cellbender 若算力允许；否则以多 marker 门控规避）。
- 产出：`data/01_filtered.h5ad`；`output/02_qc/`（小提琴图、散点、阈值表）。
### Step 2 — 归一化、整合与聚类 `script/03_integrate_cluster.py`
- 归一化 + log1p（scanpy 默认；平行试 scTransform 风格的 Pearson 残差可选）；HVG ~3000；PCA。
- **批次整合谨慎处理**：批次与基因型完全混杂（WT 单样本），整合可能过校正掉真实生物信号。策略：Harmony/scVI 按 sample 整合用于**聚类与注释**；**所有定量比较在原始（仅归一化）表达空间进行**。
- Leiden 多分辨率（0.4/0.8/1.2）+ UMAP。
- 产出：`data/02_clustered.h5ad`；`output/03_cluster/`。
### Step 3 — 细胞类型注释 `script/04_annotate.py`
- 经典 marker 手工注释，**基准用原文表 S3**（已存 `ref/sciadv.adx7815_sm/`：Rod—Cnga1/Gnat1/Rho/Sag 等；Cone—Arr3/Gnat2/Opn1mw；RGC—Rbpms/Slc17a6/Thy1；Amacrine—Gad1/Gad2/Tfap2b；Müller—Rlbp1/Slc1a3；Uveal—Gpnmb/Tyr；Bipolar—Grm6/Prkca/Cabp5；Horizontal—Lhx1/Onecut2/Prox1；Pericyte—Pdgfrb/Rgs5）。
- **microglia 专项捕捞**（作者未注释该类）：以 Aif1/C1qa/C1qb/Tmem119/P2ry12/Hexb 共表达为门控，先全数据低分辨率标记候选，再取出重聚类确认；同时检查其是否被作者并入 "Uveal/other"。
- 可选自动化辅助：CellTypist（Python）/参考映射（scANVI，参照已发表小鼠视网膜 atlas）。
- **RGC 亚群细分**：单独取 RGC 重聚类，复现原文 RGC-1/RGC-2，并对标已知 RGC 亚型 marker（Opn4/ipRGC 等）。
- 产出：`data/03_annotated.h5ad`；注释依据表 `output/04_annotation/marker_table.csv`。
### Step 4 — 细胞组成分析 `script/05_composition.py`
- 各细胞类型在 WT vs Mutant 的比例变化（ stacked bar + 每样本点）。
- 注意 n=1 vs n=2，仅作描述性展示；如需统计，用 S1/S2 间变异作噪声参照，不做正式检验。
### Step 5 — 差异表达（双层策略）`script/06_de.py`
- **探索层**：细胞级 Wilcoxon（scanpy `rank_genes_groups`），明确标注 pseudoreplication 局限，仅用于发现候选。
- **核心层 — 方向一致性策略**（应对 WT 无重复）：
1. pseudobulk（sample × celltype 求和）→ 分别算 S1 vs WT、S2 vs WT 的 log2FC；
2. 保留**两次比较方向一致**且细胞级探索层也一致的基因；
3. **S1 vs S2（同基因型）作阴性校准**，估计批次噪声基线，剔除在 S1-S2 中同样"差异"的基因；
4. pydeseq2 仅用于 LFC shrinkage 与可视化，不报告正式 P 值（WT n=1 无法估计组内方差），报告中如实说明。
- **与作者 DEG 表交叉验证**：将各细胞类型 log2FC 与 `S2. snRNAseq-DEG_All_Celltype.xlsx` 做基因级相关（Spearman）与方向一致率，输出对照表；分歧大的细胞类型需排查注释映射差异（尤其其 "Uveal" 与我们的 microglia 捕捞）。
- 产出：每细胞类型 DEG 表 `output/06_de/*.csv`；火山图/MA 图；作者对照一致性表。
### Step 5.5 — 全局转录抑制鉴别（新增，回应 §1.1 发现 3）`script/06b_global_shift.py`
- 检验 RGC（尤其 RGC-2）是否存在**整体性转录下调**而非通路特异调控：比较各细胞类型 × 基因型的每核总 UMI/检出基因数分布；housekeeping 基因集（如 Actb/Gapdh 慎用，改用 ERCC 等价物/HRT atlas 管家基因）log2FC 分布；DEG 中 UP/DOWN 数量比。
- 若存在全局下调，所有"通路下调"结论需以**相对其他细胞类型的富集特异性**表述，且 IFN/ISG 下调解读为全局塌陷的一部分还是独立事件，需用模块打分 z 值与全局背景比较。
### Step 6 — 炎症与通路分析（核心创新点）`script/07_inflammation.py`
- **模块打分**（decoupler / AUCell 路线，Python）：MSigDB Hallmark（小鼠版）——`INTERFERON_ALPHA_RESPONSE`、`INTERFERON_GAMMA_RESPONSE`、`IL6_JAK_STAT3_SIGNALING`、`TNFA_SIGNALING_VIA_NFKB`、`COMPLEMENT`、`INFLAMMATORY_RESPONSE`；Reactome——interferon signaling、cytokine signaling、cGAS-STING、NLRP3 inflammasome；另加线粒体应激相关基因集（UPRmt、mtDNA release）与 **Müller 反应性胶质化基因集**（Gfap/Vim/Serpina3n/Apoe/Lcn2 等）和 **microglia 激活态基因集**（DAM：Apoe/Spp1/Lpl/Trem2；稳态：P2ry12/Tmem119/Hexb）。
- 按细胞类型 × 基因型比较打分分布；重点 **RGC / Müller / microglia**。
- **双向解读**：作者数据显示 ISG 下调（§1.1 发现 3），分析时同时报告"激活"与"抑制"证据，避免确认偏误；与 Step 5.5 的全局背景联动解释。
- **GSEA preranked**：以 Step 5 的一致性 log2FC 排序列表做 gseapy prerank（Hallmark + Reactome 小鼠集）。
- **IFN–JAK–STAT 专项**：Ifnb1/Ifnar1/Ifnar2、Stat1/Stat2/Stat3、Irf7/Irf9、ISG（Isg15、Ifit1/2/3、Mx1/2、Oas1a、Cxcl10）逐基因表达热图 + 在每类细胞中的检出率；并与作者 S2 表中同基因 log2FC 并排对照。
- 产出：`output/07_inflammation/`（打分热图、ridge/violin 图、GSEA 表与点图）。
### Step 7 — 阳性对照：复现原文能量代谢结论 `script/08_replicate_metabolism.py`
- 在 RGC（及 RGC-1/RGC-2 亚群）检验 ETC、Complex I biogenesis、糖酵解基因下调是否复现；**基准直接用作者补充表**（S10 RGC-2 通路表的显著条目与其基因成员），而非仅对照正文图。
- 同时检查线粒体自噬/自噬基因集（原文 fig. S5：pyruvate metabolism & TCA、mitophagy、autophagy，adj.P=0.029/0.0075/0.0002）。
- **若复现失败需先排查 QC/注释差异，再下任何炎症结论**——这是整个再分析的校准锚点。
### Step 8（可选，视前期结果）— 细胞通讯 `script/09_cci.py`
- liana（Python 版 CellPhoneDB/CellChat 类）推断 RGC↔Müller↔microglia 的炎症配受体轴（如 IL-6、CXCL10-CXCR3、补体 C3-C3aR）。
- 为"线粒体损伤→胶质炎症→神经元损伤"链条提供旁证。
## 3. 预期产出清单
| 内容 | 位置 |
|---|---|
| QC 报告（阈值、双联体检出率、最终核数） | `output/02_qc/` |
| UMAP（样本/基因型/细胞类型/注释 marker） | `output/03_cluster/` |
| 注释 marker 证据表 | `output/04_annotation/` |
| 组成变化图 | `output/05_composition/` |
| 各细胞类型 DEG 表 + 图 | `output/06_de/` |
| 炎症模块打分与 GSEA 结果 | `output/07_inflammation/` |
| 能量代谢复现对照 | `output/08_replication/` |
| 第一轮分析报告（md，汇总图文与结论） | `doc/第一轮分析报告.md` |
所有图片 PNG、300 ppi；中间数据 h5ad 存 `data/`。
## 4. 风险与预案
| 风险 | 预案 |
|---|---|
| WT 文库质量差/核数少 → 稀有细胞类型在 WT 中缺失 | 组成与 DE 注明不确定性；必要时放宽 WT 阈值并做敏感性分析 |
| microglia 核数过少（预期 <1%，可能仅几十~一百核/样本） | 不做细胞级 DE，改用模块打分 + 检出率比较；全数据集中先低分辨率捞出所有免疫细胞再精细重聚类 |
| 批次=基因型混杂无法统计分离 | 方向一致性 + S1-vs-S2 阴性校准；结论措辞限定"提示/一致于"而非"证明" |
| 注释与原文不一致 | 以下载的 table S3 为准对齐；差异处记录并解释 |
| 空转数据仍缺 | 本轮分析不依赖空转；若获得，后续轮次做空间验证 |
## 5. 执行顺序与优先级
1. **P0**：Step 1–4（QC→聚类→注释→组成）——后续一切的基础。
2. **P0**：Step 7 阳性对照复现——先验证管线可信度。
3. **P1**：Step 5–6（DE + 炎症通路）——回答 Q1/Q2。
4. **P2**：Step 8 细胞通讯——加分项。
## 6. 决策记录（2026-09-17 grill-me 确认）
| 决策点 | 结论 |
|---|---|
| 本轮范围 | **仅 P0**（QC→聚类→注释→组成→代谢结论复现），P1/P2 下一轮 |
| QC 过滤 | **两阶段**：先简单阈值（knee + UMI/基因 + mt%，scrublet 去双联体）；若 ambient 明显干扰再补 cellbender 敏感性对比 |
| WT 文库质量差 | **全量分析 + 匹配敏感性对照**：定量比较时对 S1/S2 下采样至 WT 规模做核对，主结果用全量 |
| 批次整合 | **Harmony**（按 sample），仅用于聚类/注释；定量比较用原始归一化表达 |
| RGC 亚群 | 只复现 RGC-1/RGC-2，亚型身份粗标注，不引入外部 atlas |
| microglia 不足时 | 底线交付 = **模块打分 + 关键基因检出率**，不做细胞级 DE |
| 交付形式 | 每步一个 .py 脚本 + `doc/第一轮分析报告.md`，图片 PNG 300 ppi |
| 报告侧重 | **机制论证为主**（代谢 vs 炎症的时序与细胞类型归属） |
### 第二轮裁决（2026-09-17，P0 完成后 grill-me 确认）
| 悬案 | 裁决 |
|---|---|
| Müller 全面上调：真激活 vs 深度假象 | **P1 先做下采样敏感性分析**：S1/S2 下采样至 WT 核数与深度后重算模块分，上调保留才算真信号 |
| RGC-2 类似群定义（循环论证问题） | **改用作者 RGC-2 DEG 签名**（数据文件 S2 中 RGC-2 高表达基因）打分定义，弃用"WT ETC top30%" |
| H2 炎症轴重点 | **胶质为主**：Müller/microglia 的反应性胶质化 + IFN/补体模块 + DAM/稳态转换；RGC 本体只做 ISG 逐基因核查（预期抑制，与全局抑制鉴别） |
| RGC_lowETC 上调：代偿 vs 幸存者偏差 | **并列呈现不裁决**，留给实验（早期时间点）回答 |
| cellbender 时机 | 维持两阶段：仅当下采样裁决显示 ambient/深度影响关键结论时启动 |
| Visium HD 空转 | 搁置，不阻塞 P1/P2；机会性查找 |
### 第三轮裁决（2026-09-17，P1 完成后 grill-me 确认）
| 悬案/新问题 | 裁决 |
|---|---|
| 阳性对照 ETC/CI"未复现" | **实为完整复现**：P0 的"ETC 持平"是 ETC 分位法循环定义 artifact，签名法下 RGC2-like 的 ETC −0.30、CI −0.28、糖酵解 −0.72、自噬 −0.03 全部下调，pseudobulk 基因级 ETC 中位 −0.70。修订 P0/P1 报告 |
| RGC-2 定义与比例（签名法 5.8:1 vs 作者 1:1，WT 仅 7 核） | **接受签名法并标注低估**：签名法是最保守下界（突变 RGC-2 身份基因丢失被算入 RGC1-like），方向不反、效应量被低估；RGC2-like 的 7 WT 核作已知硬伤 |
| microglia 结论极弱（WT 8 核 + 深度 2 倍污染） | **接受方向性、重心转 Müller**：microglia 作支持性线索（Apoe↑ 逆深度 + DAM/稳态方向），主结论靠 Müller（183 WT 核）与 RGC；留待更大样本/空转 |
| cellbender 是否启动 | **不启动**（两阶段终止）：P1 已证主要 confounder 是深度（WT 非杆核 2 倍）而非 ambient；microglia 用多 marker 门控已规避 ambient；无 GPU 成本高 |
| Müller 下采样裁决的方法学错位 | **只改表述、不重做**：原假设"深度假象"方向反了（WT Müller 深度 3633 vs 突变 1802），下采样实际只匹配核数；但结论"ETC 真、其余假"仍成立，且 WT 深度优势下 ETC 仍上调 = 真信号甚至被低估 |
| WT 非杆核深度 2 倍的根本原因 | 待查（GEO reads/建库细节），不阻塞结论（已用 median-of-ratios 校正）；并入 P2 confounder 加固 |
| P2 优先级 | **先修报告（补 ETC 完整复现 + 修正 Müller 下采样表述 + 写入本轮裁决）→ 再 liana 细胞通讯** |
output/01_qc/barcode_threshold_summary.csv
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sample,umi_threshold,n_barcodes,median_umi,median_genes,total_genes_detected
WT,100,75506,329.0,214.0,29401
WT,500,3381,793.0,477.0,27281
WT,1000,1454,3656.5,1923.5,26893
Opa1V291D_S1,100,77137,384.0,268.0,31676
Opa1V291D_S1,500,10856,1250.5,869.0,30137
Opa1V291D_S1,1000,6074,2156.0,1342.0,29703
Opa1V291D_S2,100,77030,312.0,232.0,31426
Opa1V291D_S2,500,10733,1396.0,962.0,30274
Opa1V291D_S2,1000,7013,1909.0,1236.0,29891
1 sample umi_threshold n_barcodes median_umi median_genes total_genes_detected
2 WT 100 75506 329.0 214.0 29401
3 WT 500 3381 793.0 477.0 27281
4 WT 1000 1454 3656.5 1923.5 26893
5 Opa1V291D_S1 100 77137 384.0 268.0 31676
6 Opa1V291D_S1 500 10856 1250.5 869.0 30137
7 Opa1V291D_S1 1000 6074 2156.0 1342.0 29703
8 Opa1V291D_S2 100 77030 312.0 232.0 31426
9 Opa1V291D_S2 500 10733 1396.0 962.0 30274
10 Opa1V291D_S2 1000 7013 1909.0 1236.0 29891
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sample,genotype,raw_barcodes,after_threshold,after_doublet,mt_threshold_pct,median_umi,median_genes,median_mt_pct
WT,WT,1128179,2946,2944,10.0,868.0,528.5,1.82
Opa1V291D_S1,V291D,1450536,10749,10744,10.0,1266.5,881.0,0.66
Opa1V291D_S2,V291D,1308224,10584,10582,10.0,1412.0,972.0,0.57
1 sample genotype raw_barcodes after_threshold after_doublet mt_threshold_pct median_umi median_genes median_mt_pct
2 WT WT 1128179 2946 2944 10.0 868.0 528.5 1.82
3 Opa1V291D_S1 V291D 1450536 10749 10744 10.0 1266.5 881.0 0.66
4 Opa1V291D_S2 V291D 1308224 10584 10582 10.0 1412.0 972.0 0.57
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output/04_annotation/cluster_assignment_auto.csv
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cluster,assigned,margin,n_cells,top3
0,Oligodendrocyte,1.3755697841098848,141,"Oligodendrocyte:1.79, Amacrine:0.42, Endothelial:0.29"
1,Rod,1.2300866406657294,5455,"Rod:1.74, Muller:0.51, Cone:0.16"
10,Rod,2.26452357661412,2398,"Rod:2.23, Bipolar:-0.03, Muller:-0.14"
11,Oligodendrocyte,0.3319333256646525,298,"Oligodendrocyte:0.38, Horizontal:0.05, Amacrine:-0.03"
12,Muller,0.7522788595514944,693,"Muller:0.94, Bipolar:0.19, Astrocyte:0.08"
13,Amacrine,1.016295488953663,272,"Amacrine:1.13, Oligodendrocyte:0.11, Bipolar:-0.03"
14,Uveal_Melanocyte,3.2575989100035563,484,"Uveal_Melanocyte:3.56, RGC:0.30, Muller:0.03"
15,Muller,1.2139724591217655,231,"Muller:1.74, Rod:0.53, Uveal_Melanocyte:0.30"
16,Amacrine,1.795760767405739,179,"Amacrine:1.85, Oligodendrocyte:0.05, Microglia:-0.13"
17,Bipolar,1.395981248779651,300,"Bipolar:1.94, Oligodendrocyte:0.55, Horizontal:0.33"
18,Pericyte,1.5435031989213444,558,"Pericyte:1.98, Horizontal:0.43, Astrocyte:-0.05"
19,Horizontal,0.4418025383272959,408,"Horizontal:0.70, Endothelial:0.25, Amacrine:0.20"
2,RGC,0.10843120259880634,601,"RGC:0.81, Rod:0.70, Muller:0.67"
20,Horizontal,0.03255351501748738,237,"Horizontal:0.31, Oligodendrocyte:0.28, RGC:0.19"
21,Amacrine,0.8049685757975189,249,"Amacrine:0.78, Endothelial:-0.03, Astrocyte:-0.04"
22,Muller,2.220366027320143,1521,"Muller:3.86, Astrocyte:1.64, Pericyte:1.47"
23,Amacrine,1.021261164044226,407,"Amacrine:1.01, RGC:-0.01, Microglia:-0.09"
24,Oligodendrocyte,0.08580281939629406,262,"Oligodendrocyte:0.17, Horizontal:0.08, Endothelial:-0.02"
25,Oligodendrocyte,0.5957537873778048,143,"Oligodendrocyte:0.65, Endothelial:0.05, RGC:-0.13"
26,Pericyte,0.711183286004998,181,"Pericyte:0.88, Rod:0.17, Amacrine:0.06"
27,Endothelial,1.879617930122754,412,"Endothelial:4.92, Pericyte:3.04, Rod:0.34"
28,Amacrine,1.2662681907310283,344,"Amacrine:1.35, RGC:0.08, Microglia:-0.18"
29,Bipolar,0.7457429019218125,381,"Bipolar:1.40, Oligodendrocyte:0.66, Cone:0.09"
3,Rod,4.17394549123082,1269,"Rod:4.55, Cone:0.37, Muller:-0.05"
30,Amacrine,1.3017228024948597,250,"Amacrine:1.19, Oligodendrocyte:-0.11, Microglia:-0.17"
31,Microglia,6.113272965175508,140,"Microglia:7.10, Muller:0.99, Rod:0.18"
32,Oligodendrocyte,0.5640252468180003,192,"Oligodendrocyte:0.70, Horizontal:0.13, RGC:-0.15"
33,Amacrine,1.1254184997720227,147,"Amacrine:1.05, Microglia:-0.07, RGC:-0.22"
34,Horizontal,0.09526514691367217,148,"Horizontal:0.17, Oligodendrocyte:0.08, Amacrine:-0.04"
35,Horizontal,5.539253680442727,290,"Horizontal:5.65, Amacrine:0.11, Microglia:-0.13"
36,RGC,3.172001909360978,304,"RGC:3.20, Horizontal:0.03, Astrocyte:-0.02"
37,Uveal_Melanocyte,3.513250628567371,154,"Uveal_Melanocyte:3.90, Muller:0.39, RGC:0.27"
38,RGC,3.7443243727153517,133,"RGC:3.74, Astrocyte:-0.00, Microglia:-0.09"
39,Amacrine,2.0076484148148674,109,"Amacrine:2.07, Microglia:0.06, Bipolar:0.04"
4,Astrocyte,1.1433641059047646,105,"Astrocyte:3.77, Pericyte:2.63, Muller:1.94"
40,Pericyte,0.4618219796261279,252,"Pericyte:1.15, Horizontal:0.69, Oligodendrocyte:-0.11"
41,Endothelial,0.1927220130494714,117,"Endothelial:0.52, Muller:0.32, Amacrine:0.14"
42,Horizontal,0.13310181036933066,132,"Horizontal:0.87, Oligodendrocyte:0.74, Cone:-0.09"
43,Endothelial,0.02699846755432342,129,"Endothelial:0.09, Bipolar:0.06, Amacrine:-0.12"
44,Amacrine,1.7500744736413287,105,"Amacrine:2.12, Oligodendrocyte:0.37, Microglia:-0.10"
45,Bipolar,1.7545455057431887,131,"Bipolar:2.20, Cone:0.45, Horizontal:0.41"
46,RGC,3.7832653044842495,574,"RGC:3.67, Microglia:-0.11, Astrocyte:-0.17"
47,Amacrine,1.3667819994735397,60,"Amacrine:1.86, Bipolar:0.49, Oligodendrocyte:0.46"
48,Bipolar,0.611545404196687,139,"Bipolar:1.50, Oligodendrocyte:0.89, Horizontal:0.28"
49,Uveal_Melanocyte,2.740903872364484,84,"Uveal_Melanocyte:4.40, Oligodendrocyte:1.66, Astrocyte:1.61"
5,Rod,2.214564899854528,517,"Rod:2.29, Muller:0.08, Cone:-0.05"
50,Amacrine,0.26596532160558894,92,"Amacrine:0.42, Bipolar:0.15, Astrocyte:0.01"
51,Muller,1.5132444180288427,127,"Muller:2.83, Astrocyte:1.32, Endothelial:1.23"
6,Rod,0.43033463633817626,192,"Rod:0.70, Pericyte:0.27, Horizontal:-0.07"
7,Cone,6.050575049562531,1307,"Cone:6.89, Rod:0.84, Microglia:-0.18"
8,Amacrine,1.716464233353678,335,"Amacrine:1.99, Bipolar:0.27, RGC:-0.08"
9,Bipolar,2.1623359350656517,581,"Bipolar:2.98, Oligodendrocyte:0.82, Horizontal:0.50"
1 cluster assigned margin n_cells top3
2 0 Oligodendrocyte 1.3755697841098848 141 Oligodendrocyte:1.79, Amacrine:0.42, Endothelial:0.29
3 1 Rod 1.2300866406657294 5455 Rod:1.74, Muller:0.51, Cone:0.16
4 10 Rod 2.26452357661412 2398 Rod:2.23, Bipolar:-0.03, Muller:-0.14
5 11 Oligodendrocyte 0.3319333256646525 298 Oligodendrocyte:0.38, Horizontal:0.05, Amacrine:-0.03
6 12 Muller 0.7522788595514944 693 Muller:0.94, Bipolar:0.19, Astrocyte:0.08
7 13 Amacrine 1.016295488953663 272 Amacrine:1.13, Oligodendrocyte:0.11, Bipolar:-0.03
8 14 Uveal_Melanocyte 3.2575989100035563 484 Uveal_Melanocyte:3.56, RGC:0.30, Muller:0.03
9 15 Muller 1.2139724591217655 231 Muller:1.74, Rod:0.53, Uveal_Melanocyte:0.30
10 16 Amacrine 1.795760767405739 179 Amacrine:1.85, Oligodendrocyte:0.05, Microglia:-0.13
11 17 Bipolar 1.395981248779651 300 Bipolar:1.94, Oligodendrocyte:0.55, Horizontal:0.33
12 18 Pericyte 1.5435031989213444 558 Pericyte:1.98, Horizontal:0.43, Astrocyte:-0.05
13 19 Horizontal 0.4418025383272959 408 Horizontal:0.70, Endothelial:0.25, Amacrine:0.20
14 2 RGC 0.10843120259880634 601 RGC:0.81, Rod:0.70, Muller:0.67
15 20 Horizontal 0.03255351501748738 237 Horizontal:0.31, Oligodendrocyte:0.28, RGC:0.19
16 21 Amacrine 0.8049685757975189 249 Amacrine:0.78, Endothelial:-0.03, Astrocyte:-0.04
17 22 Muller 2.220366027320143 1521 Muller:3.86, Astrocyte:1.64, Pericyte:1.47
18 23 Amacrine 1.021261164044226 407 Amacrine:1.01, RGC:-0.01, Microglia:-0.09
19 24 Oligodendrocyte 0.08580281939629406 262 Oligodendrocyte:0.17, Horizontal:0.08, Endothelial:-0.02
20 25 Oligodendrocyte 0.5957537873778048 143 Oligodendrocyte:0.65, Endothelial:0.05, RGC:-0.13
21 26 Pericyte 0.711183286004998 181 Pericyte:0.88, Rod:0.17, Amacrine:0.06
22 27 Endothelial 1.879617930122754 412 Endothelial:4.92, Pericyte:3.04, Rod:0.34
23 28 Amacrine 1.2662681907310283 344 Amacrine:1.35, RGC:0.08, Microglia:-0.18
24 29 Bipolar 0.7457429019218125 381 Bipolar:1.40, Oligodendrocyte:0.66, Cone:0.09
25 3 Rod 4.17394549123082 1269 Rod:4.55, Cone:0.37, Muller:-0.05
26 30 Amacrine 1.3017228024948597 250 Amacrine:1.19, Oligodendrocyte:-0.11, Microglia:-0.17
27 31 Microglia 6.113272965175508 140 Microglia:7.10, Muller:0.99, Rod:0.18
28 32 Oligodendrocyte 0.5640252468180003 192 Oligodendrocyte:0.70, Horizontal:0.13, RGC:-0.15
29 33 Amacrine 1.1254184997720227 147 Amacrine:1.05, Microglia:-0.07, RGC:-0.22
30 34 Horizontal 0.09526514691367217 148 Horizontal:0.17, Oligodendrocyte:0.08, Amacrine:-0.04
31 35 Horizontal 5.539253680442727 290 Horizontal:5.65, Amacrine:0.11, Microglia:-0.13
32 36 RGC 3.172001909360978 304 RGC:3.20, Horizontal:0.03, Astrocyte:-0.02
33 37 Uveal_Melanocyte 3.513250628567371 154 Uveal_Melanocyte:3.90, Muller:0.39, RGC:0.27
34 38 RGC 3.7443243727153517 133 RGC:3.74, Astrocyte:-0.00, Microglia:-0.09
35 39 Amacrine 2.0076484148148674 109 Amacrine:2.07, Microglia:0.06, Bipolar:0.04
36 4 Astrocyte 1.1433641059047646 105 Astrocyte:3.77, Pericyte:2.63, Muller:1.94
37 40 Pericyte 0.4618219796261279 252 Pericyte:1.15, Horizontal:0.69, Oligodendrocyte:-0.11
38 41 Endothelial 0.1927220130494714 117 Endothelial:0.52, Muller:0.32, Amacrine:0.14
39 42 Horizontal 0.13310181036933066 132 Horizontal:0.87, Oligodendrocyte:0.74, Cone:-0.09
40 43 Endothelial 0.02699846755432342 129 Endothelial:0.09, Bipolar:0.06, Amacrine:-0.12
41 44 Amacrine 1.7500744736413287 105 Amacrine:2.12, Oligodendrocyte:0.37, Microglia:-0.10
42 45 Bipolar 1.7545455057431887 131 Bipolar:2.20, Cone:0.45, Horizontal:0.41
43 46 RGC 3.7832653044842495 574 RGC:3.67, Microglia:-0.11, Astrocyte:-0.17
44 47 Amacrine 1.3667819994735397 60 Amacrine:1.86, Bipolar:0.49, Oligodendrocyte:0.46
45 48 Bipolar 0.611545404196687 139 Bipolar:1.50, Oligodendrocyte:0.89, Horizontal:0.28
46 49 Uveal_Melanocyte 2.740903872364484 84 Uveal_Melanocyte:4.40, Oligodendrocyte:1.66, Astrocyte:1.61
47 5 Rod 2.214564899854528 517 Rod:2.29, Muller:0.08, Cone:-0.05
48 50 Amacrine 0.26596532160558894 92 Amacrine:0.42, Bipolar:0.15, Astrocyte:0.01
49 51 Muller 1.5132444180288427 127 Muller:2.83, Astrocyte:1.32, Endothelial:1.23
50 6 Rod 0.43033463633817626 192 Rod:0.70, Pericyte:0.27, Horizontal:-0.07
51 7 Cone 6.050575049562531 1307 Cone:6.89, Rod:0.84, Microglia:-0.18
52 8 Amacrine 1.716464233353678 335 Amacrine:1.99, Bipolar:0.27, RGC:-0.08
53 9 Bipolar 2.1623359350656517 581 Bipolar:2.98, Oligodendrocyte:0.82, Horizontal:0.50
output/04_annotation/cluster_marker_scores.csv
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,Rod,Cone,Bipolar,Horizontal,Amacrine,RGC,Muller,Microglia,Astrocyte,Pericyte,Endothelial,Uveal_Melanocyte,Oligodendrocyte
0,-0.37724321210888373,-0.14304587528999524,0.048475258997744686,-0.3096602636466186,0.4179403100731502,-0.3642067987732998,-0.328041518467747,-0.15811793674588123,-0.37213833465622564,-0.43111581393308873,0.28528741046550193,-0.20150596895302256,1.793510094183035
1,1.7379105159329633,0.1598194599729579,-0.342187847519546,-0.23381395451584544,-0.23652112475673923,-0.02614566361088035,0.5078238752672339,-0.0990715480307476,-0.10074370670663828,-0.2065506621580883,-0.16115796406670813,-0.1673983341006602,-0.6225677748460958
10,2.229881386322022,-0.15188741456603358,-0.034642190292098106,-0.3264514141421202,-0.5645880966126161,-0.3520379271329752,-0.1441980764033225,-0.15949724508944782,-0.25512653562129123,-0.3461021645305029,-0.19588863156207878,-0.22841371359961146,-0.3665168094007038
11,-0.292430762755162,-0.07133961414628137,-0.10101147808349584,0.047002723745046604,-0.026509957738049317,-0.22026772326745037,-0.47400542481642133,-0.0942982737865938,-0.3127053391254912,-0.25791633621846527,-0.1809302275358008,-0.27846253483716205,0.3789360494096991
12,-0.026089129010353807,-0.20076470348502742,0.1891966386109068,-0.34619606012325405,-0.6555249555225632,-0.08265983768782688,0.9414754981624012,-0.09987196747995533,0.08278043879706669,0.007889400696372298,-0.24026908240794423,-0.15817511795765857,-0.38513456938512386
13,-0.6479260065639337,-0.23596132286652668,-0.03130353807867302,-0.3168250802439749,1.1268369926992594,-0.14949113045371146,-0.548165824070966,-0.13443613887176797,-0.2849129176884248,-0.29534258130359603,-0.3260289257282898,-0.28513519121968245,0.11054150374559639
14,-0.019017700943647655,-0.19905900016751565,-0.5364757373703944,-0.3496770838028227,-0.6445102247525848,0.30211496446803293,0.030188446793490975,-0.13153883105746866,-0.16832779771992623,-0.0973568530066645,-0.3041662613189507,3.5597138744715893,-0.17663991605569307
15,0.5251516628128012,-0.17596731070101165,-0.35902346196290424,-0.27682999947182946,-0.5366024596290159,-0.4269277221166133,1.7391241219345668,-0.1143003865415915,-0.14202537290124292,-0.0876668740250407,-0.3067607218757933,0.2956875991403666,0.05194470547473914
16,-0.41838245272545266,-0.20177414842526356,-0.2688870098469336,-0.4123563897184898,1.8454537543741274,-0.33427782007674,-0.5280410675049886,-0.12747277171598898,-0.1819426303314299,-0.16426802146052297,-0.32477646433607166,-0.30875747082360566,0.049692986968388385
17,-0.024691946320397083,-0.11043669825349171,1.944630516390471,0.33018476752262355,-0.5997166678290338,-0.474812696671765,-0.4524863101824814,-0.19475003958985476,-0.12899251939591097,-0.21399779119252432,-0.27996240122363114,-0.2348910811819234,0.5486492676108198
18,-0.33043308219194795,-0.09038304584674368,-0.4729813014408131,0.4315663441190862,-0.6570928159983952,-0.42838137445870456,-0.43285168899781595,-0.13010778764849268,-0.05158573582043422,1.9750695430404306,-0.346471586442243,-0.24314973978471058,-0.12742350558783014
19,-0.5184418129821875,-0.1804858805642733,-0.4007126578325801,0.696327951230868,0.19906965915123154,-0.3985400752006914,-0.38498392029841816,-0.11539506213817044,-0.14982766461092475,-0.3184140688852882,0.2545254129035721,-0.2799491609538245,-0.5340577495912918
2,0.7045019383649167,0.11626339177951621,0.3356151989833981,-0.05549512130997727,-0.004302118444507036,0.812933140963723,0.6654894189130472,-0.10259286136983761,0.09255504560780455,-0.22317353115700456,-0.18216459274181543,-0.20256747251513926,-0.2915676365622264
20,-0.444749134276742,-0.25356781269453793,-0.022344434529271096,0.3119078633505435,-0.49056031895928065,0.1869380749866537,-0.24222028118620084,-0.16009178300097324,-0.15868807884792416,-0.40662469773707904,-0.08469533013190056,-0.24142410398660216,0.2793543483330561
21,-0.4401504327922638,-0.1818656670941654,-0.13251478522273277,-0.2286061467632724,0.775958984042895,-0.37348428664714545,-0.5195712114178949,-0.19736759733168502,-0.04302282318597045,-0.3655044202428739,-0.029009591754623903,-0.27629150771938676,-0.4671428217125282
22,-0.19948756411160085,-0.3045740277247771,0.08217221954059373,-0.11073015428221467,-0.6331861747141855,-0.23938953300652166,3.8565297578685787,-0.11903726610585438,1.6361637305484358,1.4692896506262016,1.372009366921464,-0.27560167840112887,-0.46416963265893413
23,-0.48227779530580595,-0.1776139937768579,-0.370273303338304,-0.2112042126376168,1.011188993323811,-0.010072170720415033,-0.39350905576110967,-0.09470406108466825,-0.26112724142824956,-0.3364844662822373,-0.24980982142426547,-0.24739244436860897,-0.10761914508267335
24,-0.15245867611200287,-0.06480821959357201,-0.1720367493910023,0.08185186917131138,-0.6008385826822445,-0.2947372847413105,-0.12402593494024959,-0.0992515177503315,-0.09262727125841001,-0.16842145512121195,-0.023464153826846107,-0.30786320009020063,0.16765468856760543
25,-0.39277207523477997,-0.33111033962816677,-0.45504304529372963,-0.3567005053081596,-0.4447286812918905,-0.12501837124584592,-0.4812425331282228,-0.1735594042602048,-0.30052373751433326,-0.23258301155555577,0.049332705092399586,-0.27272387817689747,0.6450864924702044
26,0.16924678928015285,-0.1386834351241617,-0.4826495594196555,-0.31892632629940826,0.057174679229100876,-0.21030544085004813,-0.08049909422883246,-0.14930667579244172,-0.018101679321317576,0.8804300752851508,-0.3154041073440852,-0.1855556556828535,-0.1146837138377567
27,0.3401753644221118,-0.08690292258818678,-0.544652769991036,-0.3158537031867746,-0.5702849872482636,0.14904334171544312,-0.06513255297518708,-0.19321885816184367,-0.17077631244217614,3.0384665996680025,4.9180845297907565,-0.20081446622269614,-0.6279888469043376
28,-0.5574192082262648,-0.2016586375624502,-0.4918816446636687,-0.3707916096789041,1.3460340325397055,0.07976584180867723,-0.48555282172356934,-0.17623583866155082,-0.18918313219582075,-0.20992042088334725,-0.35848306116235445,-0.3167086975159849,-0.4262405184047169
29,-0.09189249618267964,0.09462491498158924,1.4018634842527677,-0.11397383964350716,-0.6034846300827076,-0.4396584845847717,-0.469444253941585,-0.12071709409211592,-0.23096551942232568,-0.36934706940859213,-0.12186919194247613,-0.2527895644386711,0.6561205823309553
3,4.547154004950664,0.3732085137198436,-0.4576172584147028,-0.33904411465373246,-0.5395422287827151,-0.34222072871341785,-0.053521868487447076,-0.17526879638781537,-0.24737973632922708,-0.3450190003706314,-0.29163183344732985,-0.23983817038293223,-0.5684388694718002
30,-0.5409384091219412,-0.3263858264873685,-0.19746401600722513,-0.3144196115930952,1.1944328267262565,-0.40687541691010604,-0.3814600432436453,-0.1718724774070814,-0.2970088471301288,-0.39670753462248143,-0.23231305399619992,-0.3015775985281582,-0.10728997576860318
31,0.18463823124653472,0.05074659123506725,-0.38556493318080165,-0.2602409714541936,-0.4344118139598065,0.022668334694499966,0.9895583357526987,7.102831300928207,-0.16628355122316776,-0.3168268191565813,0.025390290868121213,0.019060494621156627,0.07481406331590534
32,-0.16274365624085635,-0.21710513588700397,-0.3999779806344927,0.13324165019142944,-0.5495613199342835,-0.1468614578909069,-0.3359527538733462,-0.17700237692568593,-0.17518016273580128,-0.3990779648792037,-0.23468722956756763,-0.2972997862815705,0.6972668970094297
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1 Rod Cone Bipolar Horizontal Amacrine RGC Muller Microglia Astrocyte Pericyte Endothelial Uveal_Melanocyte Oligodendrocyte
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36 4 0.05026925218963694 -0.15998416500973095 -0.13313155105426533 -0.3774887373841406 -0.680011406000569 -0.4579458293034233 1.9357322536832882 -0.20853888748998806 3.7732598222739493 2.6298957163691847 0.09567828747974395 -0.31407313076340565 -0.5679243421858685
37 40 -0.5006538285533462 -0.22882645164445634 -0.34598247893822465 0.6857347626697109 -0.716642242329088 -0.49581317782816914 -0.4919937018255817 -0.16035833522005266 -0.3120108830036078 1.1475567422958388 -0.2243052680723372 -0.2832995157240716 -0.10515433387891611
38 41 -0.5307839891329252 -0.3558180065971985 0.008765264361791897 -0.32371929275962635 0.13731948167293528 -0.3069090763269546 0.3244375160704657 -0.07522222790984602 -0.2730247550020009 -0.4022577367582322 0.5171595291199371 -0.29548332053263954 0.08376913662637757
39 42 -0.27276221173775994 -0.08740951372153433 -0.09483659418027864 0.8688624438541779 -0.277682926766169 -0.12927168735215963 -0.5226392706890527 -0.17431949974395197 -0.2914988656857655 -0.39584156504394963 -0.14189328424460693 -0.3316586105396336 0.7357606334848472
40 43 -0.5184603392124283 -0.256687650204004 0.061255468289833595 -0.18007201218305588 -0.12345358566280311 -0.4095600103878412 -0.386232136585819 -0.1607918905022587 -0.16467635600421557 -0.19280931104174753 0.08825393584415701 -0.22812449669907964 -0.1459922073963272
41 44 -0.6533610485524622 -0.2322571417588805 -0.5073079700463019 -0.3634016397683995 2.116337000526636 -0.6008721619844212 -0.4577880961521928 -0.0960706745583521 -0.21823148030480702 -0.42542890068122047 -0.14445707499047675 -0.2708975091970879 0.3662625268853075
42 45 0.2182186408255663 0.4469278684521114 2.2014733741953 0.40798370914685084 -0.695207587413912 -0.4272622366561965 -0.3066541781315916 -0.21139874657021912 -0.21686728536808952 -0.22923003979530648 -0.2692516931642204 -0.28064585387222407 0.23888364893982764
43 46 -0.8634412065159172 -0.3069832924333888 -0.43136046373213005 -0.22068178411816944 -0.6457291453230355 3.668650517634988 -0.5739849204355464 -0.11461478684926119 -0.17299149479936002 -0.374142351312137 -0.2894943030742785 -0.28330624106861885 -0.3856096859616807
44 47 -0.6174421786840012 -0.3942372223773669 0.49251645438908426 -0.4223356755038247 1.859298453862624 -0.34882084756082943 -0.42969615695880603 -0.13119076249167025 -0.26937108681031335 -0.08963245568709591 0.08882866869704827 -0.2693991109347712 0.45946938224720796
45 48 0.17234152855542706 0.12342949025385734 1.503367384319496 0.2756218158689328 -0.4716521430429585 -0.3237002644781542 -0.29355599100060703 -0.15552332698846646 -0.05819714275520777 -0.46560632408576696 -0.12535698306065876 -0.24229632419082736 0.8918219801228089
46 49 -0.15260069567554324 -0.1744292865589425 -0.2907754274920524 -0.33465414838468294 -0.6824913869971722 0.3548482172838723 -0.1752391182058467 -0.06276792169845995 1.6100037019195221 -0.3834240720322377 -0.19688549818539083 4.396356591797106 1.6554527194326223
47 5 2.2924521453699276 -0.04868552925630435 -0.10208249346406226 -0.32350677344131556 -0.44771810242474414 -0.2843313176243424 0.07788724551539983 -0.15122043924130382 -0.21997849576725714 -0.12323487897711918 -0.168222491452778 -0.2168979223754913 -0.40691431413371654
48 50 -0.6328360628240408 -0.3060749257575511 0.15297046345834053 -0.4293015775264076 0.4189357850639295 -0.31678141803471643 -0.29064024056133325 -0.19534259327638687 0.006023395016078331 -0.3410890634570635 -0.3349697214413698 -0.24385898367423423 -0.4216745955127019
49 51 0.9320353834537217 0.9008575612649524 0.27302272413835926 -0.07764727955861304 -0.6199507024115438 -0.28473682897147967 2.8333282476807624 -0.13501227852239558 1.3200838296519197 0.9784938709820894 1.226920927589184 -0.3148651041735418 -0.4811761237463559
50 6 0.7038304083933503 -0.2710181882553817 -0.4537183479367841 -0.06548652583165696 -0.5334629155016389 -0.47200641511315267 -0.22992571442676057 -0.24828066989226896 -0.3551914945826277 0.27349577205517406 -0.17432503684044917 -0.2801729854668586 -0.7006827647777941
51 7 0.8433920748263459 6.893967124388877 -0.4755675108668863 -0.3697122334699939 -0.6234692709802085 -0.3689923716118387 -0.3800690280825407 -0.17769864712213929 -0.3353946048886316 -0.3780630294738865 -0.32199485906932784 -0.23355488610130926 -0.3780736617350929
52 8 -0.6393302739734198 -0.21604649440330181 0.2738452468093719 -0.3522182692303293 1.9903094801630499 -0.0840396087542945 -0.42397951257851413 -0.15281315463781148 -0.0975378193953741 -0.31314483333498583 -0.33747771636893886 -0.2754544827653235 -0.203300357386208
53 9 0.03214630152706934 0.08286351142314362 2.978499919388449 0.5039313152120889 -0.3305686016825777 -0.27766905774178685 0.41884188592970156 -0.11773491257079385 0.2144060974756218 -0.006146704321918273 0.18846593633847733 -0.1451074939173756 0.8161639843227976
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output/05_composition/counts_per_sample.csv
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sample,Amacrine,Astrocyte,Bipolar,Cone,Endothelial,Horizontal,Microglia,Muller,Oligodendrocyte,Pericyte,RGC,Rod,Uveal_Melanocyte
Opa1V291D_S1,1065,32,577,584,178,137,57,1070,195,334,429,4610,219
Opa1V291D_S2,1208,58,849,624,195,140,75,1319,246,332,513,3256,450
WT,184,15,106,99,39,13,8,183,35,73,69,1773,53
1 sample Amacrine Astrocyte Bipolar Cone Endothelial Horizontal Microglia Muller Oligodendrocyte Pericyte RGC Rod Uveal_Melanocyte
2 Opa1V291D_S1 1065 32 577 584 178 137 57 1070 195 334 429 4610 219
3 Opa1V291D_S2 1208 58 849 624 195 140 75 1319 246 332 513 3256 450
4 WT 184 15 106 99 39 13 8 183 35 73 69 1773 53
output/05_composition/fractions_per_genotype.csv
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genotype,Amacrine,Astrocyte,Bipolar,Cone,Endothelial,Horizontal,Microglia,Muller,Oligodendrocyte,Pericyte,RGC,Rod,Uveal_Melanocyte
V291D,0.12121373720136519,0.004799488054607509,0.07604522184300341,0.064419795221843,0.019891211604095564,0.014771757679180887,0.007039249146757679,0.12739974402730375,0.02351749146757679,0.035516211604095564,0.050234641638225254,0.41947525597269625,0.03567619453924915
WT,0.06943396226415094,0.005660377358490566,0.04,0.03735849056603774,0.01471698113207547,0.004905660377358491,0.0030188679245283017,0.06905660377358491,0.013207547169811321,0.027547169811320753,0.026037735849056602,0.6690566037735849,0.02
1 genotype Amacrine Astrocyte Bipolar Cone Endothelial Horizontal Microglia Muller Oligodendrocyte Pericyte RGC Rod Uveal_Melanocyte
2 V291D 0.12121373720136519 0.004799488054607509 0.07604522184300341 0.064419795221843 0.019891211604095564 0.014771757679180887 0.007039249146757679 0.12739974402730375 0.02351749146757679 0.035516211604095564 0.050234641638225254 0.41947525597269625 0.03567619453924915
3 WT 0.06943396226415094 0.005660377358490566 0.04 0.03735849056603774 0.01471698113207547 0.004905660377358491 0.0030188679245283017 0.06905660377358491 0.013207547169811321 0.027547169811320753 0.026037735849056602 0.6690566037735849 0.02
output/05_composition/fractions_per_sample.csv
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sample,Amacrine,Astrocyte,Bipolar,Cone,Endothelial,Horizontal,Microglia,Muller,Oligodendrocyte,Pericyte,RGC,Rod,Uveal_Melanocyte
Opa1V291D_S1,0.11225888057341625,0.00337303678718246,0.060820069568883736,0.0615579213660799,0.018762517128702434,0.014440813745124908,0.0060082217771687575,0.11278591757141351,0.020554442921893117,0.03520607146621693,0.045219774428164855,0.48592811215347315,0.02308422051227996
Opa1V291D_S2,0.13038316243928763,0.006260118726389639,0.09163518618456556,0.06735024284943335,0.021046950890447922,0.015110631408526714,0.008094981111710739,0.14236373448461953,0.026551538046411225,0.035833783054506206,0.055369670804101455,0.35143011332973556,0.048569886670264434
WT,0.06943396226415094,0.005660377358490566,0.04,0.03735849056603774,0.01471698113207547,0.004905660377358491,0.0030188679245283017,0.06905660377358491,0.013207547169811321,0.027547169811320753,0.026037735849056602,0.6690566037735849,0.02
1 sample Amacrine Astrocyte Bipolar Cone Endothelial Horizontal Microglia Muller Oligodendrocyte Pericyte RGC Rod Uveal_Melanocyte
2 Opa1V291D_S1 0.11225888057341625 0.00337303678718246 0.060820069568883736 0.0615579213660799 0.018762517128702434 0.014440813745124908 0.0060082217771687575 0.11278591757141351 0.020554442921893117 0.03520607146621693 0.045219774428164855 0.48592811215347315 0.02308422051227996
3 Opa1V291D_S2 0.13038316243928763 0.006260118726389639 0.09163518618456556 0.06735024284943335 0.021046950890447922 0.015110631408526714 0.008094981111710739 0.14236373448461953 0.026551538046411225 0.035833783054506206 0.055369670804101455 0.35143011332973556 0.048569886670264434
4 WT 0.06943396226415094 0.005660377358490566 0.04 0.03735849056603774 0.01471698113207547 0.004905660377358491 0.0030188679245283017 0.06905660377358491 0.013207547169811321 0.027547169811320753 0.026037735849056602 0.6690566037735849 0.02
output/08_replication/genelevel_concordance_RGChighETC.csv
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output/08_replication/module_score_comparison.csv
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group,geneset,n_WT,n_V291D,median_WT,median_V291D,delta,wilcoxon_p_exploratory,per_sample_median
RGC_highETC(~RGC-2),ETC_WP295,21,658,-0.1946,-0.1777,0.0169,8.91e-01,Opa1V291D_S1:-0.181; Opa1V291D_S2:-0.175; WT:-0.195
RGC_highETC(~RGC-2),CI_biogenesis_RE,21,658,-0.1199,-0.1065,0.0135,4.31e-01,Opa1V291D_S1:-0.105; Opa1V291D_S2:-0.107; WT:-0.120
RGC_highETC(~RGC-2),Glycolysis_WP157,21,658,0.1865,0.0645,-0.122,1.01e-02,Opa1V291D_S1:0.049; Opa1V291D_S2:0.085; WT:0.187
RGC_highETC(~RGC-2),Ribosomal_WP163,21,658,0.0607,0.0574,-0.0033,8.51e-01,Opa1V291D_S1:0.067; Opa1V291D_S2:0.047; WT:0.061
RGC_highETC(~RGC-2),Mitophagy_Autophagy,21,658,0.0283,-0.0259,-0.0542,3.48e-02,Opa1V291D_S1:-0.036; Opa1V291D_S2:-0.022; WT:0.028
RGC_lowETC(~RGC-1),ETC_WP295,48,284,-0.3585,-0.306,0.0524,7.51e-09,Opa1V291D_S1:-0.298; Opa1V291D_S2:-0.310; WT:-0.358
RGC_lowETC(~RGC-1),CI_biogenesis_RE,48,284,-0.2539,-0.2048,0.0491,3.12e-05,Opa1V291D_S1:-0.200; Opa1V291D_S2:-0.213; WT:-0.254
RGC_lowETC(~RGC-1),Glycolysis_WP157,48,284,-0.1084,0.0418,0.1502,4.71e-09,Opa1V291D_S1:0.005; Opa1V291D_S2:0.073; WT:-0.108
RGC_lowETC(~RGC-1),Ribosomal_WP163,48,284,-0.0173,0.0344,0.0517,1.22e-12,Opa1V291D_S1:0.039; Opa1V291D_S2:0.025; WT:-0.017
RGC_lowETC(~RGC-1),Mitophagy_Autophagy,48,284,-0.0804,-0.0501,0.0303,2.11e-01,Opa1V291D_S1:-0.054; Opa1V291D_S2:-0.048; WT:-0.080
Muller,ETC_WP295,183,2389,-0.1572,-0.0374,0.1198,9.40e-18,Opa1V291D_S1:-0.030; Opa1V291D_S2:-0.040; WT:-0.157
Muller,CI_biogenesis_RE,183,2389,-0.12,-0.0401,0.0799,1.44e-11,Opa1V291D_S1:-0.027; Opa1V291D_S2:-0.048; WT:-0.120
Muller,Glycolysis_WP157,183,2389,-0.027,0.0957,0.1227,2.37e-08,Opa1V291D_S1:0.105; Opa1V291D_S2:0.085; WT:-0.027
Muller,Ribosomal_WP163,183,2389,0.052,0.1099,0.0579,1.85e-18,Opa1V291D_S1:0.127; Opa1V291D_S2:0.097; WT:0.052
Muller,Mitophagy_Autophagy,183,2389,0.0031,0.0006,-0.0025,5.19e-01,Opa1V291D_S1:-0.002; Opa1V291D_S2:0.004; WT:0.003
Rod,ETC_WP295,1773,7866,0.2393,0.2202,-0.0191,4.65e-05,Opa1V291D_S1:0.247; Opa1V291D_S2:0.175; WT:0.239
Rod,CI_biogenesis_RE,1773,7866,0.1843,0.1666,-0.0177,3.52e-01,Opa1V291D_S1:0.192; Opa1V291D_S2:0.133; WT:0.184
Rod,Glycolysis_WP157,1773,7866,0.6418,0.5654,-0.0764,8.04e-08,Opa1V291D_S1:0.584; Opa1V291D_S2:0.541; WT:0.642
Rod,Ribosomal_WP163,1773,7866,0.3027,0.2896,-0.0131,3.33e-06,Opa1V291D_S1:0.314; Opa1V291D_S2:0.236; WT:0.303
Rod,Mitophagy_Autophagy,1773,7866,-0.0343,-0.0451,-0.0108,2.65e-04,Opa1V291D_S1:-0.036; Opa1V291D_S2:-0.049; WT:-0.034
Cone,ETC_WP295,99,1208,-0.1267,-0.0106,0.1161,1.23e-18,Opa1V291D_S1:-0.009; Opa1V291D_S2:-0.012; WT:-0.127
Cone,CI_biogenesis_RE,99,1208,-0.0917,-0.0038,0.0879,3.90e-10,Opa1V291D_S1:0.003; Opa1V291D_S2:-0.013; WT:-0.092
Cone,Glycolysis_WP157,99,1208,0.0502,0.2017,0.1515,9.06e-10,Opa1V291D_S1:0.195; Opa1V291D_S2:0.218; WT:0.050
Cone,Ribosomal_WP163,99,1208,0.0557,0.1371,0.0814,2.13e-30,Opa1V291D_S1:0.149; Opa1V291D_S2:0.122; WT:0.056
Cone,Mitophagy_Autophagy,99,1208,-0.0822,-0.0601,0.0221,2.56e-02,Opa1V291D_S1:-0.038; Opa1V291D_S2:-0.075; WT:-0.082
Bipolar,ETC_WP295,106,1426,-0.1576,-0.0933,0.0643,5.37e-06,Opa1V291D_S1:-0.095; Opa1V291D_S2:-0.091; WT:-0.158
Bipolar,CI_biogenesis_RE,106,1426,-0.1219,-0.067,0.0548,4.32e-05,Opa1V291D_S1:-0.079; Opa1V291D_S2:-0.062; WT:-0.122
Bipolar,Glycolysis_WP157,106,1426,0.0026,0.0638,0.0611,5.00e-03,Opa1V291D_S1:0.062; Opa1V291D_S2:0.066; WT:0.003
Bipolar,Ribosomal_WP163,106,1426,0.0395,0.0986,0.059,5.73e-11,Opa1V291D_S1:0.111; Opa1V291D_S2:0.092; WT:0.040
Bipolar,Mitophagy_Autophagy,106,1426,-0.0443,-0.0391,0.0052,8.30e-01,Opa1V291D_S1:-0.020; Opa1V291D_S2:-0.055; WT:-0.044
Amacrine,ETC_WP295,184,2273,-0.3016,-0.178,0.1236,4.02e-39,Opa1V291D_S1:-0.176; Opa1V291D_S2:-0.180; WT:-0.302
Amacrine,CI_biogenesis_RE,184,2273,-0.2142,-0.116,0.0982,1.72e-31,Opa1V291D_S1:-0.112; Opa1V291D_S2:-0.121; WT:-0.214
Amacrine,Glycolysis_WP157,184,2273,-0.0855,0.0529,0.1385,2.04e-19,Opa1V291D_S1:0.041; Opa1V291D_S2:0.062; WT:-0.086
Amacrine,Ribosomal_WP163,184,2273,0.0038,0.0745,0.0707,3.44e-44,Opa1V291D_S1:0.087; Opa1V291D_S2:0.066; WT:0.004
Amacrine,Mitophagy_Autophagy,184,2273,-0.0772,-0.0386,0.0386,3.31e-02,Opa1V291D_S1:-0.045; Opa1V291D_S2:-0.034; WT:-0.077
1 group geneset n_WT n_V291D median_WT median_V291D delta wilcoxon_p_exploratory per_sample_median
2 RGC_highETC(~RGC-2) ETC_WP295 21 658 -0.1946 -0.1777 0.0169 8.91e-01 Opa1V291D_S1:-0.181; Opa1V291D_S2:-0.175; WT:-0.195
3 RGC_highETC(~RGC-2) CI_biogenesis_RE 21 658 -0.1199 -0.1065 0.0135 4.31e-01 Opa1V291D_S1:-0.105; Opa1V291D_S2:-0.107; WT:-0.120
4 RGC_highETC(~RGC-2) Glycolysis_WP157 21 658 0.1865 0.0645 -0.122 1.01e-02 Opa1V291D_S1:0.049; Opa1V291D_S2:0.085; WT:0.187
5 RGC_highETC(~RGC-2) Ribosomal_WP163 21 658 0.0607 0.0574 -0.0033 8.51e-01 Opa1V291D_S1:0.067; Opa1V291D_S2:0.047; WT:0.061
6 RGC_highETC(~RGC-2) Mitophagy_Autophagy 21 658 0.0283 -0.0259 -0.0542 3.48e-02 Opa1V291D_S1:-0.036; Opa1V291D_S2:-0.022; WT:0.028
7 RGC_lowETC(~RGC-1) ETC_WP295 48 284 -0.3585 -0.306 0.0524 7.51e-09 Opa1V291D_S1:-0.298; Opa1V291D_S2:-0.310; WT:-0.358
8 RGC_lowETC(~RGC-1) CI_biogenesis_RE 48 284 -0.2539 -0.2048 0.0491 3.12e-05 Opa1V291D_S1:-0.200; Opa1V291D_S2:-0.213; WT:-0.254
9 RGC_lowETC(~RGC-1) Glycolysis_WP157 48 284 -0.1084 0.0418 0.1502 4.71e-09 Opa1V291D_S1:0.005; Opa1V291D_S2:0.073; WT:-0.108
10 RGC_lowETC(~RGC-1) Ribosomal_WP163 48 284 -0.0173 0.0344 0.0517 1.22e-12 Opa1V291D_S1:0.039; Opa1V291D_S2:0.025; WT:-0.017
11 RGC_lowETC(~RGC-1) Mitophagy_Autophagy 48 284 -0.0804 -0.0501 0.0303 2.11e-01 Opa1V291D_S1:-0.054; Opa1V291D_S2:-0.048; WT:-0.080
12 Muller ETC_WP295 183 2389 -0.1572 -0.0374 0.1198 9.40e-18 Opa1V291D_S1:-0.030; Opa1V291D_S2:-0.040; WT:-0.157
13 Muller CI_biogenesis_RE 183 2389 -0.12 -0.0401 0.0799 1.44e-11 Opa1V291D_S1:-0.027; Opa1V291D_S2:-0.048; WT:-0.120
14 Muller Glycolysis_WP157 183 2389 -0.027 0.0957 0.1227 2.37e-08 Opa1V291D_S1:0.105; Opa1V291D_S2:0.085; WT:-0.027
15 Muller Ribosomal_WP163 183 2389 0.052 0.1099 0.0579 1.85e-18 Opa1V291D_S1:0.127; Opa1V291D_S2:0.097; WT:0.052
16 Muller Mitophagy_Autophagy 183 2389 0.0031 0.0006 -0.0025 5.19e-01 Opa1V291D_S1:-0.002; Opa1V291D_S2:0.004; WT:0.003
17 Rod ETC_WP295 1773 7866 0.2393 0.2202 -0.0191 4.65e-05 Opa1V291D_S1:0.247; Opa1V291D_S2:0.175; WT:0.239
18 Rod CI_biogenesis_RE 1773 7866 0.1843 0.1666 -0.0177 3.52e-01 Opa1V291D_S1:0.192; Opa1V291D_S2:0.133; WT:0.184
19 Rod Glycolysis_WP157 1773 7866 0.6418 0.5654 -0.0764 8.04e-08 Opa1V291D_S1:0.584; Opa1V291D_S2:0.541; WT:0.642
20 Rod Ribosomal_WP163 1773 7866 0.3027 0.2896 -0.0131 3.33e-06 Opa1V291D_S1:0.314; Opa1V291D_S2:0.236; WT:0.303
21 Rod Mitophagy_Autophagy 1773 7866 -0.0343 -0.0451 -0.0108 2.65e-04 Opa1V291D_S1:-0.036; Opa1V291D_S2:-0.049; WT:-0.034
22 Cone ETC_WP295 99 1208 -0.1267 -0.0106 0.1161 1.23e-18 Opa1V291D_S1:-0.009; Opa1V291D_S2:-0.012; WT:-0.127
23 Cone CI_biogenesis_RE 99 1208 -0.0917 -0.0038 0.0879 3.90e-10 Opa1V291D_S1:0.003; Opa1V291D_S2:-0.013; WT:-0.092
24 Cone Glycolysis_WP157 99 1208 0.0502 0.2017 0.1515 9.06e-10 Opa1V291D_S1:0.195; Opa1V291D_S2:0.218; WT:0.050
25 Cone Ribosomal_WP163 99 1208 0.0557 0.1371 0.0814 2.13e-30 Opa1V291D_S1:0.149; Opa1V291D_S2:0.122; WT:0.056
26 Cone Mitophagy_Autophagy 99 1208 -0.0822 -0.0601 0.0221 2.56e-02 Opa1V291D_S1:-0.038; Opa1V291D_S2:-0.075; WT:-0.082
27 Bipolar ETC_WP295 106 1426 -0.1576 -0.0933 0.0643 5.37e-06 Opa1V291D_S1:-0.095; Opa1V291D_S2:-0.091; WT:-0.158
28 Bipolar CI_biogenesis_RE 106 1426 -0.1219 -0.067 0.0548 4.32e-05 Opa1V291D_S1:-0.079; Opa1V291D_S2:-0.062; WT:-0.122
29 Bipolar Glycolysis_WP157 106 1426 0.0026 0.0638 0.0611 5.00e-03 Opa1V291D_S1:0.062; Opa1V291D_S2:0.066; WT:0.003
30 Bipolar Ribosomal_WP163 106 1426 0.0395 0.0986 0.059 5.73e-11 Opa1V291D_S1:0.111; Opa1V291D_S2:0.092; WT:0.040
31 Bipolar Mitophagy_Autophagy 106 1426 -0.0443 -0.0391 0.0052 8.30e-01 Opa1V291D_S1:-0.020; Opa1V291D_S2:-0.055; WT:-0.044
32 Amacrine ETC_WP295 184 2273 -0.3016 -0.178 0.1236 4.02e-39 Opa1V291D_S1:-0.176; Opa1V291D_S2:-0.180; WT:-0.302
33 Amacrine CI_biogenesis_RE 184 2273 -0.2142 -0.116 0.0982 1.72e-31 Opa1V291D_S1:-0.112; Opa1V291D_S2:-0.121; WT:-0.214
34 Amacrine Glycolysis_WP157 184 2273 -0.0855 0.0529 0.1385 2.04e-19 Opa1V291D_S1:0.041; Opa1V291D_S2:0.062; WT:-0.086
35 Amacrine Ribosomal_WP163 184 2273 0.0038 0.0745 0.0707 3.44e-44 Opa1V291D_S1:0.087; Opa1V291D_S2:0.066; WT:0.004
36 Amacrine Mitophagy_Autophagy 184 2273 -0.0772 -0.0386 0.0386 3.31e-02 Opa1V291D_S1:-0.045; Opa1V291D_S2:-0.034; WT:-0.077
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output/09_downsample/downsample_replicates.csv
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group,sample,seed,geneset,delta_median_score
Muller,Opa1V291D_S1,0,ETC_WP295,0.103
Muller,Opa1V291D_S1,0,CI_biogenesis_RE,0.0196
Muller,Opa1V291D_S1,0,Glycolysis_WP157,-0.0494
Muller,Opa1V291D_S1,0,Mitophagy_Autophagy,0.0787
Muller,Opa1V291D_S1,0,Ribosomal_WP163,-0.0159
Muller,Opa1V291D_S1,1,ETC_WP295,0.0822
Muller,Opa1V291D_S1,1,CI_biogenesis_RE,0.0151
Muller,Opa1V291D_S1,1,Glycolysis_WP157,-0.0282
Muller,Opa1V291D_S1,1,Mitophagy_Autophagy,0.0452
Muller,Opa1V291D_S1,1,Ribosomal_WP163,-0.0244
Muller,Opa1V291D_S1,2,ETC_WP295,0.1012
Muller,Opa1V291D_S1,2,CI_biogenesis_RE,-0.0279
Muller,Opa1V291D_S1,2,Glycolysis_WP157,-0.0175
Muller,Opa1V291D_S1,2,Mitophagy_Autophagy,0.0063
Muller,Opa1V291D_S1,2,Ribosomal_WP163,-0.0208
Muller,Opa1V291D_S1,3,ETC_WP295,0.0893
Muller,Opa1V291D_S1,3,CI_biogenesis_RE,-0.026
Muller,Opa1V291D_S1,3,Glycolysis_WP157,0.0019
Muller,Opa1V291D_S1,3,Mitophagy_Autophagy,0.0545
Muller,Opa1V291D_S1,3,Ribosomal_WP163,-0.0174
Muller,Opa1V291D_S1,4,ETC_WP295,0.1249
Muller,Opa1V291D_S1,4,CI_biogenesis_RE,-0.0127
Muller,Opa1V291D_S1,4,Glycolysis_WP157,-0.0035
Muller,Opa1V291D_S1,4,Mitophagy_Autophagy,0.0399
Muller,Opa1V291D_S1,4,Ribosomal_WP163,-0.0305
Muller,Opa1V291D_S2,0,ETC_WP295,0.1255
Muller,Opa1V291D_S2,0,CI_biogenesis_RE,0.0328
Muller,Opa1V291D_S2,0,Glycolysis_WP157,-0.0102
Muller,Opa1V291D_S2,0,Mitophagy_Autophagy,-0.0027
Muller,Opa1V291D_S2,0,Ribosomal_WP163,-0.0273
Muller,Opa1V291D_S2,1,ETC_WP295,0.0868
Muller,Opa1V291D_S2,1,CI_biogenesis_RE,-0.017
Muller,Opa1V291D_S2,1,Glycolysis_WP157,-0.049
Muller,Opa1V291D_S2,1,Mitophagy_Autophagy,0.0402
Muller,Opa1V291D_S2,1,Ribosomal_WP163,-0.0247
Muller,Opa1V291D_S2,2,ETC_WP295,0.0922
Muller,Opa1V291D_S2,2,CI_biogenesis_RE,-0.0219
Muller,Opa1V291D_S2,2,Glycolysis_WP157,-0.0103
Muller,Opa1V291D_S2,2,Mitophagy_Autophagy,0.0674
Muller,Opa1V291D_S2,2,Ribosomal_WP163,-0.026
Muller,Opa1V291D_S2,3,ETC_WP295,0.0806
Muller,Opa1V291D_S2,3,CI_biogenesis_RE,-0.0245
Muller,Opa1V291D_S2,3,Glycolysis_WP157,-0.0329
Muller,Opa1V291D_S2,3,Mitophagy_Autophagy,0.0174
Muller,Opa1V291D_S2,3,Ribosomal_WP163,-0.0178
Muller,Opa1V291D_S2,4,ETC_WP295,0.09
Muller,Opa1V291D_S2,4,CI_biogenesis_RE,0.0208
Muller,Opa1V291D_S2,4,Glycolysis_WP157,-0.0159
Muller,Opa1V291D_S2,4,Mitophagy_Autophagy,0.0431
Muller,Opa1V291D_S2,4,Ribosomal_WP163,-0.0145
RGC_highETC,Opa1V291D_S1,0,ETC_WP295,0.1482
RGC_highETC,Opa1V291D_S1,0,CI_biogenesis_RE,0.0239
RGC_highETC,Opa1V291D_S1,0,Glycolysis_WP157,-0.1312
RGC_highETC,Opa1V291D_S1,0,Mitophagy_Autophagy,-0.0023
RGC_highETC,Opa1V291D_S1,0,Ribosomal_WP163,0.0688
RGC_highETC,Opa1V291D_S1,1,ETC_WP295,0.1414
RGC_highETC,Opa1V291D_S1,1,CI_biogenesis_RE,0.0257
RGC_highETC,Opa1V291D_S1,1,Glycolysis_WP157,-0.1741
RGC_highETC,Opa1V291D_S1,1,Mitophagy_Autophagy,0.0178
RGC_highETC,Opa1V291D_S1,1,Ribosomal_WP163,0.0691
RGC_highETC,Opa1V291D_S1,2,ETC_WP295,0.1416
RGC_highETC,Opa1V291D_S1,2,CI_biogenesis_RE,0.0315
RGC_highETC,Opa1V291D_S1,2,Glycolysis_WP157,-0.1851
RGC_highETC,Opa1V291D_S1,2,Mitophagy_Autophagy,-0.0042
RGC_highETC,Opa1V291D_S1,2,Ribosomal_WP163,0.0563
RGC_highETC,Opa1V291D_S1,3,ETC_WP295,0.1386
RGC_highETC,Opa1V291D_S1,3,CI_biogenesis_RE,0.0348
RGC_highETC,Opa1V291D_S1,3,Glycolysis_WP157,-0.1279
RGC_highETC,Opa1V291D_S1,3,Mitophagy_Autophagy,-0.0122
RGC_highETC,Opa1V291D_S1,3,Ribosomal_WP163,0.0471
RGC_highETC,Opa1V291D_S1,4,ETC_WP295,0.1362
RGC_highETC,Opa1V291D_S1,4,CI_biogenesis_RE,0.0182
RGC_highETC,Opa1V291D_S1,4,Glycolysis_WP157,-0.1785
RGC_highETC,Opa1V291D_S1,4,Mitophagy_Autophagy,-0.0472
RGC_highETC,Opa1V291D_S1,4,Ribosomal_WP163,0.0489
RGC_highETC,Opa1V291D_S2,0,ETC_WP295,-0.026
RGC_highETC,Opa1V291D_S2,0,CI_biogenesis_RE,-0.0207
RGC_highETC,Opa1V291D_S2,0,Glycolysis_WP157,-0.1661
RGC_highETC,Opa1V291D_S2,0,Mitophagy_Autophagy,-0.0743
RGC_highETC,Opa1V291D_S2,0,Ribosomal_WP163,-0.0324
RGC_highETC,Opa1V291D_S2,1,ETC_WP295,0.1357
RGC_highETC,Opa1V291D_S2,1,CI_biogenesis_RE,0.0361
RGC_highETC,Opa1V291D_S2,1,Glycolysis_WP157,-0.173
RGC_highETC,Opa1V291D_S2,1,Mitophagy_Autophagy,0.0284
RGC_highETC,Opa1V291D_S2,1,Ribosomal_WP163,0.0596
RGC_highETC,Opa1V291D_S2,2,ETC_WP295,0.166
RGC_highETC,Opa1V291D_S2,2,CI_biogenesis_RE,0.0502
RGC_highETC,Opa1V291D_S2,2,Glycolysis_WP157,-0.1507
RGC_highETC,Opa1V291D_S2,2,Mitophagy_Autophagy,0.0123
RGC_highETC,Opa1V291D_S2,2,Ribosomal_WP163,0.0418
RGC_highETC,Opa1V291D_S2,3,ETC_WP295,0.1491
RGC_highETC,Opa1V291D_S2,3,CI_biogenesis_RE,0.0277
RGC_highETC,Opa1V291D_S2,3,Glycolysis_WP157,-0.1424
RGC_highETC,Opa1V291D_S2,3,Mitophagy_Autophagy,0.0215
RGC_highETC,Opa1V291D_S2,3,Ribosomal_WP163,0.053
RGC_highETC,Opa1V291D_S2,4,ETC_WP295,-0.0655
RGC_highETC,Opa1V291D_S2,4,CI_biogenesis_RE,0.0294
RGC_highETC,Opa1V291D_S2,4,Glycolysis_WP157,-0.1882
RGC_highETC,Opa1V291D_S2,4,Mitophagy_Autophagy,-0.0108
RGC_highETC,Opa1V291D_S2,4,Ribosomal_WP163,-0.0012
1 group sample seed geneset delta_median_score
2 Muller Opa1V291D_S1 0 ETC_WP295 0.103
3 Muller Opa1V291D_S1 0 CI_biogenesis_RE 0.0196
4 Muller Opa1V291D_S1 0 Glycolysis_WP157 -0.0494
5 Muller Opa1V291D_S1 0 Mitophagy_Autophagy 0.0787
6 Muller Opa1V291D_S1 0 Ribosomal_WP163 -0.0159
7 Muller Opa1V291D_S1 1 ETC_WP295 0.0822
8 Muller Opa1V291D_S1 1 CI_biogenesis_RE 0.0151
9 Muller Opa1V291D_S1 1 Glycolysis_WP157 -0.0282
10 Muller Opa1V291D_S1 1 Mitophagy_Autophagy 0.0452
11 Muller Opa1V291D_S1 1 Ribosomal_WP163 -0.0244
12 Muller Opa1V291D_S1 2 ETC_WP295 0.1012
13 Muller Opa1V291D_S1 2 CI_biogenesis_RE -0.0279
14 Muller Opa1V291D_S1 2 Glycolysis_WP157 -0.0175
15 Muller Opa1V291D_S1 2 Mitophagy_Autophagy 0.0063
16 Muller Opa1V291D_S1 2 Ribosomal_WP163 -0.0208
17 Muller Opa1V291D_S1 3 ETC_WP295 0.0893
18 Muller Opa1V291D_S1 3 CI_biogenesis_RE -0.026
19 Muller Opa1V291D_S1 3 Glycolysis_WP157 0.0019
20 Muller Opa1V291D_S1 3 Mitophagy_Autophagy 0.0545
21 Muller Opa1V291D_S1 3 Ribosomal_WP163 -0.0174
22 Muller Opa1V291D_S1 4 ETC_WP295 0.1249
23 Muller Opa1V291D_S1 4 CI_biogenesis_RE -0.0127
24 Muller Opa1V291D_S1 4 Glycolysis_WP157 -0.0035
25 Muller Opa1V291D_S1 4 Mitophagy_Autophagy 0.0399
26 Muller Opa1V291D_S1 4 Ribosomal_WP163 -0.0305
27 Muller Opa1V291D_S2 0 ETC_WP295 0.1255
28 Muller Opa1V291D_S2 0 CI_biogenesis_RE 0.0328
29 Muller Opa1V291D_S2 0 Glycolysis_WP157 -0.0102
30 Muller Opa1V291D_S2 0 Mitophagy_Autophagy -0.0027
31 Muller Opa1V291D_S2 0 Ribosomal_WP163 -0.0273
32 Muller Opa1V291D_S2 1 ETC_WP295 0.0868
33 Muller Opa1V291D_S2 1 CI_biogenesis_RE -0.017
34 Muller Opa1V291D_S2 1 Glycolysis_WP157 -0.049
35 Muller Opa1V291D_S2 1 Mitophagy_Autophagy 0.0402
36 Muller Opa1V291D_S2 1 Ribosomal_WP163 -0.0247
37 Muller Opa1V291D_S2 2 ETC_WP295 0.0922
38 Muller Opa1V291D_S2 2 CI_biogenesis_RE -0.0219
39 Muller Opa1V291D_S2 2 Glycolysis_WP157 -0.0103
40 Muller Opa1V291D_S2 2 Mitophagy_Autophagy 0.0674
41 Muller Opa1V291D_S2 2 Ribosomal_WP163 -0.026
42 Muller Opa1V291D_S2 3 ETC_WP295 0.0806
43 Muller Opa1V291D_S2 3 CI_biogenesis_RE -0.0245
44 Muller Opa1V291D_S2 3 Glycolysis_WP157 -0.0329
45 Muller Opa1V291D_S2 3 Mitophagy_Autophagy 0.0174
46 Muller Opa1V291D_S2 3 Ribosomal_WP163 -0.0178
47 Muller Opa1V291D_S2 4 ETC_WP295 0.09
48 Muller Opa1V291D_S2 4 CI_biogenesis_RE 0.0208
49 Muller Opa1V291D_S2 4 Glycolysis_WP157 -0.0159
50 Muller Opa1V291D_S2 4 Mitophagy_Autophagy 0.0431
51 Muller Opa1V291D_S2 4 Ribosomal_WP163 -0.0145
52 RGC_highETC Opa1V291D_S1 0 ETC_WP295 0.1482
53 RGC_highETC Opa1V291D_S1 0 CI_biogenesis_RE 0.0239
54 RGC_highETC Opa1V291D_S1 0 Glycolysis_WP157 -0.1312
55 RGC_highETC Opa1V291D_S1 0 Mitophagy_Autophagy -0.0023
56 RGC_highETC Opa1V291D_S1 0 Ribosomal_WP163 0.0688
57 RGC_highETC Opa1V291D_S1 1 ETC_WP295 0.1414
58 RGC_highETC Opa1V291D_S1 1 CI_biogenesis_RE 0.0257
59 RGC_highETC Opa1V291D_S1 1 Glycolysis_WP157 -0.1741
60 RGC_highETC Opa1V291D_S1 1 Mitophagy_Autophagy 0.0178
61 RGC_highETC Opa1V291D_S1 1 Ribosomal_WP163 0.0691
62 RGC_highETC Opa1V291D_S1 2 ETC_WP295 0.1416
63 RGC_highETC Opa1V291D_S1 2 CI_biogenesis_RE 0.0315
64 RGC_highETC Opa1V291D_S1 2 Glycolysis_WP157 -0.1851
65 RGC_highETC Opa1V291D_S1 2 Mitophagy_Autophagy -0.0042
66 RGC_highETC Opa1V291D_S1 2 Ribosomal_WP163 0.0563
67 RGC_highETC Opa1V291D_S1 3 ETC_WP295 0.1386
68 RGC_highETC Opa1V291D_S1 3 CI_biogenesis_RE 0.0348
69 RGC_highETC Opa1V291D_S1 3 Glycolysis_WP157 -0.1279
70 RGC_highETC Opa1V291D_S1 3 Mitophagy_Autophagy -0.0122
71 RGC_highETC Opa1V291D_S1 3 Ribosomal_WP163 0.0471
72 RGC_highETC Opa1V291D_S1 4 ETC_WP295 0.1362
73 RGC_highETC Opa1V291D_S1 4 CI_biogenesis_RE 0.0182
74 RGC_highETC Opa1V291D_S1 4 Glycolysis_WP157 -0.1785
75 RGC_highETC Opa1V291D_S1 4 Mitophagy_Autophagy -0.0472
76 RGC_highETC Opa1V291D_S1 4 Ribosomal_WP163 0.0489
77 RGC_highETC Opa1V291D_S2 0 ETC_WP295 -0.026
78 RGC_highETC Opa1V291D_S2 0 CI_biogenesis_RE -0.0207
79 RGC_highETC Opa1V291D_S2 0 Glycolysis_WP157 -0.1661
80 RGC_highETC Opa1V291D_S2 0 Mitophagy_Autophagy -0.0743
81 RGC_highETC Opa1V291D_S2 0 Ribosomal_WP163 -0.0324
82 RGC_highETC Opa1V291D_S2 1 ETC_WP295 0.1357
83 RGC_highETC Opa1V291D_S2 1 CI_biogenesis_RE 0.0361
84 RGC_highETC Opa1V291D_S2 1 Glycolysis_WP157 -0.173
85 RGC_highETC Opa1V291D_S2 1 Mitophagy_Autophagy 0.0284
86 RGC_highETC Opa1V291D_S2 1 Ribosomal_WP163 0.0596
87 RGC_highETC Opa1V291D_S2 2 ETC_WP295 0.166
88 RGC_highETC Opa1V291D_S2 2 CI_biogenesis_RE 0.0502
89 RGC_highETC Opa1V291D_S2 2 Glycolysis_WP157 -0.1507
90 RGC_highETC Opa1V291D_S2 2 Mitophagy_Autophagy 0.0123
91 RGC_highETC Opa1V291D_S2 2 Ribosomal_WP163 0.0418
92 RGC_highETC Opa1V291D_S2 3 ETC_WP295 0.1491
93 RGC_highETC Opa1V291D_S2 3 CI_biogenesis_RE 0.0277
94 RGC_highETC Opa1V291D_S2 3 Glycolysis_WP157 -0.1424
95 RGC_highETC Opa1V291D_S2 3 Mitophagy_Autophagy 0.0215
96 RGC_highETC Opa1V291D_S2 3 Ribosomal_WP163 0.053
97 RGC_highETC Opa1V291D_S2 4 ETC_WP295 -0.0655
98 RGC_highETC Opa1V291D_S2 4 CI_biogenesis_RE 0.0294
99 RGC_highETC Opa1V291D_S2 4 Glycolysis_WP157 -0.1882
100 RGC_highETC Opa1V291D_S2 4 Mitophagy_Autophagy -0.0108
101 RGC_highETC Opa1V291D_S2 4 Ribosomal_WP163 -0.0012
output/09_downsample/downsample_summary.csv
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group,sample,geneset,mean,std,count
Muller,Opa1V291D_S1,CI_biogenesis_RE,-0.0064,0.0225,5
Muller,Opa1V291D_S1,ETC_WP295,0.1001,0.0163,5
Muller,Opa1V291D_S1,Glycolysis_WP157,-0.0193,0.0205,5
Muller,Opa1V291D_S1,Mitophagy_Autophagy,0.0449,0.0262,5
Muller,Opa1V291D_S1,Ribosomal_WP163,-0.0218,0.0059,5
Muller,Opa1V291D_S2,CI_biogenesis_RE,-0.002,0.0267,5
Muller,Opa1V291D_S2,ETC_WP295,0.095,0.0176,5
Muller,Opa1V291D_S2,Glycolysis_WP157,-0.0237,0.0169,5
Muller,Opa1V291D_S2,Mitophagy_Autophagy,0.0331,0.0267,5
Muller,Opa1V291D_S2,Ribosomal_WP163,-0.0221,0.0056,5
RGC_highETC,Opa1V291D_S1,CI_biogenesis_RE,0.0268,0.0065,5
RGC_highETC,Opa1V291D_S1,ETC_WP295,0.1412,0.0045,5
RGC_highETC,Opa1V291D_S1,Glycolysis_WP157,-0.1594,0.0275,5
RGC_highETC,Opa1V291D_S1,Mitophagy_Autophagy,-0.0096,0.0237,5
RGC_highETC,Opa1V291D_S1,Ribosomal_WP163,0.058,0.0105,5
RGC_highETC,Opa1V291D_S2,CI_biogenesis_RE,0.0245,0.0268,5
RGC_highETC,Opa1V291D_S2,ETC_WP295,0.0719,0.1088,5
RGC_highETC,Opa1V291D_S2,Glycolysis_WP157,-0.1641,0.0181,5
RGC_highETC,Opa1V291D_S2,Mitophagy_Autophagy,-0.0046,0.0417,5
RGC_highETC,Opa1V291D_S2,Ribosomal_WP163,0.0242,0.0395,5
1 group sample geneset mean std count
2 Muller Opa1V291D_S1 CI_biogenesis_RE -0.0064 0.0225 5
3 Muller Opa1V291D_S1 ETC_WP295 0.1001 0.0163 5
4 Muller Opa1V291D_S1 Glycolysis_WP157 -0.0193 0.0205 5
5 Muller Opa1V291D_S1 Mitophagy_Autophagy 0.0449 0.0262 5
6 Muller Opa1V291D_S1 Ribosomal_WP163 -0.0218 0.0059 5
7 Muller Opa1V291D_S2 CI_biogenesis_RE -0.002 0.0267 5
8 Muller Opa1V291D_S2 ETC_WP295 0.095 0.0176 5
9 Muller Opa1V291D_S2 Glycolysis_WP157 -0.0237 0.0169 5
10 Muller Opa1V291D_S2 Mitophagy_Autophagy 0.0331 0.0267 5
11 Muller Opa1V291D_S2 Ribosomal_WP163 -0.0221 0.0056 5
12 RGC_highETC Opa1V291D_S1 CI_biogenesis_RE 0.0268 0.0065 5
13 RGC_highETC Opa1V291D_S1 ETC_WP295 0.1412 0.0045 5
14 RGC_highETC Opa1V291D_S1 Glycolysis_WP157 -0.1594 0.0275 5
15 RGC_highETC Opa1V291D_S1 Mitophagy_Autophagy -0.0096 0.0237 5
16 RGC_highETC Opa1V291D_S1 Ribosomal_WP163 0.058 0.0105 5
17 RGC_highETC Opa1V291D_S2 CI_biogenesis_RE 0.0245 0.0268 5
18 RGC_highETC Opa1V291D_S2 ETC_WP295 0.0719 0.1088 5
19 RGC_highETC Opa1V291D_S2 Glycolysis_WP157 -0.1641 0.0181 5
20 RGC_highETC Opa1V291D_S2 Mitophagy_Autophagy -0.0046 0.0417 5
21 RGC_highETC Opa1V291D_S2 Ribosomal_WP163 0.0242 0.0395 5
output/09_downsample/fulldata_deltas.csv
+21
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@@ -0,0 +1,21 @@
group,sample,geneset,delta_full
Muller,Opa1V291D_S1,ETC_WP295,0.127
Muller,Opa1V291D_S2,ETC_WP295,0.1176
Muller,Opa1V291D_S1,CI_biogenesis_RE,0.0927
Muller,Opa1V291D_S2,CI_biogenesis_RE,0.0722
Muller,Opa1V291D_S1,Glycolysis_WP157,0.1317
Muller,Opa1V291D_S2,Glycolysis_WP157,0.1125
Muller,Opa1V291D_S1,Mitophagy_Autophagy,-0.0051
Muller,Opa1V291D_S2,Mitophagy_Autophagy,0.001
Muller,Opa1V291D_S1,Ribosomal_WP163,0.0746
Muller,Opa1V291D_S2,Ribosomal_WP163,0.0447
RGC_highETC,Opa1V291D_S1,ETC_WP295,0.0141
RGC_highETC,Opa1V291D_S2,ETC_WP295,0.0196
RGC_highETC,Opa1V291D_S1,CI_biogenesis_RE,0.0151
RGC_highETC,Opa1V291D_S2,CI_biogenesis_RE,0.013
RGC_highETC,Opa1V291D_S1,Glycolysis_WP157,-0.138
RGC_highETC,Opa1V291D_S2,Glycolysis_WP157,-0.1011
RGC_highETC,Opa1V291D_S1,Mitophagy_Autophagy,-0.0643
RGC_highETC,Opa1V291D_S2,Mitophagy_Autophagy,-0.0499
RGC_highETC,Opa1V291D_S1,Ribosomal_WP163,0.0066
RGC_highETC,Opa1V291D_S2,Ribosomal_WP163,-0.0141
1 group sample geneset delta_full
2 Muller Opa1V291D_S1 ETC_WP295 0.127
3 Muller Opa1V291D_S2 ETC_WP295 0.1176
4 Muller Opa1V291D_S1 CI_biogenesis_RE 0.0927
5 Muller Opa1V291D_S2 CI_biogenesis_RE 0.0722
6 Muller Opa1V291D_S1 Glycolysis_WP157 0.1317
7 Muller Opa1V291D_S2 Glycolysis_WP157 0.1125
8 Muller Opa1V291D_S1 Mitophagy_Autophagy -0.0051
9 Muller Opa1V291D_S2 Mitophagy_Autophagy 0.001
10 Muller Opa1V291D_S1 Ribosomal_WP163 0.0746
11 Muller Opa1V291D_S2 Ribosomal_WP163 0.0447
12 RGC_highETC Opa1V291D_S1 ETC_WP295 0.0141
13 RGC_highETC Opa1V291D_S2 ETC_WP295 0.0196
14 RGC_highETC Opa1V291D_S1 CI_biogenesis_RE 0.0151
15 RGC_highETC Opa1V291D_S2 CI_biogenesis_RE 0.013
16 RGC_highETC Opa1V291D_S1 Glycolysis_WP157 -0.138
17 RGC_highETC Opa1V291D_S2 Glycolysis_WP157 -0.1011
18 RGC_highETC Opa1V291D_S1 Mitophagy_Autophagy -0.0643
19 RGC_highETC Opa1V291D_S2 Mitophagy_Autophagy -0.0499
20 RGC_highETC Opa1V291D_S1 Ribosomal_WP163 0.0066
21 RGC_highETC Opa1V291D_S2 Ribosomal_WP163 -0.0141
output/10_rgc2_signature/authors_RG1_signature.csv
+101
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@@ -0,0 +1,101 @@
gene,avg_log2fc_r1,pct.opa1_r1,pct.wt_r1,p_val_r1,avg_log2fc_r2,pct.opa1_r2,pct.wt_r2,p_val_r2,pct_diff_r2
Pcca,-0.11459343976799,0.568,0.831,0.0992359385549777,4.03683435447867,0.378,0.0,0.00977539194962215,-0.831
Ccny,0.112237022505018,0.537,0.797,0.53167566569716,2.75156403810611,0.361,0.083,0.033996371036788,-0.7140000000000001
Gm15414,-0.238933050272538,0.312,0.678,0.000774275837141936,3.55228758324298,0.245,0.0,0.0526521521466759,-0.678
Btbd7,0.197737457292821,0.552,0.831,0.680909155607548,2.43650839730342,0.337,0.167,0.111434905773546,-0.6639999999999999
Tle4,-0.170516586444484,0.559,0.814,0.0610759309140046,2.7066653306106,0.392,0.167,0.0504866317396016,-0.6469999999999999
Tmem63c,-0.487992008478252,0.258,0.644,3.37083351787917e-05,2.72397605223538,0.173,0.0,0.116210283090324,-0.644
Arfgef3,0.259980077782427,0.617,0.797,0.693300966391207,1.94617048049751,0.398,0.167,0.0621754365962196,-0.63
Plaa,-0.562695002051114,0.274,0.627,8.08254907889985e-05,2.93913887153656,0.2,0.0,0.0872165870175527,-0.627
Kcnh5,-0.245224575123709,0.797,0.932,0.0454607350528051,2.39220689390923,0.669,0.333,0.00168719374234735,-0.599
Ctnnal1,-0.457966946390789,0.425,0.847,0.00104406570766991,1.04599494661941,0.296,0.25,0.456328987267656,-0.597
E130307A14Rik,-0.222921914233354,0.592,0.847,0.0599510572377279,1.0059169784179,0.422,0.25,0.193147151281782,-0.597
Pibf1,-0.214670220121144,0.412,0.763,0.0212938176843002,0.720306962096653,0.257,0.167,0.446387699107806,-0.596
Med13l,-0.274843516135092,0.474,0.763,0.0247325799281074,2.33452432418036,0.355,0.167,0.0881052904178521,-0.596
Sarnp,-0.290906087762795,0.365,0.678,0.00886913067440994,2.16293058901288,0.208,0.083,0.235577579741124,-0.5950000000000001
Tmem87a,-0.462088758814833,0.276,0.593,0.000728737786648836,2.58843634831113,0.153,0.0,0.144288570191902,-0.593
Slc8a1,-0.167617870843234,0.811,0.915,0.26734795469745,0.639752906631129,0.629,0.333,0.0435510714518217,-0.5820000000000001
Gm26936,0.194984325338391,0.735,0.915,0.63028180838645,1.45230697394613,0.614,0.333,0.0198884539623186,-0.5820000000000001
Nrg2,-0.303891825450531,0.61,0.831,0.0406420363521536,2.04502513748872,0.406,0.25,0.0955075687453312,-0.581
Uggt2,-0.198287081705522,0.474,0.746,0.0290355653583915,1.63217884186897,0.294,0.167,0.211535089220621,-0.579
Sdk1,0.621913126514368,0.639,0.661,0.213143964249914,0.443058029144121,0.486,0.083,0.0203711728857302,-0.5780000000000001
AC122538.1,-0.570344790516947,0.225,0.576,3.13272974609786e-05,2.24122024485767,0.127,0.0,0.190430760288874,-0.576
Sik3,-0.195131326144749,0.78,0.983,0.0391110289706085,0.702448296306514,0.598,0.417,0.156157508448635,-0.5660000000000001
Dcdc5,-0.443105016334727,0.679,0.898,0.00252623740616387,-0.125005791511728,0.524,0.333,0.206451210803603,-0.565
Mapkap1,-0.109530170214569,0.519,0.814,0.111611643658441,0.654529072701961,0.294,0.25,0.59097991868271,-0.564
Tardbp,-0.176205817033319,0.381,0.729,0.0185434263495172,1.28059279962434,0.343,0.167,0.184429573473454,-0.5619999999999999
Lrrfip1,0.196097202115969,0.526,0.729,0.829934759426266,1.83118609700671,0.314,0.167,0.162623952932,-0.5619999999999999
Tcte2,-0.293644582514575,0.347,0.644,0.00510258102495708,1.43483473987814,0.239,0.083,0.197988222838213,-0.561
Sh3rf1,-0.173002375839651,0.403,0.644,0.053212211386035,1.2371908694104,0.241,0.083,0.227715725494983,-0.561
Reln,-0.208236668604835,0.644,0.881,0.0321072708404152,1.19840294161267,0.631,0.333,0.0353410190546521,-0.548
Gm44829,-0.273364837943327,0.621,0.881,0.172558319597169,1.85958355622124,0.533,0.333,0.0315828324898101,-0.548
Dtnb,-0.104977132641802,0.617,0.881,0.225821727832413,1.58299053831751,0.427,0.333,0.146470456676308,-0.548
Gucy1a2,-0.366965595045029,0.517,0.797,0.0102720244699212,1.31854764355235,0.333,0.25,0.336805794565742,-0.547
Rnf217,-0.263724699567528,0.408,0.712,0.0148688454955015,1.57057783393829,0.237,0.167,0.392528450623604,-0.5449999999999999
Nipsnap2,-0.281023289088293,0.321,0.627,0.00588458787209261,1.97569229113069,0.2,0.083,0.261213754790553,-0.544
Fam184a,0.185007442709405,0.37,0.627,0.141375018483547,2.03325198581294,0.231,0.083,0.184820235916324,-0.544
Phf20l1,-0.211970627385,0.708,0.949,0.0503307706784918,0.640372087557929,0.453,0.417,0.43861318913133,-0.532
Trpm7,-0.413716670633175,0.535,0.864,0.00222946637892411,1.26956803947982,0.363,0.333,0.323981683127971,-0.5309999999999999
Ints6,-0.574028997743286,0.428,0.78,0.00080566494827266,0.377078221925768,0.267,0.25,0.76802330783008,-0.53
Ncoa7,-0.152479694415144,0.563,0.78,0.0916747308179802,1.10297238725187,0.357,0.25,0.281682596290824,-0.53
Rock1,-0.240534468650114,0.677,0.932,0.0267371867704453,1.13562806203546,0.533,0.417,0.0867281480163916,-0.5150000000000001
Gm16599,0.244078843723442,0.784,0.932,0.535027100051553,1.22500343558963,0.657,0.417,0.0371536405526939,-0.5150000000000001
2610037D02Rik,-0.289304188536,0.508,0.847,0.0191847525497839,0.719379910499591,0.269,0.333,0.962022070574025,-0.514
Nt5c2,-0.294977790454494,0.532,0.847,0.0240475240335183,1.36529692261013,0.394,0.333,0.245784789125794,-0.514
Leng8,-0.331402033660265,0.519,0.763,0.0285683617455505,1.51315231364573,0.365,0.25,0.20637424652402,-0.513
Tox2,-0.154761382984966,0.479,0.763,0.0343022207056962,1.0605838986334,0.369,0.25,0.25993042132773,-0.513
Fam208a,-0.200498810187254,0.463,0.763,0.0539959161373689,0.88589514661926,0.339,0.25,0.381632001515381,-0.513
Mark2,-0.414613945689242,0.383,0.678,0.00375397808733913,1.25629858047334,0.294,0.167,0.24956109954494,-0.511
Ubac2,-0.21094362133341,0.365,0.678,0.0205364574384761,0.575303458997533,0.235,0.167,0.556122573755639,-0.511
Gm26749,0.103321605212769,0.403,0.678,0.162950430385706,1.82448318147512,0.278,0.167,0.250777337043919,-0.511
Micu2,0.201967302147211,0.445,0.678,0.378375513459497,1.75700818095589,0.308,0.167,0.171241507662337,-0.511
Rgs17,0.164976289455797,0.488,0.678,0.546764116880749,1.30917155770524,0.257,0.167,0.332425932286085,-0.511
Naa16,-0.497919813953157,0.29,0.593,0.00216091967150352,0.663515766900259,0.184,0.083,0.39525196645835,-0.51
Aaed1,0.157634567301847,0.356,0.593,0.159884224181814,1.64055823379347,0.227,0.083,0.199676562532527,-0.51
Zhx3,0.2827070147882,0.392,0.593,0.545648591337134,1.76795503005544,0.216,0.083,0.220593656787052,-0.51
Sgms1,0.242596412481901,0.506,0.593,0.583636773070548,0.716166411226331,0.351,0.083,0.0936423569006368,-0.51
Gm26904,0.420511474798147,0.757,0.915,0.0754897164509585,1.71393408682617,0.582,0.417,0.0392855977913849,-0.49800000000000005
Pan3,-0.178866091803952,0.659,0.915,0.0945246391274154,1.10560283970973,0.445,0.417,0.28458679727694,-0.49800000000000005
Chfr,-0.488957870386475,0.356,0.746,0.000158313799118571,0.862893889672321,0.224,0.25,0.828801450949251,-0.496
Sh3glb1,-0.394967707623058,0.414,0.746,0.00651697099034567,1.28083123976786,0.296,0.25,0.416563993708473,-0.496
Nav3,-0.476432414841157,0.584,0.746,0.0225300755294954,1.38064363568462,0.347,0.25,0.29850843796345,-0.496
Snx24,-0.201663837485646,0.345,0.661,0.00779775054257865,1.80160902531488,0.227,0.167,0.44134670973938,-0.494
Lrpprc,0.290735182932263,0.443,0.661,0.576346450053596,1.1530390110051,0.276,0.167,0.346763028257665,-0.494
Gtdc1,0.260060543220211,0.492,0.661,0.589948978915672,2.19919188297729,0.306,0.167,0.168002134761125,-0.494
Prkag2,0.196153476678954,0.468,0.661,0.629715725482696,1.73198303024379,0.292,0.167,0.215084043553859,-0.494
Ric8b,0.330101441849905,0.477,0.661,0.851870991712386,1.90736075958755,0.243,0.167,0.36201300125986,-0.494
Gm42769,-0.876884236449371,0.198,0.576,1.0947837529224e-07,1.3378041191462,0.143,0.083,0.496606371030909,-0.49299999999999994
Rars2,-0.249725841205927,0.31,0.576,0.012073389426004,1.33477120686983,0.202,0.083,0.269941243461637,-0.49299999999999994
Mtf2,0.171145202076659,0.365,0.576,0.351042563619126,1.55334133359152,0.2,0.083,0.266168587076133,-0.49299999999999994
Anks1b,0.110543138813593,0.967,0.983,0.400860657252171,1.29698831005829,0.849,0.5,0.00403831341471953,-0.483
Rbm5,-0.13660845289313,0.646,0.898,0.267535945251285,1.64970296868325,0.414,0.417,0.269551359561511,-0.48100000000000004
Picalm,-0.382155622008384,0.41,0.814,0.00089775141679032,0.545223562785275,0.292,0.333,0.832333633223849,-0.4809999999999999
Rnf130,-0.236907056142141,0.477,0.814,0.0191233231126417,0.287550805324708,0.333,0.333,0.882528284317602,-0.4809999999999999
Adk,-0.181406406690963,0.604,0.814,0.165560386962789,1.05130532204695,0.384,0.333,0.356198765500811,-0.4809999999999999
Prdm2,-0.181643907239009,0.381,0.729,0.00957006663392902,1.03705335968144,0.28,0.25,0.478758936532312,-0.479
Me3,0.120150946624214,0.452,0.729,0.165656029707176,1.23559792655242,0.259,0.25,0.608197053818817,-0.479
Slc25a27,0.104242813675642,0.457,0.729,0.432950201425916,1.269918006282,0.292,0.25,0.443714093247764,-0.479
Ssbp2,0.296843405024321,0.617,0.729,0.591014824485889,1.72525174458459,0.435,0.25,0.102435222447176,-0.479
Gab2,0.196894384019533,0.559,0.729,0.908645903691172,0.807668400227596,0.351,0.25,0.403891138958203,-0.479
Senp2,-0.185515087346808,0.383,0.644,0.0419224971577239,1.12484344549564,0.255,0.167,0.345688959969324,-0.477
Prr16,-0.43071294001728,0.586,0.644,0.276428160371796,1.85466266927493,0.427,0.167,0.0638794156803773,-0.477
Gm48512,0.292299592077301,0.528,0.644,0.336699681601466,2.51634228027436,0.386,0.167,0.0574408318194355,-0.477
Kdm5c,-0.139845148735642,0.285,0.559,0.0179333745103178,2.04591070827302,0.186,0.083,0.307233636169635,-0.47600000000000003
Herc4,0.104423953932095,0.347,0.559,0.202626410391349,1.97441341776085,0.231,0.083,0.183008189298896,-0.47600000000000003
Ctnnbl1,0.126315796554753,0.35,0.559,0.249705718674189,1.24440423739501,0.186,0.083,0.325985419313228,-0.47600000000000003
Dlgap1,0.214571572597658,0.878,0.966,0.694526455718769,0.502623482717812,0.708,0.5,0.377349600932446,-0.46599999999999997
Patj,-0.426214439177812,0.419,0.797,0.000237434542377364,0.215678449492842,0.253,0.333,0.803703826632158,-0.464
Sclt1,-0.278752228235497,0.572,0.881,0.0321761397489417,1.23278798510277,0.38,0.417,0.468084081743243,-0.464
Gapvd1,-0.181565073561962,0.552,0.881,0.0429251467717762,1.28060543958312,0.398,0.417,0.352797643108129,-0.464
Csmd3,-0.454241367105576,0.715,0.881,0.1635328468044,1.4792120552191,0.665,0.417,0.0490740423986681,-0.464
Snx27,-0.43577453837108,0.361,0.712,0.00119004057990781,0.904381542509282,0.247,0.25,0.7016019027373,-0.46199999999999997
Gm43320,-0.582580346441385,0.305,0.627,0.000263861892212186,0.394964871829057,0.19,0.167,0.758086015564098,-0.45999999999999996
Cttn,-0.168761568608169,0.29,0.627,0.00142650789463209,0.493845756658203,0.204,0.167,0.695685370968144,-0.45999999999999996
Pced1b,-0.107952289016465,0.359,0.627,0.0275820058965213,0.762824079623596,0.247,0.167,0.454108000722488,-0.45999999999999996
Ripor2,0.141596101921976,0.412,0.627,0.241433238103337,1.60508869157752,0.257,0.167,0.318196175839761,-0.45999999999999996
Bclaf3,-0.640239465331652,0.249,0.542,0.000215898445104092,1.79925481847151,0.163,0.083,0.389679521849519,-0.459
Ddhd1,0.138665329288669,0.365,0.542,0.396695229295796,1.15052733120665,0.227,0.083,0.243457824183978,-0.459
Gm26917,0.425145379976708,0.924,0.949,0.00386851898964332,1.19909565851273,0.747,0.5,0.0149614351144888,-0.44899999999999995
Dhdds,-0.334047403459908,0.376,0.78,0.00239801855871297,0.213863326790437,0.249,0.333,0.774099718179693,-0.447
Setd2,-0.175018182038382,0.414,0.78,0.0189345914487239,0.263844050343691,0.255,0.333,0.849186905354685,-0.447
Rufy2,-0.283500814570015,0.501,0.78,0.0203982332532047,0.862084923500499,0.357,0.333,0.435634054923212,-0.447
1 gene avg_log2fc_r1 pct.opa1_r1 pct.wt_r1 p_val_r1 avg_log2fc_r2 pct.opa1_r2 pct.wt_r2 p_val_r2 pct_diff_r2
2 Pcca -0.11459343976799 0.568 0.831 0.0992359385549777 4.03683435447867 0.378 0.0 0.00977539194962215 -0.831
3 Ccny 0.112237022505018 0.537 0.797 0.53167566569716 2.75156403810611 0.361 0.083 0.033996371036788 -0.7140000000000001
4 Gm15414 -0.238933050272538 0.312 0.678 0.000774275837141936 3.55228758324298 0.245 0.0 0.0526521521466759 -0.678
5 Btbd7 0.197737457292821 0.552 0.831 0.680909155607548 2.43650839730342 0.337 0.167 0.111434905773546 -0.6639999999999999
6 Tle4 -0.170516586444484 0.559 0.814 0.0610759309140046 2.7066653306106 0.392 0.167 0.0504866317396016 -0.6469999999999999
7 Tmem63c -0.487992008478252 0.258 0.644 3.37083351787917e-05 2.72397605223538 0.173 0.0 0.116210283090324 -0.644
8 Arfgef3 0.259980077782427 0.617 0.797 0.693300966391207 1.94617048049751 0.398 0.167 0.0621754365962196 -0.63
9 Plaa -0.562695002051114 0.274 0.627 8.08254907889985e-05 2.93913887153656 0.2 0.0 0.0872165870175527 -0.627
10 Kcnh5 -0.245224575123709 0.797 0.932 0.0454607350528051 2.39220689390923 0.669 0.333 0.00168719374234735 -0.599
11 Ctnnal1 -0.457966946390789 0.425 0.847 0.00104406570766991 1.04599494661941 0.296 0.25 0.456328987267656 -0.597
12 E130307A14Rik -0.222921914233354 0.592 0.847 0.0599510572377279 1.0059169784179 0.422 0.25 0.193147151281782 -0.597
13 Pibf1 -0.214670220121144 0.412 0.763 0.0212938176843002 0.720306962096653 0.257 0.167 0.446387699107806 -0.596
14 Med13l -0.274843516135092 0.474 0.763 0.0247325799281074 2.33452432418036 0.355 0.167 0.0881052904178521 -0.596
15 Sarnp -0.290906087762795 0.365 0.678 0.00886913067440994 2.16293058901288 0.208 0.083 0.235577579741124 -0.5950000000000001
16 Tmem87a -0.462088758814833 0.276 0.593 0.000728737786648836 2.58843634831113 0.153 0.0 0.144288570191902 -0.593
17 Slc8a1 -0.167617870843234 0.811 0.915 0.26734795469745 0.639752906631129 0.629 0.333 0.0435510714518217 -0.5820000000000001
18 Gm26936 0.194984325338391 0.735 0.915 0.63028180838645 1.45230697394613 0.614 0.333 0.0198884539623186 -0.5820000000000001
19 Nrg2 -0.303891825450531 0.61 0.831 0.0406420363521536 2.04502513748872 0.406 0.25 0.0955075687453312 -0.581
20 Uggt2 -0.198287081705522 0.474 0.746 0.0290355653583915 1.63217884186897 0.294 0.167 0.211535089220621 -0.579
21 Sdk1 0.621913126514368 0.639 0.661 0.213143964249914 0.443058029144121 0.486 0.083 0.0203711728857302 -0.5780000000000001
22 AC122538.1 -0.570344790516947 0.225 0.576 3.13272974609786e-05 2.24122024485767 0.127 0.0 0.190430760288874 -0.576
23 Sik3 -0.195131326144749 0.78 0.983 0.0391110289706085 0.702448296306514 0.598 0.417 0.156157508448635 -0.5660000000000001
24 Dcdc5 -0.443105016334727 0.679 0.898 0.00252623740616387 -0.125005791511728 0.524 0.333 0.206451210803603 -0.565
25 Mapkap1 -0.109530170214569 0.519 0.814 0.111611643658441 0.654529072701961 0.294 0.25 0.59097991868271 -0.564
26 Tardbp -0.176205817033319 0.381 0.729 0.0185434263495172 1.28059279962434 0.343 0.167 0.184429573473454 -0.5619999999999999
27 Lrrfip1 0.196097202115969 0.526 0.729 0.829934759426266 1.83118609700671 0.314 0.167 0.162623952932 -0.5619999999999999
28 Tcte2 -0.293644582514575 0.347 0.644 0.00510258102495708 1.43483473987814 0.239 0.083 0.197988222838213 -0.561
29 Sh3rf1 -0.173002375839651 0.403 0.644 0.053212211386035 1.2371908694104 0.241 0.083 0.227715725494983 -0.561
30 Reln -0.208236668604835 0.644 0.881 0.0321072708404152 1.19840294161267 0.631 0.333 0.0353410190546521 -0.548
31 Gm44829 -0.273364837943327 0.621 0.881 0.172558319597169 1.85958355622124 0.533 0.333 0.0315828324898101 -0.548
32 Dtnb -0.104977132641802 0.617 0.881 0.225821727832413 1.58299053831751 0.427 0.333 0.146470456676308 -0.548
33 Gucy1a2 -0.366965595045029 0.517 0.797 0.0102720244699212 1.31854764355235 0.333 0.25 0.336805794565742 -0.547
34 Rnf217 -0.263724699567528 0.408 0.712 0.0148688454955015 1.57057783393829 0.237 0.167 0.392528450623604 -0.5449999999999999
35 Nipsnap2 -0.281023289088293 0.321 0.627 0.00588458787209261 1.97569229113069 0.2 0.083 0.261213754790553 -0.544
36 Fam184a 0.185007442709405 0.37 0.627 0.141375018483547 2.03325198581294 0.231 0.083 0.184820235916324 -0.544
37 Phf20l1 -0.211970627385 0.708 0.949 0.0503307706784918 0.640372087557929 0.453 0.417 0.43861318913133 -0.532
38 Trpm7 -0.413716670633175 0.535 0.864 0.00222946637892411 1.26956803947982 0.363 0.333 0.323981683127971 -0.5309999999999999
39 Ints6 -0.574028997743286 0.428 0.78 0.00080566494827266 0.377078221925768 0.267 0.25 0.76802330783008 -0.53
40 Ncoa7 -0.152479694415144 0.563 0.78 0.0916747308179802 1.10297238725187 0.357 0.25 0.281682596290824 -0.53
41 Rock1 -0.240534468650114 0.677 0.932 0.0267371867704453 1.13562806203546 0.533 0.417 0.0867281480163916 -0.5150000000000001
42 Gm16599 0.244078843723442 0.784 0.932 0.535027100051553 1.22500343558963 0.657 0.417 0.0371536405526939 -0.5150000000000001
43 2610037D02Rik -0.289304188536 0.508 0.847 0.0191847525497839 0.719379910499591 0.269 0.333 0.962022070574025 -0.514
44 Nt5c2 -0.294977790454494 0.532 0.847 0.0240475240335183 1.36529692261013 0.394 0.333 0.245784789125794 -0.514
45 Leng8 -0.331402033660265 0.519 0.763 0.0285683617455505 1.51315231364573 0.365 0.25 0.20637424652402 -0.513
46 Tox2 -0.154761382984966 0.479 0.763 0.0343022207056962 1.0605838986334 0.369 0.25 0.25993042132773 -0.513
47 Fam208a -0.200498810187254 0.463 0.763 0.0539959161373689 0.88589514661926 0.339 0.25 0.381632001515381 -0.513
48 Mark2 -0.414613945689242 0.383 0.678 0.00375397808733913 1.25629858047334 0.294 0.167 0.24956109954494 -0.511
49 Ubac2 -0.21094362133341 0.365 0.678 0.0205364574384761 0.575303458997533 0.235 0.167 0.556122573755639 -0.511
50 Gm26749 0.103321605212769 0.403 0.678 0.162950430385706 1.82448318147512 0.278 0.167 0.250777337043919 -0.511
51 Micu2 0.201967302147211 0.445 0.678 0.378375513459497 1.75700818095589 0.308 0.167 0.171241507662337 -0.511
52 Rgs17 0.164976289455797 0.488 0.678 0.546764116880749 1.30917155770524 0.257 0.167 0.332425932286085 -0.511
53 Naa16 -0.497919813953157 0.29 0.593 0.00216091967150352 0.663515766900259 0.184 0.083 0.39525196645835 -0.51
54 Aaed1 0.157634567301847 0.356 0.593 0.159884224181814 1.64055823379347 0.227 0.083 0.199676562532527 -0.51
55 Zhx3 0.2827070147882 0.392 0.593 0.545648591337134 1.76795503005544 0.216 0.083 0.220593656787052 -0.51
56 Sgms1 0.242596412481901 0.506 0.593 0.583636773070548 0.716166411226331 0.351 0.083 0.0936423569006368 -0.51
57 Gm26904 0.420511474798147 0.757 0.915 0.0754897164509585 1.71393408682617 0.582 0.417 0.0392855977913849 -0.49800000000000005
58 Pan3 -0.178866091803952 0.659 0.915 0.0945246391274154 1.10560283970973 0.445 0.417 0.28458679727694 -0.49800000000000005
59 Chfr -0.488957870386475 0.356 0.746 0.000158313799118571 0.862893889672321 0.224 0.25 0.828801450949251 -0.496
60 Sh3glb1 -0.394967707623058 0.414 0.746 0.00651697099034567 1.28083123976786 0.296 0.25 0.416563993708473 -0.496
61 Nav3 -0.476432414841157 0.584 0.746 0.0225300755294954 1.38064363568462 0.347 0.25 0.29850843796345 -0.496
62 Snx24 -0.201663837485646 0.345 0.661 0.00779775054257865 1.80160902531488 0.227 0.167 0.44134670973938 -0.494
63 Lrpprc 0.290735182932263 0.443 0.661 0.576346450053596 1.1530390110051 0.276 0.167 0.346763028257665 -0.494
64 Gtdc1 0.260060543220211 0.492 0.661 0.589948978915672 2.19919188297729 0.306 0.167 0.168002134761125 -0.494
65 Prkag2 0.196153476678954 0.468 0.661 0.629715725482696 1.73198303024379 0.292 0.167 0.215084043553859 -0.494
66 Ric8b 0.330101441849905 0.477 0.661 0.851870991712386 1.90736075958755 0.243 0.167 0.36201300125986 -0.494
67 Gm42769 -0.876884236449371 0.198 0.576 1.0947837529224e-07 1.3378041191462 0.143 0.083 0.496606371030909 -0.49299999999999994
68 Rars2 -0.249725841205927 0.31 0.576 0.012073389426004 1.33477120686983 0.202 0.083 0.269941243461637 -0.49299999999999994
69 Mtf2 0.171145202076659 0.365 0.576 0.351042563619126 1.55334133359152 0.2 0.083 0.266168587076133 -0.49299999999999994
70 Anks1b 0.110543138813593 0.967 0.983 0.400860657252171 1.29698831005829 0.849 0.5 0.00403831341471953 -0.483
71 Rbm5 -0.13660845289313 0.646 0.898 0.267535945251285 1.64970296868325 0.414 0.417 0.269551359561511 -0.48100000000000004
72 Picalm -0.382155622008384 0.41 0.814 0.00089775141679032 0.545223562785275 0.292 0.333 0.832333633223849 -0.4809999999999999
73 Rnf130 -0.236907056142141 0.477 0.814 0.0191233231126417 0.287550805324708 0.333 0.333 0.882528284317602 -0.4809999999999999
74 Adk -0.181406406690963 0.604 0.814 0.165560386962789 1.05130532204695 0.384 0.333 0.356198765500811 -0.4809999999999999
75 Prdm2 -0.181643907239009 0.381 0.729 0.00957006663392902 1.03705335968144 0.28 0.25 0.478758936532312 -0.479
76 Me3 0.120150946624214 0.452 0.729 0.165656029707176 1.23559792655242 0.259 0.25 0.608197053818817 -0.479
77 Slc25a27 0.104242813675642 0.457 0.729 0.432950201425916 1.269918006282 0.292 0.25 0.443714093247764 -0.479
78 Ssbp2 0.296843405024321 0.617 0.729 0.591014824485889 1.72525174458459 0.435 0.25 0.102435222447176 -0.479
79 Gab2 0.196894384019533 0.559 0.729 0.908645903691172 0.807668400227596 0.351 0.25 0.403891138958203 -0.479
80 Senp2 -0.185515087346808 0.383 0.644 0.0419224971577239 1.12484344549564 0.255 0.167 0.345688959969324 -0.477
81 Prr16 -0.43071294001728 0.586 0.644 0.276428160371796 1.85466266927493 0.427 0.167 0.0638794156803773 -0.477
82 Gm48512 0.292299592077301 0.528 0.644 0.336699681601466 2.51634228027436 0.386 0.167 0.0574408318194355 -0.477
83 Kdm5c -0.139845148735642 0.285 0.559 0.0179333745103178 2.04591070827302 0.186 0.083 0.307233636169635 -0.47600000000000003
84 Herc4 0.104423953932095 0.347 0.559 0.202626410391349 1.97441341776085 0.231 0.083 0.183008189298896 -0.47600000000000003
85 Ctnnbl1 0.126315796554753 0.35 0.559 0.249705718674189 1.24440423739501 0.186 0.083 0.325985419313228 -0.47600000000000003
86 Dlgap1 0.214571572597658 0.878 0.966 0.694526455718769 0.502623482717812 0.708 0.5 0.377349600932446 -0.46599999999999997
87 Patj -0.426214439177812 0.419 0.797 0.000237434542377364 0.215678449492842 0.253 0.333 0.803703826632158 -0.464
88 Sclt1 -0.278752228235497 0.572 0.881 0.0321761397489417 1.23278798510277 0.38 0.417 0.468084081743243 -0.464
89 Gapvd1 -0.181565073561962 0.552 0.881 0.0429251467717762 1.28060543958312 0.398 0.417 0.352797643108129 -0.464
90 Csmd3 -0.454241367105576 0.715 0.881 0.1635328468044 1.4792120552191 0.665 0.417 0.0490740423986681 -0.464
91 Snx27 -0.43577453837108 0.361 0.712 0.00119004057990781 0.904381542509282 0.247 0.25 0.7016019027373 -0.46199999999999997
92 Gm43320 -0.582580346441385 0.305 0.627 0.000263861892212186 0.394964871829057 0.19 0.167 0.758086015564098 -0.45999999999999996
93 Cttn -0.168761568608169 0.29 0.627 0.00142650789463209 0.493845756658203 0.204 0.167 0.695685370968144 -0.45999999999999996
94 Pced1b -0.107952289016465 0.359 0.627 0.0275820058965213 0.762824079623596 0.247 0.167 0.454108000722488 -0.45999999999999996
95 Ripor2 0.141596101921976 0.412 0.627 0.241433238103337 1.60508869157752 0.257 0.167 0.318196175839761 -0.45999999999999996
96 Bclaf3 -0.640239465331652 0.249 0.542 0.000215898445104092 1.79925481847151 0.163 0.083 0.389679521849519 -0.459
97 Ddhd1 0.138665329288669 0.365 0.542 0.396695229295796 1.15052733120665 0.227 0.083 0.243457824183978 -0.459
98 Gm26917 0.425145379976708 0.924 0.949 0.00386851898964332 1.19909565851273 0.747 0.5 0.0149614351144888 -0.44899999999999995
99 Dhdds -0.334047403459908 0.376 0.78 0.00239801855871297 0.213863326790437 0.249 0.333 0.774099718179693 -0.447
100 Setd2 -0.175018182038382 0.414 0.78 0.0189345914487239 0.263844050343691 0.255 0.333 0.849186905354685 -0.447
101 Rufy2 -0.283500814570015 0.501 0.78 0.0203982332532047 0.862084923500499 0.357 0.333 0.435634054923212 -0.447
output/10_rgc2_signature/authors_RG2_signature.csv
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gene,avg_log2fc_r1,pct.opa1_r1,pct.wt_r1,p_val_r1,avg_log2fc_r2,pct.opa1_r2,pct.wt_r2,p_val_r2,pct_diff_r2
Tubb3,0.79450472774753,0.109,0.119,0.958485404528225,-2.84060326306335,0.12,0.833,2.5972044241376e-12,0.714
Eef2,0.946852168537043,0.292,0.339,0.554692826981986,-1.61901498373035,0.306,1.0,2.06785445452351e-06,0.661
Tgoln1,1.78036354603182,0.225,0.153,0.0685588946189396,-1.59766881199497,0.153,0.75,1.56898706300087e-06,0.597
Timm13,0.383182577176802,0.138,0.153,0.960061993857616,-1.24427119609979,0.167,0.75,1.36377059109632e-05,0.597
Tubb4b,-0.182716806279187,0.165,0.237,0.328885604097807,-2.59923429460839,0.165,0.833,3.44319082399783e-09,0.596
Rpl21,0.932953806641402,0.316,0.322,0.28215571866864,-1.07923839551978,0.322,0.917,0.00091167025666756,0.595
Atp5l,0.9465691739094,0.267,0.322,0.768526015392968,-1.8175107125187,0.218,0.917,3.74537113361768e-07,0.595
Uqcr11,1.08152098660872,0.194,0.169,0.353189722220523,-1.14803206707634,0.2,0.75,0.000176273116755541,0.581
Rpl18a,0.45455529148095,0.327,0.339,0.631994511766892,-1.49637329449574,0.369,0.917,0.000290393226658511,0.5780000000000001
Chchd2,0.810218166641106,0.43,0.441,0.111692922941569,-0.845993845078889,0.471,1.0,0.00388182855018752,0.5589999999999999
Rpl18,0.528446988169901,0.2,0.22,0.968392744036157,-1.45687981171311,0.206,0.75,4.29458701023366e-05,0.53
Atp5j,0.40697786154564,0.229,0.305,0.663022485930743,-1.33170456668031,0.263,0.833,0.000308766307816131,0.528
Atp6v1e1,0.136685146613233,0.285,0.39,0.621417622024421,-1.5876255300904,0.253,0.917,3.1740687004702e-06,0.527
Tceal9,0.641146250721447,0.12,0.153,0.719452982315463,-1.07142829739176,0.141,0.667,2.12341492463759e-05,0.514
Fabp5,1.21129255499213,0.303,0.237,0.0787348997931872,-0.795617397292901,0.302,0.75,0.01906057128027,0.513
Bex3,0.642072493043613,0.169,0.237,0.535146457240487,-1.21116718093423,0.21,0.75,0.000182278005944436,0.513
Elob,0.47742771361923,0.171,0.237,0.544913208915019,-1.30683418103432,0.184,0.75,4.37479682506387e-05,0.513
Tubb2a,0.717221959140293,0.185,0.237,0.76744471309425,-1.67253347444918,0.216,0.75,3.70470777766425e-05,0.513
Ndufb8,0.158452570666001,0.198,0.237,0.82355885360063,-1.42915572825392,0.249,0.75,0.000357593588132878,0.513
Tagln3,-0.278895963470218,0.156,0.322,0.0131166748085004,-1.92367781633955,0.155,0.833,2.36394342438425e-08,0.5109999999999999
Pomp,0.841895825583676,0.178,0.169,0.547346403162976,-1.46526550394965,0.147,0.667,2.56000174106239e-05,0.498
Ndufb7,0.602688557677008,0.178,0.169,0.634372817842982,-1.59858275858968,0.155,0.667,3.0226374855996e-05,0.498
Cyb5r3,-0.559098180896072,0.031,0.085,0.0520353149998478,-3.22184866239203,0.041,0.583,1.42053138037143e-15,0.49799999999999994
AC121965.1,0.416684792807243,0.185,0.254,0.5376138613364,-1.00114503302094,0.171,0.75,4.50689569736277e-05,0.496
Tubb5,0.546129253440581,0.194,0.254,0.654405345908873,-1.64847060479884,0.243,0.75,0.000126980493805329,0.496
Chchd10,0.808406780550554,0.238,0.254,0.682369719829123,-0.872957076870523,0.28,0.75,0.00465192049670251,0.496
Hist1h4d,0.301589308835843,0.205,0.254,0.758821714552774,-1.13005464593582,0.216,0.75,0.000507756390389973,0.496
Ubc,0.729385658990562,0.272,0.339,0.961486281648046,-1.89693262657494,0.235,0.833,3.32888407561134e-06,0.49399999999999994
Rplp1,1.07487179289198,0.499,0.424,0.00628120568658711,-0.467434255833367,0.488,0.917,0.080861982168712,0.49300000000000005
Rps15,1.31896514865808,0.439,0.424,0.0185070874821656,-0.720089054690377,0.406,0.917,0.00919016506514012,0.49300000000000005
Map1lc3a,1.21367696692869,0.183,0.186,0.596969063542686,-1.64936087345827,0.176,0.667,6.09548104314735e-05,0.48100000000000004
Gpr162,0.574698484171257,0.194,0.186,0.634550071802284,-1.58362203550715,0.143,0.667,9.56965124213913e-06,0.48100000000000004
Ndufa1,1.10850266667233,0.107,0.102,0.717071408850292,-1.90800951299914,0.088,0.583,1.41008911126085e-07,0.481
Cox17,0.294296057433156,0.085,0.102,0.769547369848079,-1.96520325517615,0.088,0.583,1.32352939836054e-07,0.481
Eef1g,1.40861912775505,0.303,0.271,0.102801655806966,-1.44725728462788,0.227,0.75,0.000122776969652964,0.479
Rpl34,0.595316590014059,0.252,0.271,0.671284061230795,-1.27712419558024,0.292,0.75,0.00186097722155954,0.479
2900097C17Rik,0.552937463555942,0.334,0.356,0.437200636314712,-1.4842481269794,0.298,0.833,8.3729755379005e-05,0.477
Dynll2,0.586733416687908,0.307,0.356,0.660277124026432,-1.88584903154769,0.278,0.833,4.60975696148595e-06,0.477
Rpl3,0.179147990294253,0.361,0.525,0.359874855125418,-1.21835502821256,0.408,1.0,9.18898407314444e-05,0.475
Eif5a,0.569727385076143,0.147,0.203,0.560073962383731,-2.42035847443773,0.131,0.667,1.3209704870164e-07,0.464
Nme1,0.696149424234412,0.154,0.203,0.67202798439859,-1.08670033159241,0.204,0.667,0.00135126346348659,0.464
Brk1,0.65128592474389,0.185,0.203,0.8689081420387,-1.11883751492357,0.161,0.667,0.000118353065421916,0.464
Atxn7l3b,0.895643136531756,0.265,0.288,0.516441691771034,-1.6144632077297,0.22,0.75,8.30587154937844e-05,0.462
Cd81,0.628396992360589,0.214,0.288,0.702234143267831,-1.26859537960884,0.204,0.75,8.66437961890473e-05,0.462
Ap2a1,0.320515556063042,0.229,0.373,0.232856690777229,-1.74559526909698,0.194,0.833,2.91850341329023e-06,0.45999999999999996
Lsm4,1.30850477136503,0.06,0.051,0.706632173311454,-1.77287218066938,0.071,0.5,6.31461657205826e-07,0.449
Rpl7a,0.842128900219428,0.163,0.22,0.646073870581225,-0.861591647287034,0.176,0.667,0.000561258341453647,0.44700000000000006
Ptov1,0.669057921042856,0.171,0.22,0.729435025668708,-1.35167711144316,0.149,0.667,5.64052852004996e-05,0.44700000000000006
Fam8a1,0.48271792115665,0.187,0.22,0.908019714677543,-1.60645343393011,0.147,0.667,1.62805375609762e-05,0.44700000000000006
Uqcrb,0.609235607189531,0.187,0.22,0.933134619988657,-1.63186618583614,0.198,0.667,0.000125583255965519,0.44700000000000006
Atp6v1f,1.3973166134761,0.145,0.136,0.563008623014503,-1.57295557767468,0.163,0.583,0.000555421432185117,0.44699999999999995
Rpl4,-0.520186686795131,0.122,0.305,0.00154754438579836,-2.37703711750187,0.143,0.75,3.87566522066175e-08,0.445
Rps4x,0.170844247887895,0.232,0.39,0.125147583359009,-1.53194612973929,0.257,0.833,5.38230163692621e-05,0.44299999999999995
Eif4g2,0.716343161967693,0.479,0.475,0.0837595609617951,-1.28714132962125,0.431,0.917,0.000272292087747008,0.44200000000000006
Hspa8,0.377341262273151,0.514,0.559,0.318073304077842,-1.68199137018608,0.478,1.0,7.84428450753292e-06,0.44099999999999995
1500009C09Rik,0.482258245783808,0.042,0.068,0.433622479463622,-2.30403805984352,0.067,0.5,1.20772227874026e-07,0.432
Sh3bgrl3,0.713923497838747,0.073,0.068,0.790294807091382,-1.33245855921368,0.094,0.5,3.55805948239764e-05,0.432
Med28,0.866196664276768,0.071,0.068,0.819668445869052,-2.87541831937101,0.043,0.5,1.45583090775618e-11,0.432
Stk32c,-0.336860613152645,0.096,0.237,0.00406116856252478,-1.91898268593864,0.096,0.667,1.1940347291055e-08,0.43000000000000005
Eef1b2,0.378012009042903,0.149,0.237,0.256211276381553,-1.86460454845676,0.135,0.667,2.74416787888298e-06,0.43000000000000005
Cuedc2,0.354372911741466,0.171,0.237,0.513511036223771,-1.67254375505609,0.151,0.667,1.74833953959527e-05,0.43000000000000005
Bri3bp,0.834925106518588,0.109,0.153,0.534023504911534,-1.32570267841321,0.1,0.583,3.02031264938829e-06,0.42999999999999994
Aldoc,0.573868418884917,0.252,0.322,0.899716635240336,-1.44272534499698,0.21,0.75,2.92672158674775e-05,0.428
Rpl8,1.13697829278572,0.488,0.407,0.00513218603956276,-0.653473632051217,0.449,0.833,0.0798189263868305,0.426
Rpl26,0.775872901804347,0.332,0.407,0.693348038905267,-0.479065684503611,0.396,0.833,0.0733678463212699,0.426
E130218I03Rik,-0.204257972685306,0.258,0.492,0.0152899577371268,-1.31165971779405,0.32,0.917,0.000252618269568491,0.42500000000000004
Commd1,0.712382495849941,0.069,0.085,0.776280834970001,-1.94755648388483,0.055,0.5,4.92878095107689e-09,0.415
Ndufa3,0.79388980531691,0.089,0.085,0.807934509849927,-1.61262242253079,0.084,0.5,6.61051216502385e-06,0.415
Tprgl,0.214041772887387,0.082,0.169,0.0650615312909808,-1.73818840204558,0.088,0.583,1.63388677703375e-07,0.4139999999999999
Vma21,0.216582488130818,0.089,0.169,0.106854930999606,-2.54004063681804,0.059,0.583,3.18763311965518e-11,0.4139999999999999
Pfdn6,0.310068707061878,0.131,0.169,0.647088124529709,-1.83325272230704,0.12,0.583,1.28223717688895e-05,0.4139999999999999
Cend1,0.477324448297883,0.178,0.254,0.476775205276608,-1.56851809456485,0.196,0.667,0.000234496483217043,0.41300000000000003
Ndufb9,0.692761176367048,0.198,0.254,0.786928705849671,-1.63949973405951,0.22,0.667,0.000468670258456626,0.41300000000000003
Ghitm,-0.272769263864793,0.209,0.424,0.00692021592255752,-1.59315209204928,0.208,0.833,9.45689797992108e-06,0.409
Mif,0.74421993749139,0.396,0.424,0.267712421033083,-0.860083922474261,0.418,0.833,0.00614934363259215,0.409
Rpl6,0.474663508114448,0.372,0.424,0.557352824477459,-1.25147019583906,0.324,0.833,0.00133318006897576,0.409
Rpl37a,0.452955258818751,0.339,0.424,0.898062010353726,-0.628608977292624,0.335,0.833,0.0103051284281538,0.409
Ndufs3,0.553017974343186,0.069,0.102,0.47004716267774,-3.14376636752593,0.055,0.5,1.24272228750724e-09,0.398
Fhdc1,0.653058774805392,0.111,0.102,0.717518247501955,-1.89964135470436,0.08,0.5,2.48597228340886e-06,0.398
Diras1,1.13198596557157,0.087,0.102,0.876787721481507,-1.59170354920666,0.096,0.5,3.13152700676009e-05,0.398
Serf2,0.929325061110838,0.183,0.186,0.673716423436412,-1.88745262650797,0.129,0.583,2.29340273531839e-05,0.39699999999999996
Pnmal2,0.682641970306638,0.167,0.186,0.932100964187678,-1.41556286941491,0.163,0.583,0.000628737765537838,0.39699999999999996
Ppp1r7,0.175641557118963,0.154,0.271,0.100465755599841,-1.02540811895768,0.149,0.667,5.71643464654001e-05,0.396
Fbxw2,1.14513759992783,0.294,0.271,0.192983003720504,-1.3709388754341,0.2,0.667,0.000570129385777722,0.396
Arf1,0.240689476014312,0.167,0.271,0.199572422766884,-1.24320546878807,0.153,0.667,4.09399958650382e-05,0.396
Ankrd40,0.649558585395246,0.187,0.271,0.468882283872847,-1.56186363811906,0.157,0.667,2.6957813404907e-05,0.396
Cox6b1,0.980242337612691,0.247,0.271,0.571553570057767,-1.22062722221244,0.247,0.667,0.00343594927373464,0.396
Rps16,0.68404290563041,0.214,0.271,0.882666268296752,-0.603500619959605,0.224,0.667,0.00902264462864654,0.396
Dtx3,0.165229793383,0.238,0.356,0.322302106997744,-1.54920123222027,0.167,0.75,3.32765663801228e-06,0.394
Rps29,1.1301289140846,0.461,0.441,0.0203844713073919,-1.15420541391795,0.404,0.833,0.00824315768186131,0.39199999999999996
mt-Nd4,-0.243918109659489,0.399,0.61,0.14874518822207,-1.76355709320397,0.416,1.0,4.42237702611445e-06,0.39
Mt1,2.35404358740512,0.249,0.119,0.00738304258859187,-0.225231361160738,0.267,0.5,0.282651530221086,0.381
Ndufa11,1.51973270995631,0.2,0.119,0.0739871523300409,-0.48740708005687,0.184,0.5,0.0359862411685355,0.381
Rpl13a,0.281897579800399,0.062,0.119,0.163837405698096,-2.69854464642385,0.073,0.5,2.34076853034657e-07,0.381
Degs1,0.761509610154818,0.094,0.119,0.71398208444793,-1.54118352041749,0.076,0.5,1.69348151370912e-06,0.381
Polr2f,0.956304166949369,0.116,0.119,0.864019480919111,-2.01233572902799,0.086,0.5,4.19580606153747e-06,0.381
mt-Co3,-1.23567631118791,0.053,0.203,6.78595679931762e-05,-2.84170053903702,0.067,0.583,1.73838225544892e-10,0.37999999999999995
Atp5g1,1.24030319492342,0.254,0.203,0.12759668119327,-0.88013318506464,0.2,0.583,0.00803452736686206,0.37999999999999995
Tln1,-0.123729964895425,0.12,0.203,0.155560262614591,-2.03809484114926,0.088,0.583,1.47080737568268e-07,0.37999999999999995
Uhmk1,0.566490999667568,0.151,0.203,0.6009561026714,-1.98508827245464,0.112,0.583,3.3986526106211e-06,0.37999999999999995
1 gene avg_log2fc_r1 pct.opa1_r1 pct.wt_r1 p_val_r1 avg_log2fc_r2 pct.opa1_r2 pct.wt_r2 p_val_r2 pct_diff_r2
2 Tubb3 0.79450472774753 0.109 0.119 0.958485404528225 -2.84060326306335 0.12 0.833 2.5972044241376e-12 0.714
3 Eef2 0.946852168537043 0.292 0.339 0.554692826981986 -1.61901498373035 0.306 1.0 2.06785445452351e-06 0.661
4 Tgoln1 1.78036354603182 0.225 0.153 0.0685588946189396 -1.59766881199497 0.153 0.75 1.56898706300087e-06 0.597
5 Timm13 0.383182577176802 0.138 0.153 0.960061993857616 -1.24427119609979 0.167 0.75 1.36377059109632e-05 0.597
6 Tubb4b -0.182716806279187 0.165 0.237 0.328885604097807 -2.59923429460839 0.165 0.833 3.44319082399783e-09 0.596
7 Rpl21 0.932953806641402 0.316 0.322 0.28215571866864 -1.07923839551978 0.322 0.917 0.00091167025666756 0.595
8 Atp5l 0.9465691739094 0.267 0.322 0.768526015392968 -1.8175107125187 0.218 0.917 3.74537113361768e-07 0.595
9 Uqcr11 1.08152098660872 0.194 0.169 0.353189722220523 -1.14803206707634 0.2 0.75 0.000176273116755541 0.581
10 Rpl18a 0.45455529148095 0.327 0.339 0.631994511766892 -1.49637329449574 0.369 0.917 0.000290393226658511 0.5780000000000001
11 Chchd2 0.810218166641106 0.43 0.441 0.111692922941569 -0.845993845078889 0.471 1.0 0.00388182855018752 0.5589999999999999
12 Rpl18 0.528446988169901 0.2 0.22 0.968392744036157 -1.45687981171311 0.206 0.75 4.29458701023366e-05 0.53
13 Atp5j 0.40697786154564 0.229 0.305 0.663022485930743 -1.33170456668031 0.263 0.833 0.000308766307816131 0.528
14 Atp6v1e1 0.136685146613233 0.285 0.39 0.621417622024421 -1.5876255300904 0.253 0.917 3.1740687004702e-06 0.527
15 Tceal9 0.641146250721447 0.12 0.153 0.719452982315463 -1.07142829739176 0.141 0.667 2.12341492463759e-05 0.514
16 Fabp5 1.21129255499213 0.303 0.237 0.0787348997931872 -0.795617397292901 0.302 0.75 0.01906057128027 0.513
17 Bex3 0.642072493043613 0.169 0.237 0.535146457240487 -1.21116718093423 0.21 0.75 0.000182278005944436 0.513
18 Elob 0.47742771361923 0.171 0.237 0.544913208915019 -1.30683418103432 0.184 0.75 4.37479682506387e-05 0.513
19 Tubb2a 0.717221959140293 0.185 0.237 0.76744471309425 -1.67253347444918 0.216 0.75 3.70470777766425e-05 0.513
20 Ndufb8 0.158452570666001 0.198 0.237 0.82355885360063 -1.42915572825392 0.249 0.75 0.000357593588132878 0.513
21 Tagln3 -0.278895963470218 0.156 0.322 0.0131166748085004 -1.92367781633955 0.155 0.833 2.36394342438425e-08 0.5109999999999999
22 Pomp 0.841895825583676 0.178 0.169 0.547346403162976 -1.46526550394965 0.147 0.667 2.56000174106239e-05 0.498
23 Ndufb7 0.602688557677008 0.178 0.169 0.634372817842982 -1.59858275858968 0.155 0.667 3.0226374855996e-05 0.498
24 Cyb5r3 -0.559098180896072 0.031 0.085 0.0520353149998478 -3.22184866239203 0.041 0.583 1.42053138037143e-15 0.49799999999999994
25 AC121965.1 0.416684792807243 0.185 0.254 0.5376138613364 -1.00114503302094 0.171 0.75 4.50689569736277e-05 0.496
26 Tubb5 0.546129253440581 0.194 0.254 0.654405345908873 -1.64847060479884 0.243 0.75 0.000126980493805329 0.496
27 Chchd10 0.808406780550554 0.238 0.254 0.682369719829123 -0.872957076870523 0.28 0.75 0.00465192049670251 0.496
28 Hist1h4d 0.301589308835843 0.205 0.254 0.758821714552774 -1.13005464593582 0.216 0.75 0.000507756390389973 0.496
29 Ubc 0.729385658990562 0.272 0.339 0.961486281648046 -1.89693262657494 0.235 0.833 3.32888407561134e-06 0.49399999999999994
30 Rplp1 1.07487179289198 0.499 0.424 0.00628120568658711 -0.467434255833367 0.488 0.917 0.080861982168712 0.49300000000000005
31 Rps15 1.31896514865808 0.439 0.424 0.0185070874821656 -0.720089054690377 0.406 0.917 0.00919016506514012 0.49300000000000005
32 Map1lc3a 1.21367696692869 0.183 0.186 0.596969063542686 -1.64936087345827 0.176 0.667 6.09548104314735e-05 0.48100000000000004
33 Gpr162 0.574698484171257 0.194 0.186 0.634550071802284 -1.58362203550715 0.143 0.667 9.56965124213913e-06 0.48100000000000004
34 Ndufa1 1.10850266667233 0.107 0.102 0.717071408850292 -1.90800951299914 0.088 0.583 1.41008911126085e-07 0.481
35 Cox17 0.294296057433156 0.085 0.102 0.769547369848079 -1.96520325517615 0.088 0.583 1.32352939836054e-07 0.481
36 Eef1g 1.40861912775505 0.303 0.271 0.102801655806966 -1.44725728462788 0.227 0.75 0.000122776969652964 0.479
37 Rpl34 0.595316590014059 0.252 0.271 0.671284061230795 -1.27712419558024 0.292 0.75 0.00186097722155954 0.479
38 2900097C17Rik 0.552937463555942 0.334 0.356 0.437200636314712 -1.4842481269794 0.298 0.833 8.3729755379005e-05 0.477
39 Dynll2 0.586733416687908 0.307 0.356 0.660277124026432 -1.88584903154769 0.278 0.833 4.60975696148595e-06 0.477
40 Rpl3 0.179147990294253 0.361 0.525 0.359874855125418 -1.21835502821256 0.408 1.0 9.18898407314444e-05 0.475
41 Eif5a 0.569727385076143 0.147 0.203 0.560073962383731 -2.42035847443773 0.131 0.667 1.3209704870164e-07 0.464
42 Nme1 0.696149424234412 0.154 0.203 0.67202798439859 -1.08670033159241 0.204 0.667 0.00135126346348659 0.464
43 Brk1 0.65128592474389 0.185 0.203 0.8689081420387 -1.11883751492357 0.161 0.667 0.000118353065421916 0.464
44 Atxn7l3b 0.895643136531756 0.265 0.288 0.516441691771034 -1.6144632077297 0.22 0.75 8.30587154937844e-05 0.462
45 Cd81 0.628396992360589 0.214 0.288 0.702234143267831 -1.26859537960884 0.204 0.75 8.66437961890473e-05 0.462
46 Ap2a1 0.320515556063042 0.229 0.373 0.232856690777229 -1.74559526909698 0.194 0.833 2.91850341329023e-06 0.45999999999999996
47 Lsm4 1.30850477136503 0.06 0.051 0.706632173311454 -1.77287218066938 0.071 0.5 6.31461657205826e-07 0.449
48 Rpl7a 0.842128900219428 0.163 0.22 0.646073870581225 -0.861591647287034 0.176 0.667 0.000561258341453647 0.44700000000000006
49 Ptov1 0.669057921042856 0.171 0.22 0.729435025668708 -1.35167711144316 0.149 0.667 5.64052852004996e-05 0.44700000000000006
50 Fam8a1 0.48271792115665 0.187 0.22 0.908019714677543 -1.60645343393011 0.147 0.667 1.62805375609762e-05 0.44700000000000006
51 Uqcrb 0.609235607189531 0.187 0.22 0.933134619988657 -1.63186618583614 0.198 0.667 0.000125583255965519 0.44700000000000006
52 Atp6v1f 1.3973166134761 0.145 0.136 0.563008623014503 -1.57295557767468 0.163 0.583 0.000555421432185117 0.44699999999999995
53 Rpl4 -0.520186686795131 0.122 0.305 0.00154754438579836 -2.37703711750187 0.143 0.75 3.87566522066175e-08 0.445
54 Rps4x 0.170844247887895 0.232 0.39 0.125147583359009 -1.53194612973929 0.257 0.833 5.38230163692621e-05 0.44299999999999995
55 Eif4g2 0.716343161967693 0.479 0.475 0.0837595609617951 -1.28714132962125 0.431 0.917 0.000272292087747008 0.44200000000000006
56 Hspa8 0.377341262273151 0.514 0.559 0.318073304077842 -1.68199137018608 0.478 1.0 7.84428450753292e-06 0.44099999999999995
57 1500009C09Rik 0.482258245783808 0.042 0.068 0.433622479463622 -2.30403805984352 0.067 0.5 1.20772227874026e-07 0.432
58 Sh3bgrl3 0.713923497838747 0.073 0.068 0.790294807091382 -1.33245855921368 0.094 0.5 3.55805948239764e-05 0.432
59 Med28 0.866196664276768 0.071 0.068 0.819668445869052 -2.87541831937101 0.043 0.5 1.45583090775618e-11 0.432
60 Stk32c -0.336860613152645 0.096 0.237 0.00406116856252478 -1.91898268593864 0.096 0.667 1.1940347291055e-08 0.43000000000000005
61 Eef1b2 0.378012009042903 0.149 0.237 0.256211276381553 -1.86460454845676 0.135 0.667 2.74416787888298e-06 0.43000000000000005
62 Cuedc2 0.354372911741466 0.171 0.237 0.513511036223771 -1.67254375505609 0.151 0.667 1.74833953959527e-05 0.43000000000000005
63 Bri3bp 0.834925106518588 0.109 0.153 0.534023504911534 -1.32570267841321 0.1 0.583 3.02031264938829e-06 0.42999999999999994
64 Aldoc 0.573868418884917 0.252 0.322 0.899716635240336 -1.44272534499698 0.21 0.75 2.92672158674775e-05 0.428
65 Rpl8 1.13697829278572 0.488 0.407 0.00513218603956276 -0.653473632051217 0.449 0.833 0.0798189263868305 0.426
66 Rpl26 0.775872901804347 0.332 0.407 0.693348038905267 -0.479065684503611 0.396 0.833 0.0733678463212699 0.426
67 E130218I03Rik -0.204257972685306 0.258 0.492 0.0152899577371268 -1.31165971779405 0.32 0.917 0.000252618269568491 0.42500000000000004
68 Commd1 0.712382495849941 0.069 0.085 0.776280834970001 -1.94755648388483 0.055 0.5 4.92878095107689e-09 0.415
69 Ndufa3 0.79388980531691 0.089 0.085 0.807934509849927 -1.61262242253079 0.084 0.5 6.61051216502385e-06 0.415
70 Tprgl 0.214041772887387 0.082 0.169 0.0650615312909808 -1.73818840204558 0.088 0.583 1.63388677703375e-07 0.4139999999999999
71 Vma21 0.216582488130818 0.089 0.169 0.106854930999606 -2.54004063681804 0.059 0.583 3.18763311965518e-11 0.4139999999999999
72 Pfdn6 0.310068707061878 0.131 0.169 0.647088124529709 -1.83325272230704 0.12 0.583 1.28223717688895e-05 0.4139999999999999
73 Cend1 0.477324448297883 0.178 0.254 0.476775205276608 -1.56851809456485 0.196 0.667 0.000234496483217043 0.41300000000000003
74 Ndufb9 0.692761176367048 0.198 0.254 0.786928705849671 -1.63949973405951 0.22 0.667 0.000468670258456626 0.41300000000000003
75 Ghitm -0.272769263864793 0.209 0.424 0.00692021592255752 -1.59315209204928 0.208 0.833 9.45689797992108e-06 0.409
76 Mif 0.74421993749139 0.396 0.424 0.267712421033083 -0.860083922474261 0.418 0.833 0.00614934363259215 0.409
77 Rpl6 0.474663508114448 0.372 0.424 0.557352824477459 -1.25147019583906 0.324 0.833 0.00133318006897576 0.409
78 Rpl37a 0.452955258818751 0.339 0.424 0.898062010353726 -0.628608977292624 0.335 0.833 0.0103051284281538 0.409
79 Ndufs3 0.553017974343186 0.069 0.102 0.47004716267774 -3.14376636752593 0.055 0.5 1.24272228750724e-09 0.398
80 Fhdc1 0.653058774805392 0.111 0.102 0.717518247501955 -1.89964135470436 0.08 0.5 2.48597228340886e-06 0.398
81 Diras1 1.13198596557157 0.087 0.102 0.876787721481507 -1.59170354920666 0.096 0.5 3.13152700676009e-05 0.398
82 Serf2 0.929325061110838 0.183 0.186 0.673716423436412 -1.88745262650797 0.129 0.583 2.29340273531839e-05 0.39699999999999996
83 Pnmal2 0.682641970306638 0.167 0.186 0.932100964187678 -1.41556286941491 0.163 0.583 0.000628737765537838 0.39699999999999996
84 Ppp1r7 0.175641557118963 0.154 0.271 0.100465755599841 -1.02540811895768 0.149 0.667 5.71643464654001e-05 0.396
85 Fbxw2 1.14513759992783 0.294 0.271 0.192983003720504 -1.3709388754341 0.2 0.667 0.000570129385777722 0.396
86 Arf1 0.240689476014312 0.167 0.271 0.199572422766884 -1.24320546878807 0.153 0.667 4.09399958650382e-05 0.396
87 Ankrd40 0.649558585395246 0.187 0.271 0.468882283872847 -1.56186363811906 0.157 0.667 2.6957813404907e-05 0.396
88 Cox6b1 0.980242337612691 0.247 0.271 0.571553570057767 -1.22062722221244 0.247 0.667 0.00343594927373464 0.396
89 Rps16 0.68404290563041 0.214 0.271 0.882666268296752 -0.603500619959605 0.224 0.667 0.00902264462864654 0.396
90 Dtx3 0.165229793383 0.238 0.356 0.322302106997744 -1.54920123222027 0.167 0.75 3.32765663801228e-06 0.394
91 Rps29 1.1301289140846 0.461 0.441 0.0203844713073919 -1.15420541391795 0.404 0.833 0.00824315768186131 0.39199999999999996
92 mt-Nd4 -0.243918109659489 0.399 0.61 0.14874518822207 -1.76355709320397 0.416 1.0 4.42237702611445e-06 0.39
93 Mt1 2.35404358740512 0.249 0.119 0.00738304258859187 -0.225231361160738 0.267 0.5 0.282651530221086 0.381
94 Ndufa11 1.51973270995631 0.2 0.119 0.0739871523300409 -0.48740708005687 0.184 0.5 0.0359862411685355 0.381
95 Rpl13a 0.281897579800399 0.062 0.119 0.163837405698096 -2.69854464642385 0.073 0.5 2.34076853034657e-07 0.381
96 Degs1 0.761509610154818 0.094 0.119 0.71398208444793 -1.54118352041749 0.076 0.5 1.69348151370912e-06 0.381
97 Polr2f 0.956304166949369 0.116 0.119 0.864019480919111 -2.01233572902799 0.086 0.5 4.19580606153747e-06 0.381
98 mt-Co3 -1.23567631118791 0.053 0.203 6.78595679931762e-05 -2.84170053903702 0.067 0.583 1.73838225544892e-10 0.37999999999999995
99 Atp5g1 1.24030319492342 0.254 0.203 0.12759668119327 -0.88013318506464 0.2 0.583 0.00803452736686206 0.37999999999999995
100 Tln1 -0.123729964895425 0.12 0.203 0.155560262614591 -2.03809484114926 0.088 0.583 1.47080737568268e-07 0.37999999999999995
101 Uhmk1 0.566490999667568 0.151 0.203 0.6009561026714 -1.98508827245464 0.112 0.583 3.3986526106211e-06 0.37999999999999995
output/10_rgc2_signature/genelevel_concordance_RGC2like.csv
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group,module,n_WT,median_WT,delta_S1,delta_S2
RGC2-like,ETC_WP295,7,0.1521,-0.299,-0.2565
RGC2-like,CI_biogenesis_RE,7,0.1945,-0.2833,-0.2521
RGC2-like,Glycolysis_WP157,7,0.8739,-0.7213,-0.6503
RGC2-like,Ribosomal_WP163,7,0.1636,-0.0438,-0.0636
RGC2-like,Mitophagy_Autophagy,7,0.0283,-0.0344,-0.0346
RGC1-like,ETC_WP295,62,-0.3428,0.1234,0.1073
RGC1-like,CI_biogenesis_RE,62,-0.2327,0.0886,0.081
RGC1-like,Glycolysis_WP157,62,-0.0959,0.1179,0.1532
RGC1-like,Ribosomal_WP163,62,-0.0117,0.062,0.0464
RGC1-like,Mitophagy_Autophagy,62,-0.0699,0.0237,0.0335
1 group module n_WT median_WT delta_S1 delta_S2
2 RGC2-like ETC_WP295 7 0.1521 -0.299 -0.2565
3 RGC2-like CI_biogenesis_RE 7 0.1945 -0.2833 -0.2521
4 RGC2-like Glycolysis_WP157 7 0.8739 -0.7213 -0.6503
5 RGC2-like Ribosomal_WP163 7 0.1636 -0.0438 -0.0636
6 RGC2-like Mitophagy_Autophagy 7 0.0283 -0.0344 -0.0346
7 RGC1-like ETC_WP295 62 -0.3428 0.1234 0.1073
8 RGC1-like CI_biogenesis_RE 62 -0.2327 0.0886 0.081
9 RGC1-like Glycolysis_WP157 62 -0.0959 0.1179 0.1532
10 RGC1-like Ribosomal_WP163 62 -0.0117 0.062 0.0464
11 RGC1-like Mitophagy_Autophagy 62 -0.0699 0.0237 0.0335
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output/11_pseudobulk_de/de_Amacrine.csv
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group,n_genes_tested,consistent_UP,consistent_DOWN,UP_DOWN_ratio,n_WT_nuclei,low_power,author_sheet,spearman_rho,author_sig_gene_concordance
Amacrine,11122,1345,400,3.362,184,False,Amacrine,0.505,0.683
Bipolar,11601,1105,569,1.942,106,False,Bipolar,0.538,0.789
Cone,11006,2030,959,2.117,99,False,Cone,0.948,0.994
Endothelial,14282,1767,1420,1.244,39,False,,,
LowConf,11552,904,424,2.132,294,False,,,
Muller,11929,1690,769,2.198,183,False,Muller,0.773,0.911
Oligodendrocyte,12554,2010,1342,1.498,35,False,,,
Pericyte,12033,2160,995,2.171,73,False,Pericyte,0.813,0.988
RGC1-like,11188,1243,629,1.976,62,False,RGC-1,0.829,0.988
Rod,10540,462,214,2.159,1773,False,Rod,0.667,0.954
Uveal_Melanocyte,13079,2152,1342,1.604,53,False,Uveal,0.734,0.958
Horizontal,11826,1808,1498,1.207,13,True,Horizontal,0.175,0.663
RGC2-like,13005,1748,1391,1.257,7,True,RGC-2,0.671,0.954
1 group n_genes_tested consistent_UP consistent_DOWN UP_DOWN_ratio n_WT_nuclei low_power author_sheet spearman_rho author_sig_gene_concordance
2 Amacrine 11122 1345 400 3.362 184 False Amacrine 0.505 0.683
3 Bipolar 11601 1105 569 1.942 106 False Bipolar 0.538 0.789
4 Cone 11006 2030 959 2.117 99 False Cone 0.948 0.994
5 Endothelial 14282 1767 1420 1.244 39 False
6 LowConf 11552 904 424 2.132 294 False
7 Muller 11929 1690 769 2.198 183 False Muller 0.773 0.911
8 Oligodendrocyte 12554 2010 1342 1.498 35 False
9 Pericyte 12033 2160 995 2.171 73 False Pericyte 0.813 0.988
10 RGC1-like 11188 1243 629 1.976 62 False RGC-1 0.829 0.988
11 Rod 10540 462 214 2.159 1773 False Rod 0.667 0.954
12 Uveal_Melanocyte 13079 2152 1342 1.604 53 False Uveal 0.734 0.958
13 Horizontal 11826 1808 1498 1.207 13 True Horizontal 0.175 0.663
14 RGC2-like 13005 1748 1391 1.257 7 True RGC-2 0.671 0.954
output/11_pseudobulk_de/pseudobulk_group_sizes.csv
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pb_group,Opa1V291D_S1,Opa1V291D_S2,WT
Amacrine,1065,1208,184
Astrocyte,32,58,15
Bipolar,577,849,106
Cone,584,624,99
Endothelial,178,195,39
Horizontal,137,140,13
LowConf,1257,1317,294
Microglia,57,75,8
Muller,1070,1319,183
Oligodendrocyte,195,246,35
Pericyte,334,332,73
RGC1-like,371,430,62
RGC2-like,58,83,7
Rod,4610,3256,1773
Uveal_Melanocyte,219,450,53
1 pb_group Opa1V291D_S1 Opa1V291D_S2 WT
2 Amacrine 1065 1208 184
3 Astrocyte 32 58 15
4 Bipolar 577 849 106
5 Cone 584 624 99
6 Endothelial 178 195 39
7 Horizontal 137 140 13
8 LowConf 1257 1317 294
9 Microglia 57 75 8
10 Muller 1070 1319 183
11 Oligodendrocyte 195 246 35
12 Pericyte 334 332 73
13 RGC1-like 371 430 62
14 RGC2-like 58 83 7
15 Rod 4610 3256 1773
16 Uveal_Melanocyte 219 450 53
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output/12_inflammation/inflammation_module_scores.csv
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group,module,n_WT,median_WT,median_S1,median_S2,delta_S1,delta_S2
RGC2-like,IFN_alpha_ISG,7,-0.0393,-0.0332,-0.0279,0.0062,0.0114
RGC2-like,IFN_gamma,7,-0.0765,-0.0798,-0.0651,-0.0033,0.0115
RGC2-like,IL6_JAK_STAT3,7,-0.0678,-0.0497,-0.0627,0.0181,0.0051
RGC2-like,TNFA_NFkB,7,-0.0637,-0.0063,-0.023,0.0574,0.0407
RGC2-like,Complement,7,-0.1439,-0.1111,-0.109,0.0328,0.0349
RGC2-like,cGAS_STING,7,-0.0211,-0.0656,-0.0682,-0.0446,-0.0471
RGC2-like,NLRP3_inflammasome,7,-0.0895,-0.0676,-0.08,0.0219,0.0094
RGC2-like,UPRmt,7,0.0934,0.0283,0.009,-0.0652,-0.0844
RGC2-like,ISR,7,-0.1125,0.0073,-0.0907,0.1198,0.0218
RGC2-like,Muller_reactive_gliosis,7,-0.1552,-0.057,-0.0738,0.0982,0.0814
RGC2-like,Microglia_DAM,7,-0.0366,-0.0321,-0.0122,0.0045,0.0244
RGC2-like,Microglia_homeostatic,7,-0.0052,-0.0479,-0.0451,-0.0427,-0.04
RGC1-like,IFN_alpha_ISG,62,-0.0427,-0.035,-0.0332,0.0077,0.0095
RGC1-like,IFN_gamma,62,-0.0694,-0.062,-0.0612,0.0074,0.0082
RGC1-like,IL6_JAK_STAT3,62,-0.0374,-0.0493,-0.0416,-0.012,-0.0042
RGC1-like,TNFA_NFkB,62,-0.0236,-0.027,-0.029,-0.0035,-0.0055
RGC1-like,Complement,62,-0.136,-0.1185,-0.1125,0.0175,0.0236
RGC1-like,cGAS_STING,62,0.009,-0.062,-0.0548,-0.071,-0.0638
RGC1-like,NLRP3_inflammasome,62,-0.0877,-0.0784,-0.0797,0.0093,0.0081
RGC1-like,UPRmt,62,-0.0284,-0.0322,-0.0092,-0.0038,0.0192
RGC1-like,ISR,62,-0.1292,-0.1103,-0.1143,0.0189,0.0148
RGC1-like,Muller_reactive_gliosis,62,-0.1791,-0.0867,-0.065,0.0924,0.1141
RGC1-like,Microglia_DAM,62,-0.1299,-0.0375,-0.0527,0.0924,0.0772
RGC1-like,Microglia_homeostatic,62,-0.0386,-0.0477,-0.0393,-0.009,-0.0006
Muller,IFN_alpha_ISG,183,0.0104,0.0093,0.0044,-0.001,-0.0059
Muller,IFN_gamma,183,-0.0252,-0.0286,-0.0404,-0.0034,-0.0152
Muller,IL6_JAK_STAT3,183,-0.0027,0.0092,0.008,0.0119,0.0107
Muller,TNFA_NFkB,183,-0.0042,0.0013,-0.0186,0.0055,-0.0144
Muller,Complement,183,-0.0287,0.0098,-0.0033,0.0385,0.0254
Muller,cGAS_STING,183,-0.0298,-0.0515,-0.0499,-0.0218,-0.0201
Muller,NLRP3_inflammasome,183,-0.0028,-0.0474,-0.0498,-0.0446,-0.0471
Muller,UPRmt,183,0.0009,0.0006,-0.008,-0.0003,-0.009
Muller,ISR,183,-0.1052,-0.0747,-0.0721,0.0305,0.0331
Muller,Muller_reactive_gliosis,183,0.2367,0.3839,0.306,0.1472,0.0693
Muller,Microglia_DAM,183,0.1171,0.2422,0.1803,0.125,0.0632
Muller,Microglia_homeostatic,183,-0.0246,-0.0362,-0.037,-0.0116,-0.0124
Microglia,IFN_alpha_ISG,8,0.0184,0.0205,0.0276,0.0021,0.0092
Microglia,IFN_gamma,8,0.1776,0.0245,0.0675,-0.1531,-0.1102
Microglia,IL6_JAK_STAT3,8,0.2843,0.0721,0.0665,-0.2123,-0.2178
Microglia,TNFA_NFkB,8,0.1342,0.0455,0.0516,-0.0887,-0.0826
Microglia,Complement,8,0.1235,0.1238,0.0553,0.0003,-0.0683
Microglia,cGAS_STING,8,0.086,-0.0452,-0.0389,-0.1313,-0.1249
Microglia,NLRP3_inflammasome,8,0.3213,0.1892,-0.0253,-0.1321,-0.3466
Microglia,UPRmt,8,-0.0613,-0.0245,-0.068,0.0368,-0.0068
Microglia,ISR,8,-0.1088,-0.0687,-0.0647,0.0402,0.0441
Microglia,Muller_reactive_gliosis,8,-0.1146,0.0981,0.152,0.2127,0.2666
Microglia,Microglia_DAM,8,0.1362,0.2498,0.4148,0.1136,0.2786
Microglia,Microglia_homeostatic,8,1.1461,0.8063,0.334,-0.3399,-0.8121
Astrocyte,IFN_alpha_ISG,15,0.0048,-0.0041,0.0137,-0.0089,0.0089
Astrocyte,IFN_gamma,15,-0.0145,-0.0431,-0.0416,-0.0286,-0.0271
Astrocyte,IL6_JAK_STAT3,15,-0.0097,0.0363,0.0227,0.046,0.0324
Astrocyte,TNFA_NFkB,15,-0.0083,0.0214,-0.0333,0.0297,-0.0251
Astrocyte,Complement,15,-0.0248,0.0275,0.0111,0.0523,0.0359
Astrocyte,cGAS_STING,15,-0.0248,-0.0406,-0.0507,-0.0158,-0.026
Astrocyte,NLRP3_inflammasome,15,0.0897,-0.0428,-0.0463,-0.1325,-0.1359
Astrocyte,UPRmt,15,0.0116,-0.0124,0.0515,-0.0241,0.0399
Astrocyte,ISR,15,-0.0813,-0.0629,-0.0727,0.0184,0.0087
Astrocyte,Muller_reactive_gliosis,15,0.2695,0.4174,0.3023,0.1479,0.0328
Astrocyte,Microglia_DAM,15,0.123,0.1693,0.1982,0.0464,0.0752
Astrocyte,Microglia_homeostatic,15,-0.0145,-0.0282,-0.0375,-0.0136,-0.0229
Rod,IFN_alpha_ISG,1773,-0.0105,-0.0109,-0.0126,-0.0004,-0.0021
Rod,IFN_gamma,1773,-0.0253,-0.0254,-0.0313,-0.0,-0.0059
Rod,IL6_JAK_STAT3,1773,-0.0372,-0.0385,-0.044,-0.0013,-0.0068
Rod,TNFA_NFkB,1773,-0.017,-0.0184,-0.0213,-0.0013,-0.0043
Rod,Complement,1773,-0.0318,-0.0333,-0.0337,-0.0015,-0.0019
Rod,cGAS_STING,1773,-0.0279,-0.0284,-0.0291,-0.0005,-0.0013
Rod,NLRP3_inflammasome,1773,-0.0259,-0.0258,-0.0302,0.0001,-0.0043
Rod,UPRmt,1773,-0.0424,-0.0415,-0.0538,0.0009,-0.0114
Rod,ISR,1773,-0.0374,-0.0384,-0.0456,-0.001,-0.0082
Rod,Muller_reactive_gliosis,1773,0.1412,0.1721,0.1386,0.031,-0.0026
Rod,Microglia_DAM,1773,0.1274,0.1554,0.1233,0.028,-0.0041
Rod,Microglia_homeostatic,1773,-0.0192,-0.0198,-0.0233,-0.0006,-0.0041
Cone,IFN_alpha_ISG,99,-0.0314,-0.0209,-0.0211,0.0105,0.0103
Cone,IFN_gamma,99,-0.0603,-0.0478,-0.0416,0.0125,0.0187
Cone,IL6_JAK_STAT3,99,-0.018,-0.0605,-0.0591,-0.0425,-0.0411
Cone,TNFA_NFkB,99,0.0587,0.0425,0.0443,-0.0161,-0.0144
Cone,Complement,99,-0.1006,-0.0485,-0.0634,0.0522,0.0372
Cone,cGAS_STING,99,-0.0673,-0.0481,-0.0444,0.0192,0.0229
Cone,NLRP3_inflammasome,99,0.0616,-0.0439,-0.0387,-0.1055,-0.1003
Cone,UPRmt,99,-0.0151,0.0068,-0.0778,0.0219,-0.0627
Cone,ISR,99,-0.1122,-0.0811,-0.079,0.0311,0.0332
Cone,Muller_reactive_gliosis,99,-0.1064,0.0438,0.0537,0.1503,0.1602
Cone,Microglia_DAM,99,-0.0401,0.0527,0.0486,0.0928,0.0888
Cone,Microglia_homeostatic,99,-0.054,-0.036,-0.0329,0.018,0.0212
1 group module n_WT median_WT median_S1 median_S2 delta_S1 delta_S2
2 RGC2-like IFN_alpha_ISG 7 -0.0393 -0.0332 -0.0279 0.0062 0.0114
3 RGC2-like IFN_gamma 7 -0.0765 -0.0798 -0.0651 -0.0033 0.0115
4 RGC2-like IL6_JAK_STAT3 7 -0.0678 -0.0497 -0.0627 0.0181 0.0051
5 RGC2-like TNFA_NFkB 7 -0.0637 -0.0063 -0.023 0.0574 0.0407
6 RGC2-like Complement 7 -0.1439 -0.1111 -0.109 0.0328 0.0349
7 RGC2-like cGAS_STING 7 -0.0211 -0.0656 -0.0682 -0.0446 -0.0471
8 RGC2-like NLRP3_inflammasome 7 -0.0895 -0.0676 -0.08 0.0219 0.0094
9 RGC2-like UPRmt 7 0.0934 0.0283 0.009 -0.0652 -0.0844
10 RGC2-like ISR 7 -0.1125 0.0073 -0.0907 0.1198 0.0218
11 RGC2-like Muller_reactive_gliosis 7 -0.1552 -0.057 -0.0738 0.0982 0.0814
12 RGC2-like Microglia_DAM 7 -0.0366 -0.0321 -0.0122 0.0045 0.0244
13 RGC2-like Microglia_homeostatic 7 -0.0052 -0.0479 -0.0451 -0.0427 -0.04
14 RGC1-like IFN_alpha_ISG 62 -0.0427 -0.035 -0.0332 0.0077 0.0095
15 RGC1-like IFN_gamma 62 -0.0694 -0.062 -0.0612 0.0074 0.0082
16 RGC1-like IL6_JAK_STAT3 62 -0.0374 -0.0493 -0.0416 -0.012 -0.0042
17 RGC1-like TNFA_NFkB 62 -0.0236 -0.027 -0.029 -0.0035 -0.0055
18 RGC1-like Complement 62 -0.136 -0.1185 -0.1125 0.0175 0.0236
19 RGC1-like cGAS_STING 62 0.009 -0.062 -0.0548 -0.071 -0.0638
20 RGC1-like NLRP3_inflammasome 62 -0.0877 -0.0784 -0.0797 0.0093 0.0081
21 RGC1-like UPRmt 62 -0.0284 -0.0322 -0.0092 -0.0038 0.0192
22 RGC1-like ISR 62 -0.1292 -0.1103 -0.1143 0.0189 0.0148
23 RGC1-like Muller_reactive_gliosis 62 -0.1791 -0.0867 -0.065 0.0924 0.1141
24 RGC1-like Microglia_DAM 62 -0.1299 -0.0375 -0.0527 0.0924 0.0772
25 RGC1-like Microglia_homeostatic 62 -0.0386 -0.0477 -0.0393 -0.009 -0.0006
26 Muller IFN_alpha_ISG 183 0.0104 0.0093 0.0044 -0.001 -0.0059
27 Muller IFN_gamma 183 -0.0252 -0.0286 -0.0404 -0.0034 -0.0152
28 Muller IL6_JAK_STAT3 183 -0.0027 0.0092 0.008 0.0119 0.0107
29 Muller TNFA_NFkB 183 -0.0042 0.0013 -0.0186 0.0055 -0.0144
30 Muller Complement 183 -0.0287 0.0098 -0.0033 0.0385 0.0254
31 Muller cGAS_STING 183 -0.0298 -0.0515 -0.0499 -0.0218 -0.0201
32 Muller NLRP3_inflammasome 183 -0.0028 -0.0474 -0.0498 -0.0446 -0.0471
33 Muller UPRmt 183 0.0009 0.0006 -0.008 -0.0003 -0.009
34 Muller ISR 183 -0.1052 -0.0747 -0.0721 0.0305 0.0331
35 Muller Muller_reactive_gliosis 183 0.2367 0.3839 0.306 0.1472 0.0693
36 Muller Microglia_DAM 183 0.1171 0.2422 0.1803 0.125 0.0632
37 Muller Microglia_homeostatic 183 -0.0246 -0.0362 -0.037 -0.0116 -0.0124
38 Microglia IFN_alpha_ISG 8 0.0184 0.0205 0.0276 0.0021 0.0092
39 Microglia IFN_gamma 8 0.1776 0.0245 0.0675 -0.1531 -0.1102
40 Microglia IL6_JAK_STAT3 8 0.2843 0.0721 0.0665 -0.2123 -0.2178
41 Microglia TNFA_NFkB 8 0.1342 0.0455 0.0516 -0.0887 -0.0826
42 Microglia Complement 8 0.1235 0.1238 0.0553 0.0003 -0.0683
43 Microglia cGAS_STING 8 0.086 -0.0452 -0.0389 -0.1313 -0.1249
44 Microglia NLRP3_inflammasome 8 0.3213 0.1892 -0.0253 -0.1321 -0.3466
45 Microglia UPRmt 8 -0.0613 -0.0245 -0.068 0.0368 -0.0068
46 Microglia ISR 8 -0.1088 -0.0687 -0.0647 0.0402 0.0441
47 Microglia Muller_reactive_gliosis 8 -0.1146 0.0981 0.152 0.2127 0.2666
48 Microglia Microglia_DAM 8 0.1362 0.2498 0.4148 0.1136 0.2786
49 Microglia Microglia_homeostatic 8 1.1461 0.8063 0.334 -0.3399 -0.8121
50 Astrocyte IFN_alpha_ISG 15 0.0048 -0.0041 0.0137 -0.0089 0.0089
51 Astrocyte IFN_gamma 15 -0.0145 -0.0431 -0.0416 -0.0286 -0.0271
52 Astrocyte IL6_JAK_STAT3 15 -0.0097 0.0363 0.0227 0.046 0.0324
53 Astrocyte TNFA_NFkB 15 -0.0083 0.0214 -0.0333 0.0297 -0.0251
54 Astrocyte Complement 15 -0.0248 0.0275 0.0111 0.0523 0.0359
55 Astrocyte cGAS_STING 15 -0.0248 -0.0406 -0.0507 -0.0158 -0.026
56 Astrocyte NLRP3_inflammasome 15 0.0897 -0.0428 -0.0463 -0.1325 -0.1359
57 Astrocyte UPRmt 15 0.0116 -0.0124 0.0515 -0.0241 0.0399
58 Astrocyte ISR 15 -0.0813 -0.0629 -0.0727 0.0184 0.0087
59 Astrocyte Muller_reactive_gliosis 15 0.2695 0.4174 0.3023 0.1479 0.0328
60 Astrocyte Microglia_DAM 15 0.123 0.1693 0.1982 0.0464 0.0752
61 Astrocyte Microglia_homeostatic 15 -0.0145 -0.0282 -0.0375 -0.0136 -0.0229
62 Rod IFN_alpha_ISG 1773 -0.0105 -0.0109 -0.0126 -0.0004 -0.0021
63 Rod IFN_gamma 1773 -0.0253 -0.0254 -0.0313 -0.0 -0.0059
64 Rod IL6_JAK_STAT3 1773 -0.0372 -0.0385 -0.044 -0.0013 -0.0068
65 Rod TNFA_NFkB 1773 -0.017 -0.0184 -0.0213 -0.0013 -0.0043
66 Rod Complement 1773 -0.0318 -0.0333 -0.0337 -0.0015 -0.0019
67 Rod cGAS_STING 1773 -0.0279 -0.0284 -0.0291 -0.0005 -0.0013
68 Rod NLRP3_inflammasome 1773 -0.0259 -0.0258 -0.0302 0.0001 -0.0043
69 Rod UPRmt 1773 -0.0424 -0.0415 -0.0538 0.0009 -0.0114
70 Rod ISR 1773 -0.0374 -0.0384 -0.0456 -0.001 -0.0082
71 Rod Muller_reactive_gliosis 1773 0.1412 0.1721 0.1386 0.031 -0.0026
72 Rod Microglia_DAM 1773 0.1274 0.1554 0.1233 0.028 -0.0041
73 Rod Microglia_homeostatic 1773 -0.0192 -0.0198 -0.0233 -0.0006 -0.0041
74 Cone IFN_alpha_ISG 99 -0.0314 -0.0209 -0.0211 0.0105 0.0103
75 Cone IFN_gamma 99 -0.0603 -0.0478 -0.0416 0.0125 0.0187
76 Cone IL6_JAK_STAT3 99 -0.018 -0.0605 -0.0591 -0.0425 -0.0411
77 Cone TNFA_NFkB 99 0.0587 0.0425 0.0443 -0.0161 -0.0144
78 Cone Complement 99 -0.1006 -0.0485 -0.0634 0.0522 0.0372
79 Cone cGAS_STING 99 -0.0673 -0.0481 -0.0444 0.0192 0.0229
80 Cone NLRP3_inflammasome 99 0.0616 -0.0439 -0.0387 -0.1055 -0.1003
81 Cone UPRmt 99 -0.0151 0.0068 -0.0778 0.0219 -0.0627
82 Cone ISR 99 -0.1122 -0.0811 -0.079 0.0311 0.0332
83 Cone Muller_reactive_gliosis 99 -0.1064 0.0438 0.0537 0.1503 0.1602
84 Cone Microglia_DAM 99 -0.0401 0.0527 0.0486 0.0928 0.0888
85 Cone Microglia_homeostatic 99 -0.054 -0.036 -0.0329 0.018 0.0212
output/12_inflammation/key_gene_detection_rates.csv
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group,gene,det_WT,n_WT,det_V291D,n_V291D
RGC2-like,Ifnar1,0.4286,7,0.0496,141
RGC2-like,Ifnar2,0.0,7,0.0284,141
RGC2-like,Jak1,0.4286,7,0.2411,141
RGC2-like,Jak2,0.0,7,0.1277,141
RGC2-like,Stat1,0.0,7,0.0213,141
RGC2-like,Stat2,0.1429,7,0.1844,141
RGC2-like,Stat3,0.4286,7,0.1773,141
RGC2-like,Irf7,0.0,7,0.0071,141
RGC2-like,Irf9,0.1429,7,0.078,141
RGC2-like,Isg15,0.0,7,0.0142,141
RGC2-like,Ifit1,0.0,7,0.0071,141
RGC2-like,Ifit2,0.1429,7,0.0142,141
RGC2-like,Ifit3,0.0,7,0.0071,141
RGC2-like,Mx1,0.0,7,0.0,141
RGC2-like,Tnf,0.0,7,0.0,141
RGC2-like,C3,0.0,7,0.0,141
RGC2-like,C1qa,0.0,7,0.0,141
RGC2-like,Mb21d1,0.0,7,0.0,141
RGC2-like,Tmem173,0.0,7,0.0,141
RGC2-like,Nlrp3,0.0,7,0.0,141
RGC2-like,Gfap,0.0,7,0.0,141
RGC2-like,Serpina3n,0.0,7,0.0496,141
RGC2-like,Apoe,0.2857,7,0.5461,141
RGC2-like,Lcn2,0.0,7,0.0071,141
RGC2-like,Spp1,0.0,7,0.0638,141
RGC2-like,Trem2,0.0,7,0.0,141
RGC1-like,Ifnar1,0.2742,62,0.0936,801
RGC1-like,Ifnar2,0.1452,62,0.0474,801
RGC1-like,Jak1,0.5645,62,0.2772,801
RGC1-like,Jak2,0.4677,62,0.1823,801
RGC1-like,Stat1,0.129,62,0.0449,801
RGC1-like,Stat2,0.4355,62,0.1823,801
RGC1-like,Stat3,0.4355,62,0.171,801
RGC1-like,Irf7,0.0161,62,0.005,801
RGC1-like,Irf9,0.1129,62,0.0424,801
RGC1-like,Isg15,0.0161,62,0.0062,801
RGC1-like,Ifit1,0.0,62,0.0012,801
RGC1-like,Ifit2,0.0161,62,0.0162,801
RGC1-like,Ifit3,0.0161,62,0.0062,801
RGC1-like,Mx1,0.0,62,0.0,801
RGC1-like,Tnf,0.0,62,0.0,801
RGC1-like,C3,0.0,62,0.015,801
RGC1-like,C1qa,0.0,62,0.0025,801
RGC1-like,Mb21d1,0.0,62,0.0025,801
RGC1-like,Tmem173,0.0,62,0.0,801
RGC1-like,Nlrp3,0.0,62,0.0,801
RGC1-like,Gfap,0.0,62,0.0025,801
RGC1-like,Serpina3n,0.0161,62,0.0187,801
RGC1-like,Apoe,0.3226,62,0.5381,801
RGC1-like,Lcn2,0.0,62,0.0025,801
RGC1-like,Spp1,0.0,62,0.035,801
RGC1-like,Trem2,0.0,62,0.0025,801
Muller,Ifnar1,0.0546,183,0.0473,2389
Muller,Ifnar2,0.1148,183,0.054,2389
Muller,Jak1,0.3224,183,0.1984,2389
Muller,Jak2,0.1858,183,0.085,2389
Muller,Stat1,0.0984,183,0.0448,2389
Muller,Stat2,0.1038,183,0.0603,2389
Muller,Stat3,0.2131,183,0.1436,2389
Muller,Irf7,0.0273,183,0.0113,2389
Muller,Irf9,0.0656,183,0.0343,2389
Muller,Isg15,0.0109,183,0.0117,2389
Muller,Ifit1,0.0219,183,0.0126,2389
Muller,Ifit2,0.0383,183,0.0205,2389
Muller,Ifit3,0.0328,183,0.0193,2389
Muller,Mx1,0.0,183,0.0,2389
Muller,Tnf,0.0055,183,0.0021,2389
Muller,C3,0.0,183,0.0092,2389
Muller,C1qa,0.0,183,0.0054,2389
Muller,Mb21d1,0.0,183,0.0,2389
Muller,Tmem173,0.0055,183,0.0008,2389
Muller,Nlrp3,0.0,183,0.0,2389
Muller,Gfap,0.0164,183,0.0071,2389
Muller,Serpina3n,0.0109,183,0.0092,2389
Muller,Apoe,0.9235,183,0.9439,2389
Muller,Lcn2,0.0164,183,0.0096,2389
Muller,Spp1,0.0164,183,0.0088,2389
Muller,Trem2,0.0,183,0.0029,2389
Microglia,Ifnar1,0.25,8,0.0682,132
Microglia,Ifnar2,0.375,8,0.1364,132
Microglia,Jak1,0.75,8,0.2424,132
Microglia,Jak2,0.5,8,0.0455,132
Microglia,Stat1,0.125,8,0.053,132
Microglia,Stat2,0.125,8,0.0758,132
Microglia,Stat3,0.5,8,0.1667,132
Microglia,Irf7,0.0,8,0.0303,132
Microglia,Irf9,0.125,8,0.0606,132
Microglia,Isg15,0.0,8,0.0,132
Microglia,Ifit1,0.0,8,0.0,132
Microglia,Ifit2,0.0,8,0.0152,132
Microglia,Ifit3,0.0,8,0.0152,132
Microglia,Mx1,0.0,8,0.0152,132
Microglia,Tnf,0.0,8,0.0227,132
Microglia,C3,0.0,8,0.0,132
Microglia,C1qa,0.625,8,0.4091,132
Microglia,Mb21d1,0.0,8,0.0379,132
Microglia,Tmem173,0.125,8,0.053,132
Microglia,Nlrp3,0.125,8,0.053,132
Microglia,Gfap,0.0,8,0.0,132
Microglia,Serpina3n,0.0,8,0.0,132
Microglia,Apoe,0.125,8,0.6288,132
Microglia,Lcn2,0.0,8,0.0,132
Microglia,Spp1,0.0,8,0.0227,132
Microglia,Trem2,0.25,8,0.1364,132
Astrocyte,Ifnar1,0.0667,15,0.0333,90
Astrocyte,Ifnar2,0.1333,15,0.0556,90
Astrocyte,Jak1,0.2667,15,0.1667,90
Astrocyte,Jak2,0.1333,15,0.0889,90
Astrocyte,Stat1,0.0667,15,0.0444,90
Astrocyte,Stat2,0.0667,15,0.0556,90
Astrocyte,Stat3,0.3333,15,0.2444,90
Astrocyte,Irf7,0.0667,15,0.0222,90
Astrocyte,Irf9,0.0667,15,0.0222,90
Astrocyte,Isg15,0.0667,15,0.0111,90
Astrocyte,Ifit1,0.0,15,0.0222,90
Astrocyte,Ifit2,0.1333,15,0.0222,90
Astrocyte,Ifit3,0.0667,15,0.0222,90
Astrocyte,Mx1,0.0,15,0.0,90
Astrocyte,Tnf,0.0,15,0.0,90
Astrocyte,C3,0.0,15,0.0,90
Astrocyte,C1qa,0.0,15,0.0,90
Astrocyte,Mb21d1,0.0,15,0.0,90
Astrocyte,Tmem173,0.0,15,0.0,90
Astrocyte,Nlrp3,0.0,15,0.0,90
Astrocyte,Gfap,0.4667,15,0.4444,90
Astrocyte,Serpina3n,0.1333,15,0.0222,90
Astrocyte,Apoe,0.9333,15,0.9,90
Astrocyte,Lcn2,0.0,15,0.0111,90
Astrocyte,Spp1,0.0,15,0.0111,90
Astrocyte,Trem2,0.0,15,0.0,90
Rod,Ifnar1,0.0152,1773,0.0141,7866
Rod,Ifnar2,0.0056,1773,0.0057,7866
Rod,Jak1,0.0908,1773,0.0824,7866
Rod,Jak2,0.0226,1773,0.0235,7866
Rod,Stat1,0.0113,1773,0.0136,7866
Rod,Stat2,0.0158,1773,0.0153,7866
Rod,Stat3,0.0259,1773,0.0317,7866
Rod,Irf7,0.0056,1773,0.0051,7866
Rod,Irf9,0.0045,1773,0.0055,7866
Rod,Isg15,0.0028,1773,0.0041,7866
Rod,Ifit1,0.0017,1773,0.0028,7866
Rod,Ifit2,0.0079,1773,0.0051,7866
Rod,Ifit3,0.0062,1773,0.0066,7866
Rod,Mx1,0.0,1773,0.0,7866
Rod,Tnf,0.0006,1773,0.0003,7866
Rod,C3,0.0011,1773,0.0022,7866
Rod,C1qa,0.0056,1773,0.0061,7866
Rod,Mb21d1,0.0,1773,0.0003,7866
Rod,Tmem173,0.0,1773,0.0008,7866
Rod,Nlrp3,0.0,1773,0.0001,7866
Rod,Gfap,0.0039,1773,0.0023,7866
Rod,Serpina3n,0.0062,1773,0.0057,7866
Rod,Apoe,0.4411,1773,0.5755,7866
Rod,Lcn2,0.0039,1773,0.0038,7866
Rod,Spp1,0.0085,1773,0.0131,7866
Rod,Trem2,0.0011,1773,0.0017,7866
Cone,Ifnar1,0.0101,99,0.0257,1208
Cone,Ifnar2,0.0,99,0.0058,1208
Cone,Jak1,0.1919,99,0.1399,1208
Cone,Jak2,0.2424,99,0.0712,1208
Cone,Stat1,0.101,99,0.0497,1208
Cone,Stat2,0.0808,99,0.0414,1208
Cone,Stat3,0.3131,99,0.1184,1208
Cone,Irf7,0.0,99,0.0033,1208
Cone,Irf9,0.0303,99,0.0141,1208
Cone,Isg15,0.0,99,0.0058,1208
Cone,Ifit1,0.0,99,0.0025,1208
Cone,Ifit2,0.0,99,0.0041,1208
Cone,Ifit3,0.0,99,0.0025,1208
Cone,Mx1,0.0,99,0.0,1208
Cone,Tnf,0.0,99,0.0008,1208
Cone,C3,0.0,99,0.0017,1208
Cone,C1qa,0.0101,99,0.0041,1208
Cone,Mb21d1,0.0,99,0.0017,1208
Cone,Tmem173,0.0,99,0.0,1208
Cone,Nlrp3,0.0,99,0.0,1208
Cone,Gfap,0.0,99,0.0008,1208
Cone,Serpina3n,0.0,99,0.0017,1208
Cone,Apoe,0.3535,99,0.4959,1208
Cone,Lcn2,0.0,99,0.0017,1208
Cone,Spp1,0.0101,99,0.0066,1208
Cone,Trem2,0.0,99,0.005,1208
1 group gene det_WT n_WT det_V291D n_V291D
2 RGC2-like Ifnar1 0.4286 7 0.0496 141
3 RGC2-like Ifnar2 0.0 7 0.0284 141
4 RGC2-like Jak1 0.4286 7 0.2411 141
5 RGC2-like Jak2 0.0 7 0.1277 141
6 RGC2-like Stat1 0.0 7 0.0213 141
7 RGC2-like Stat2 0.1429 7 0.1844 141
8 RGC2-like Stat3 0.4286 7 0.1773 141
9 RGC2-like Irf7 0.0 7 0.0071 141
10 RGC2-like Irf9 0.1429 7 0.078 141
11 RGC2-like Isg15 0.0 7 0.0142 141
12 RGC2-like Ifit1 0.0 7 0.0071 141
13 RGC2-like Ifit2 0.1429 7 0.0142 141
14 RGC2-like Ifit3 0.0 7 0.0071 141
15 RGC2-like Mx1 0.0 7 0.0 141
16 RGC2-like Tnf 0.0 7 0.0 141
17 RGC2-like C3 0.0 7 0.0 141
18 RGC2-like C1qa 0.0 7 0.0 141
19 RGC2-like Mb21d1 0.0 7 0.0 141
20 RGC2-like Tmem173 0.0 7 0.0 141
21 RGC2-like Nlrp3 0.0 7 0.0 141
22 RGC2-like Gfap 0.0 7 0.0 141
23 RGC2-like Serpina3n 0.0 7 0.0496 141
24 RGC2-like Apoe 0.2857 7 0.5461 141
25 RGC2-like Lcn2 0.0 7 0.0071 141
26 RGC2-like Spp1 0.0 7 0.0638 141
27 RGC2-like Trem2 0.0 7 0.0 141
28 RGC1-like Ifnar1 0.2742 62 0.0936 801
29 RGC1-like Ifnar2 0.1452 62 0.0474 801
30 RGC1-like Jak1 0.5645 62 0.2772 801
31 RGC1-like Jak2 0.4677 62 0.1823 801
32 RGC1-like Stat1 0.129 62 0.0449 801
33 RGC1-like Stat2 0.4355 62 0.1823 801
34 RGC1-like Stat3 0.4355 62 0.171 801
35 RGC1-like Irf7 0.0161 62 0.005 801
36 RGC1-like Irf9 0.1129 62 0.0424 801
37 RGC1-like Isg15 0.0161 62 0.0062 801
38 RGC1-like Ifit1 0.0 62 0.0012 801
39 RGC1-like Ifit2 0.0161 62 0.0162 801
40 RGC1-like Ifit3 0.0161 62 0.0062 801
41 RGC1-like Mx1 0.0 62 0.0 801
42 RGC1-like Tnf 0.0 62 0.0 801
43 RGC1-like C3 0.0 62 0.015 801
44 RGC1-like C1qa 0.0 62 0.0025 801
45 RGC1-like Mb21d1 0.0 62 0.0025 801
46 RGC1-like Tmem173 0.0 62 0.0 801
47 RGC1-like Nlrp3 0.0 62 0.0 801
48 RGC1-like Gfap 0.0 62 0.0025 801
49 RGC1-like Serpina3n 0.0161 62 0.0187 801
50 RGC1-like Apoe 0.3226 62 0.5381 801
51 RGC1-like Lcn2 0.0 62 0.0025 801
52 RGC1-like Spp1 0.0 62 0.035 801
53 RGC1-like Trem2 0.0 62 0.0025 801
54 Muller Ifnar1 0.0546 183 0.0473 2389
55 Muller Ifnar2 0.1148 183 0.054 2389
56 Muller Jak1 0.3224 183 0.1984 2389
57 Muller Jak2 0.1858 183 0.085 2389
58 Muller Stat1 0.0984 183 0.0448 2389
59 Muller Stat2 0.1038 183 0.0603 2389
60 Muller Stat3 0.2131 183 0.1436 2389
61 Muller Irf7 0.0273 183 0.0113 2389
62 Muller Irf9 0.0656 183 0.0343 2389
63 Muller Isg15 0.0109 183 0.0117 2389
64 Muller Ifit1 0.0219 183 0.0126 2389
65 Muller Ifit2 0.0383 183 0.0205 2389
66 Muller Ifit3 0.0328 183 0.0193 2389
67 Muller Mx1 0.0 183 0.0 2389
68 Muller Tnf 0.0055 183 0.0021 2389
69 Muller C3 0.0 183 0.0092 2389
70 Muller C1qa 0.0 183 0.0054 2389
71 Muller Mb21d1 0.0 183 0.0 2389
72 Muller Tmem173 0.0055 183 0.0008 2389
73 Muller Nlrp3 0.0 183 0.0 2389
74 Muller Gfap 0.0164 183 0.0071 2389
75 Muller Serpina3n 0.0109 183 0.0092 2389
76 Muller Apoe 0.9235 183 0.9439 2389
77 Muller Lcn2 0.0164 183 0.0096 2389
78 Muller Spp1 0.0164 183 0.0088 2389
79 Muller Trem2 0.0 183 0.0029 2389
80 Microglia Ifnar1 0.25 8 0.0682 132
81 Microglia Ifnar2 0.375 8 0.1364 132
82 Microglia Jak1 0.75 8 0.2424 132
83 Microglia Jak2 0.5 8 0.0455 132
84 Microglia Stat1 0.125 8 0.053 132
85 Microglia Stat2 0.125 8 0.0758 132
86 Microglia Stat3 0.5 8 0.1667 132
87 Microglia Irf7 0.0 8 0.0303 132
88 Microglia Irf9 0.125 8 0.0606 132
89 Microglia Isg15 0.0 8 0.0 132
90 Microglia Ifit1 0.0 8 0.0 132
91 Microglia Ifit2 0.0 8 0.0152 132
92 Microglia Ifit3 0.0 8 0.0152 132
93 Microglia Mx1 0.0 8 0.0152 132
94 Microglia Tnf 0.0 8 0.0227 132
95 Microglia C3 0.0 8 0.0 132
96 Microglia C1qa 0.625 8 0.4091 132
97 Microglia Mb21d1 0.0 8 0.0379 132
98 Microglia Tmem173 0.125 8 0.053 132
99 Microglia Nlrp3 0.125 8 0.053 132
100 Microglia Gfap 0.0 8 0.0 132
101 Microglia Serpina3n 0.0 8 0.0 132
102 Microglia Apoe 0.125 8 0.6288 132
103 Microglia Lcn2 0.0 8 0.0 132
104 Microglia Spp1 0.0 8 0.0227 132
105 Microglia Trem2 0.25 8 0.1364 132
106 Astrocyte Ifnar1 0.0667 15 0.0333 90
107 Astrocyte Ifnar2 0.1333 15 0.0556 90
108 Astrocyte Jak1 0.2667 15 0.1667 90
109 Astrocyte Jak2 0.1333 15 0.0889 90
110 Astrocyte Stat1 0.0667 15 0.0444 90
111 Astrocyte Stat2 0.0667 15 0.0556 90
112 Astrocyte Stat3 0.3333 15 0.2444 90
113 Astrocyte Irf7 0.0667 15 0.0222 90
114 Astrocyte Irf9 0.0667 15 0.0222 90
115 Astrocyte Isg15 0.0667 15 0.0111 90
116 Astrocyte Ifit1 0.0 15 0.0222 90
117 Astrocyte Ifit2 0.1333 15 0.0222 90
118 Astrocyte Ifit3 0.0667 15 0.0222 90
119 Astrocyte Mx1 0.0 15 0.0 90
120 Astrocyte Tnf 0.0 15 0.0 90
121 Astrocyte C3 0.0 15 0.0 90
122 Astrocyte C1qa 0.0 15 0.0 90
123 Astrocyte Mb21d1 0.0 15 0.0 90
124 Astrocyte Tmem173 0.0 15 0.0 90
125 Astrocyte Nlrp3 0.0 15 0.0 90
126 Astrocyte Gfap 0.4667 15 0.4444 90
127 Astrocyte Serpina3n 0.1333 15 0.0222 90
128 Astrocyte Apoe 0.9333 15 0.9 90
129 Astrocyte Lcn2 0.0 15 0.0111 90
130 Astrocyte Spp1 0.0 15 0.0111 90
131 Astrocyte Trem2 0.0 15 0.0 90
132 Rod Ifnar1 0.0152 1773 0.0141 7866
133 Rod Ifnar2 0.0056 1773 0.0057 7866
134 Rod Jak1 0.0908 1773 0.0824 7866
135 Rod Jak2 0.0226 1773 0.0235 7866
136 Rod Stat1 0.0113 1773 0.0136 7866
137 Rod Stat2 0.0158 1773 0.0153 7866
138 Rod Stat3 0.0259 1773 0.0317 7866
139 Rod Irf7 0.0056 1773 0.0051 7866
140 Rod Irf9 0.0045 1773 0.0055 7866
141 Rod Isg15 0.0028 1773 0.0041 7866
142 Rod Ifit1 0.0017 1773 0.0028 7866
143 Rod Ifit2 0.0079 1773 0.0051 7866
144 Rod Ifit3 0.0062 1773 0.0066 7866
145 Rod Mx1 0.0 1773 0.0 7866
146 Rod Tnf 0.0006 1773 0.0003 7866
147 Rod C3 0.0011 1773 0.0022 7866
148 Rod C1qa 0.0056 1773 0.0061 7866
149 Rod Mb21d1 0.0 1773 0.0003 7866
150 Rod Tmem173 0.0 1773 0.0008 7866
151 Rod Nlrp3 0.0 1773 0.0001 7866
152 Rod Gfap 0.0039 1773 0.0023 7866
153 Rod Serpina3n 0.0062 1773 0.0057 7866
154 Rod Apoe 0.4411 1773 0.5755 7866
155 Rod Lcn2 0.0039 1773 0.0038 7866
156 Rod Spp1 0.0085 1773 0.0131 7866
157 Rod Trem2 0.0011 1773 0.0017 7866
158 Cone Ifnar1 0.0101 99 0.0257 1208
159 Cone Ifnar2 0.0 99 0.0058 1208
160 Cone Jak1 0.1919 99 0.1399 1208
161 Cone Jak2 0.2424 99 0.0712 1208
162 Cone Stat1 0.101 99 0.0497 1208
163 Cone Stat2 0.0808 99 0.0414 1208
164 Cone Stat3 0.3131 99 0.1184 1208
165 Cone Irf7 0.0 99 0.0033 1208
166 Cone Irf9 0.0303 99 0.0141 1208
167 Cone Isg15 0.0 99 0.0058 1208
168 Cone Ifit1 0.0 99 0.0025 1208
169 Cone Ifit2 0.0 99 0.0041 1208
170 Cone Ifit3 0.0 99 0.0025 1208
171 Cone Mx1 0.0 99 0.0 1208
172 Cone Tnf 0.0 99 0.0008 1208
173 Cone C3 0.0 99 0.0017 1208
174 Cone C1qa 0.0101 99 0.0041 1208
175 Cone Mb21d1 0.0 99 0.0017 1208
176 Cone Tmem173 0.0 99 0.0 1208
177 Cone Nlrp3 0.0 99 0.0 1208
178 Cone Gfap 0.0 99 0.0008 1208
179 Cone Serpina3n 0.0 99 0.0017 1208
180 Cone Apoe 0.3535 99 0.4959 1208
181 Cone Lcn2 0.0 99 0.0017 1208
182 Cone Spp1 0.0101 99 0.0066 1208
183 Cone Trem2 0.0 99 0.005 1208
output/13_isg_check/isg_genelevel_rgc.csv
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group,gene,present,our_lfc_S1,our_lfc_S2,our_lfc_null,consistent,cpm_WT,author_log2fc,author_p,bg_median_lfc
RGC2-like,Isg15,False,,,,,,,,
RGC2-like,Ifit1,False,,,,,,,,
RGC2-like,Ifit2,True,-2.569,-1.032,1.537,ns,15.3,-4.137,5.43917027056696e-06,0.178
RGC2-like,Ifit3,False,,,,,,,,
RGC2-like,Ifit3b,False,,,,,,,,
RGC2-like,Ifi27,True,-0.543,-0.917,-0.374,DOWN,30.6,-1.668,0.0125143913168659,0.178
RGC2-like,Ifi27l2a,False,,,,,,,,
RGC2-like,Ifi44,False,,,,,,,,
RGC2-like,Ifi47,False,,,,,,,,
RGC2-like,Ifitm1,False,,,,,,,,
RGC2-like,Ifitm2,True,-2.569,-1.605,0.964,ns,15.3,-1.833,0.119639393310113,0.178
RGC2-like,Ifitm3,False,,,,,,,,
RGC2-like,Mx1,False,,,,,,,,
RGC2-like,Mx2,False,,,,,,,,
RGC2-like,Oas1a,False,,,,,,,,
RGC2-like,Oas1b,True,2.546,0.964,-1.582,ns,0.0,,,0.178
RGC2-like,Oas2,False,,,,,,,,
RGC2-like,Oasl1,False,,,,,,,,
RGC2-like,Oasl2,False,,,,,,,,
RGC2-like,Rsad2,False,,,,,,,,
RGC2-like,Bst2,False,,,,,,,,
RGC2-like,Usp18,False,,,,,,,,
RGC2-like,Irf7,False,,,,,,,,
RGC2-like,Irf9,True,-0.022,0.688,0.71,ns,15.3,-0.329,0.498719017681588,0.178
RGC2-like,Stat1,False,,,,,,,,
RGC2-like,Stat2,True,1.53,1.754,0.224,UP,15.3,0.218,0.97053153007984,0.178
RGC2-like,Ddx58,False,,,,,,,,
RGC2-like,Ifih1,False,,,,,,,,
RGC2-like,Ddx60,False,,,,,,,,
RGC2-like,Cmpk2,True,-3.982,-2.445,1.537,ns,45.9,-4.197,2.27481513069604e-12,0.178
RGC2-like,Nlrc5,False,,,,,,,,
RGC2-like,Psmb8,False,,,,,,,,
RGC2-like,Psmb9,False,,,,,,,,
RGC2-like,Tap1,False,,,,,,,,
RGC2-like,Tap2,True,-0.489,-2.569,-2.08,ns,15.3,-1.814,0.12163034585952,0.178
RGC2-like,B2m,False,,,,,,,,
RGC2-like,Gbp2,False,,,,,,,,
RGC2-like,Gbp3,False,,,,,,,,
RGC2-like,Gbp4,False,,,,,,,,
RGC2-like,Gbp5,False,,,,,,,,
RGC2-like,Irgm1,False,,,,,,,,
RGC2-like,Trim21,False,,,,,,,,
RGC2-like,Xaf1,False,,,,,,,,
RGC2-like,Ifi35,True,0.0,2.524,2.524,ns,0.0,-2.066,0.147167712953075,0.178
RGC2-like,Parp9,True,1.386,2.264,0.878,ns,0.0,-1.116,0.680655332431346,0.178
RGC2-like,Parp14,False,,,,,,,,
RGC2-like,Samd9l,False,,,,,,,,
RGC2-like,Eif2ak2,True,2.08,3.105,1.025,ns,0.0,-0.554,0.586237491834889,0.178
RGC2-like,Zbp1,False,,,,,,,,
RGC2-like,Herc6,False,,,,,,,,
RGC2-like,Rtp4,True,-2.569,-2.569,0.0,ns,15.3,-3.859,1.11407029200155e-05,0.178
RGC2-like,Lgals3bp,False,,,,,,,,
RGC2-like,Ccl5,False,,,,,,,,
RGC1-like,Isg15,False,,,,,,,,
RGC1-like,Ifit1,False,,,,,,,,
RGC1-like,Ifit2,False,,,,,,,,
RGC1-like,Ifit3,False,,,,,,,,
RGC1-like,Ifit3b,False,,,,,,,,
RGC1-like,Ifi27,True,0.47,1.1,0.629,ns,4.5,0.362,0.728539464624236,0.001
RGC1-like,Ifi27l2a,False,,,,,,,,
RGC1-like,Ifi44,False,,,,,,,,
RGC1-like,Ifi47,False,,,,,,,,
RGC1-like,Ifitm1,False,,,,,,,,
RGC1-like,Ifitm2,False,,,,,,,,
RGC1-like,Ifitm3,False,,,,,,,,
RGC1-like,Mx1,False,,,,,,,,
RGC1-like,Mx2,False,,,,,,,,
RGC1-like,Oas1a,False,,,,,,,,
RGC1-like,Oas1b,False,,,,,,,,
RGC1-like,Oas2,False,,,,,,,,
RGC1-like,Oasl1,False,,,,,,,,
RGC1-like,Oasl2,False,,,,,,,,
RGC1-like,Rsad2,False,,,,,,,,
RGC1-like,Bst2,False,,,,,,,,
RGC1-like,Usp18,False,,,,,,,,
RGC1-like,Irf7,False,,,,,,,,
RGC1-like,Irf9,True,-0.644,0.285,0.929,ns,10.2,-0.685,0.0622457067944726,0.001
RGC1-like,Stat1,True,-0.267,0.215,0.482,ns,9.1,-0.896,0.00376800017352199,0.001
RGC1-like,Stat2,True,0.208,0.11,-0.098,ns,37.5,,,0.001
RGC1-like,Ddx58,False,,,,,,,,
RGC1-like,Ifih1,False,,,,,,,,
RGC1-like,Ddx60,False,,,,,,,,
RGC1-like,Cmpk2,False,,,,,,,,
RGC1-like,Nlrc5,False,,,,,,,,
RGC1-like,Psmb8,False,,,,,,,,
RGC1-like,Psmb9,False,,,,,,,,
RGC1-like,Tap1,False,,,,,,,,
RGC1-like,Tap2,False,,,,,,,,
RGC1-like,B2m,False,,,,,,,,
RGC1-like,Gbp2,False,,,,,,,,
RGC1-like,Gbp3,False,,,,,,,,
RGC1-like,Gbp4,False,,,,,,,,
RGC1-like,Gbp5,False,,,,,,,,
RGC1-like,Irgm1,False,,,,,,,,
RGC1-like,Trim21,False,,,,,,,,
RGC1-like,Xaf1,False,,,,,,,,
RGC1-like,Ifi35,False,,,,,,,,
RGC1-like,Parp9,False,,,,,,,,
RGC1-like,Parp14,False,,,,,,,,
RGC1-like,Samd9l,False,,,,,,,,
RGC1-like,Eif2ak2,False,,,,,,,,
RGC1-like,Zbp1,False,,,,,,,,
RGC1-like,Herc6,False,,,,,,,,
RGC1-like,Rtp4,False,,,,,,,,
RGC1-like,Lgals3bp,False,,,,,,,,
RGC1-like,Ccl5,False,,,,,,,,
1 group gene present our_lfc_S1 our_lfc_S2 our_lfc_null consistent cpm_WT author_log2fc author_p bg_median_lfc
2 RGC2-like Isg15 False
3 RGC2-like Ifit1 False
4 RGC2-like Ifit2 True -2.569 -1.032 1.537 ns 15.3 -4.137 5.43917027056696e-06 0.178
5 RGC2-like Ifit3 False
6 RGC2-like Ifit3b False
7 RGC2-like Ifi27 True -0.543 -0.917 -0.374 DOWN 30.6 -1.668 0.0125143913168659 0.178
8 RGC2-like Ifi27l2a False
9 RGC2-like Ifi44 False
10 RGC2-like Ifi47 False
11 RGC2-like Ifitm1 False
12 RGC2-like Ifitm2 True -2.569 -1.605 0.964 ns 15.3 -1.833 0.119639393310113 0.178
13 RGC2-like Ifitm3 False
14 RGC2-like Mx1 False
15 RGC2-like Mx2 False
16 RGC2-like Oas1a False
17 RGC2-like Oas1b True 2.546 0.964 -1.582 ns 0.0 0.178
18 RGC2-like Oas2 False
19 RGC2-like Oasl1 False
20 RGC2-like Oasl2 False
21 RGC2-like Rsad2 False
22 RGC2-like Bst2 False
23 RGC2-like Usp18 False
24 RGC2-like Irf7 False
25 RGC2-like Irf9 True -0.022 0.688 0.71 ns 15.3 -0.329 0.498719017681588 0.178
26 RGC2-like Stat1 False
27 RGC2-like Stat2 True 1.53 1.754 0.224 UP 15.3 0.218 0.97053153007984 0.178
28 RGC2-like Ddx58 False
29 RGC2-like Ifih1 False
30 RGC2-like Ddx60 False
31 RGC2-like Cmpk2 True -3.982 -2.445 1.537 ns 45.9 -4.197 2.27481513069604e-12 0.178
32 RGC2-like Nlrc5 False
33 RGC2-like Psmb8 False
34 RGC2-like Psmb9 False
35 RGC2-like Tap1 False
36 RGC2-like Tap2 True -0.489 -2.569 -2.08 ns 15.3 -1.814 0.12163034585952 0.178
37 RGC2-like B2m False
38 RGC2-like Gbp2 False
39 RGC2-like Gbp3 False
40 RGC2-like Gbp4 False
41 RGC2-like Gbp5 False
42 RGC2-like Irgm1 False
43 RGC2-like Trim21 False
44 RGC2-like Xaf1 False
45 RGC2-like Ifi35 True 0.0 2.524 2.524 ns 0.0 -2.066 0.147167712953075 0.178
46 RGC2-like Parp9 True 1.386 2.264 0.878 ns 0.0 -1.116 0.680655332431346 0.178
47 RGC2-like Parp14 False
48 RGC2-like Samd9l False
49 RGC2-like Eif2ak2 True 2.08 3.105 1.025 ns 0.0 -0.554 0.586237491834889 0.178
50 RGC2-like Zbp1 False
51 RGC2-like Herc6 False
52 RGC2-like Rtp4 True -2.569 -2.569 0.0 ns 15.3 -3.859 1.11407029200155e-05 0.178
53 RGC2-like Lgals3bp False
54 RGC2-like Ccl5 False
55 RGC1-like Isg15 False
56 RGC1-like Ifit1 False
57 RGC1-like Ifit2 False
58 RGC1-like Ifit3 False
59 RGC1-like Ifit3b False
60 RGC1-like Ifi27 True 0.47 1.1 0.629 ns 4.5 0.362 0.728539464624236 0.001
61 RGC1-like Ifi27l2a False
62 RGC1-like Ifi44 False
63 RGC1-like Ifi47 False
64 RGC1-like Ifitm1 False
65 RGC1-like Ifitm2 False
66 RGC1-like Ifitm3 False
67 RGC1-like Mx1 False
68 RGC1-like Mx2 False
69 RGC1-like Oas1a False
70 RGC1-like Oas1b False
71 RGC1-like Oas2 False
72 RGC1-like Oasl1 False
73 RGC1-like Oasl2 False
74 RGC1-like Rsad2 False
75 RGC1-like Bst2 False
76 RGC1-like Usp18 False
77 RGC1-like Irf7 False
78 RGC1-like Irf9 True -0.644 0.285 0.929 ns 10.2 -0.685 0.0622457067944726 0.001
79 RGC1-like Stat1 True -0.267 0.215 0.482 ns 9.1 -0.896 0.00376800017352199 0.001
80 RGC1-like Stat2 True 0.208 0.11 -0.098 ns 37.5 0.001
81 RGC1-like Ddx58 False
82 RGC1-like Ifih1 False
83 RGC1-like Ddx60 False
84 RGC1-like Cmpk2 False
85 RGC1-like Nlrc5 False
86 RGC1-like Psmb8 False
87 RGC1-like Psmb9 False
88 RGC1-like Tap1 False
89 RGC1-like Tap2 False
90 RGC1-like B2m False
91 RGC1-like Gbp2 False
92 RGC1-like Gbp3 False
93 RGC1-like Gbp4 False
94 RGC1-like Gbp5 False
95 RGC1-like Irgm1 False
96 RGC1-like Trim21 False
97 RGC1-like Xaf1 False
98 RGC1-like Ifi35 False
99 RGC1-like Parp9 False
100 RGC1-like Parp14 False
101 RGC1-like Samd9l False
102 RGC1-like Eif2ak2 False
103 RGC1-like Zbp1 False
104 RGC1-like Herc6 False
105 RGC1-like Rtp4 False
106 RGC1-like Lgals3bp False
107 RGC1-like Ccl5 False
output/13_isg_check/isg_vs_background.png
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output/13_isg_check/isg_vs_background_stats.csv
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group,n_ISG,ISG_median,bg_median,HK_median,ISG_less_than_bg_p,HK_vs_bg_p
RGC2-like,12,-0.199,0.178,-0.501,1.61e-01,9.71e-03
RGC1-like,4,0.066,0.001,0.22,7.31e-01,1.13e-01
1 group n_ISG ISG_median bg_median HK_median ISG_less_than_bg_p HK_vs_bg_p
2 RGC2-like 12 -0.199 0.178 -0.501 1.61e-01 9.71e-03
3 RGC1-like 4 0.066 0.001 0.22 7.31e-01 1.13e-01
output/14_global_shift/global_shift.png
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output/14_global_shift/housekeeping_module_scores.csv
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group,n_WT,HK_median_WT,delta_S1,delta_S2
Rod,1773,0.4691,-0.0156,-0.1215
Cone,99,0.0192,0.1156,0.1019
Bipolar,106,-0.0266,0.0661,0.0735
Amacrine,184,-0.1117,0.1114,0.0995
Muller,183,0.027,0.0735,0.0679
RGC1-like,62,-0.1157,0.0547,0.0599
RGC2-like,7,0.3539,-0.2954,-0.2454
Microglia,8,0.1352,0.1854,0.1061
Pericyte,73,-0.0049,0.1286,0.1027
Oligodendrocyte,35,-0.0647,0.0679,0.1047
1 group n_WT HK_median_WT delta_S1 delta_S2
2 Rod 1773 0.4691 -0.0156 -0.1215
3 Cone 99 0.0192 0.1156 0.1019
4 Bipolar 106 -0.0266 0.0661 0.0735
5 Amacrine 184 -0.1117 0.1114 0.0995
6 Muller 183 0.027 0.0735 0.0679
7 RGC1-like 62 -0.1157 0.0547 0.0599
8 RGC2-like 7 0.3539 -0.2954 -0.2454
9 Microglia 8 0.1352 0.1854 0.1061
10 Pericyte 73 -0.0049 0.1286 0.1027
11 Oligodendrocyte 35 -0.0647 0.0679 0.1047
output/14_global_shift/pernucleus_transcript_load.csv
+11
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@@ -0,0 +1,11 @@
group,V291D,WT,ratio_V291D_WT,counts_V291D,counts_WT
Amacrine,1635.0,3113.5,0.525,2745.0,7457.0
Bipolar,1228.0,2307.0,0.532,1958.0,5083.0
Cone,949.0,1866.0,0.509,1358.0,3398.0
Microglia,809.0,1499.5,0.54,1065.5,2376.0
Muller,1118.0,1944.0,0.575,1664.0,3633.0
Oligodendrocyte,1358.0,2542.0,0.534,2158.0,5517.0
Pericyte,1246.0,2252.0,0.553,1919.0,4485.0
RGC1-like,2326.0,4493.5,0.518,4440.0,12300.0
RGC2-like,1936.0,3899.0,0.497,3300.0,10197.0
Rod,442.0,407.0,1.086,623.0,669.0
1 group V291D WT ratio_V291D_WT counts_V291D counts_WT
2 Amacrine 1635.0 3113.5 0.525 2745.0 7457.0
3 Bipolar 1228.0 2307.0 0.532 1958.0 5083.0
4 Cone 949.0 1866.0 0.509 1358.0 3398.0
5 Microglia 809.0 1499.5 0.54 1065.5 2376.0
6 Muller 1118.0 1944.0 0.575 1664.0 3633.0
7 Oligodendrocyte 1358.0 2542.0 0.534 2158.0 5517.0
8 Pericyte 1246.0 2252.0 0.553 1919.0 4485.0
9 RGC1-like 2326.0 4493.5 0.518 4440.0 12300.0
10 RGC2-like 1936.0 3899.0 0.497 3300.0 10197.0
11 Rod 442.0 407.0 1.086 623.0 669.0
output/16_gsea/gsea_Muller_Hallmark.csv
+51
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@@ -0,0 +1,51 @@
Name,Term,ES,NES,NOM p-val,FDR q-val,FWER p-val,Tag %,Gene %,Lead_genes
prerank,TNF-alpha Signaling via NF-kB,-0.48652809585459444,-2.0870123461387995,0.0,0.0014927601134497683,0.001,27/119,7.15%,Phlda1;Gch1;Ier3;Icam1;Plk2;Nr4a1;Gadd45a;Il1a;Tnf;Rel;Serpinb8;Fosl2;Rhob;Sik1;Bhlhe40;Hbegf;Junb;Ptpre;Ehd1;Map2k3;Ripk2;Cebpd;Dusp5;Birc3;Cd44;Trib1;Tank
prerank,Oxidative Phosphorylation,0.49964240940325544,1.9266059589415796,0.0,0.002458882028304464,0.003,74/169,24.06%,Timm8b;Hsd17b10;Ndufs6;Timm10;Ndufs2;Mrpl34;Ndufb5;Ndufv1;Cox4i1;Mrpl11;Surf1;Cyc1;Uqcr10;Ndufb2;Cox7a2l;Ndufa9;Uqcr11;Polr2f;Timm13;Cox6c;Ndufs7;Tomm22;Oat;Uqcrh;Ndufb8;Atp6v1f;Ndufb3;Cyb5a;Cox17;Ndufb7;Ldha;Atp6v0b;Cox6b1;Ndufa8;Uqcrq;Ndufa2;Slc25a4;Uqcrfs1;Cox5b;Prdx3;Ndufs8;Ndufa7;Ndufa3;Grpel1;Ndufa5;Atp1b1;Cox7c;Suclg1;Ech1;Cox7b;Mrpl35;Cox8a;Mrps12;Ndufa1;Ndufb4;Ndufc2;Eci1;Uqcrb;Acadvl;Slc25a5;Mdh1;Ndufc1;Vdac2;Bckdha;Ndufab1;Acaa2;Vdac1;Gpx4;Mdh2;Got2;Cox7a2;Ldhb;Slc25a12;Fdx1
prerank,Myc Targets V1,0.4716435950002281,1.8345013932087526,0.0,0.0028686956996885416,0.007,83/172,29.36%,Pold2;Txnl4a;Kpna2;Snrpb2;Pcbp1;Ranbp1;Rack1;Snrpd2;Cyc1;Ran;Rps2;Snrpa;Odc1;Hspe1;Prdx4;Rpl18;Snrpd1;Nme1;Rps3;Eef1b2;Ppm1g;Hsp90ab1;Psmb3;Npm1;Rpl22;Bub3;Rps5;C1qbp;Ldha;Ppia;Psma7;Ncbp1;Prdx3;Set;Rsl1d1;Rpl34;Eif3d;Nolc1;Dut;Rpl6;Mrpl23;Psmd7;Eif3b;Psmd3;Mrps18b;Cct5;Psma4;Cct3;Hnrnpa1;Gspt1;Rpl14;Tcp1;Cct7;Snrpd3;Rad23b;Ndufab1;Ywhaq;Psma6;Trim28;Vdac1;Srsf3;Hspd1;Glo1;Ywhae;Got2;Mrpl9;Stard7;Pgk1;Psmc4;Pcna;Cox5a;Cnbp;Eif4g2;Eif4a1;Sf3b3;Lsm7;Hnrnpd;Canx;Rps10;Vdac3;Clns1a;U2af1;Serbp1
prerank,Inflammatory Response,-0.3970909218965643,-1.636138093862084,0.0,0.06269592476489028,0.081,18/90,10.02%,Rtp4;Gch1;Icam1;Il1a;Ifitm1;Lyn;Ly6e;Hbegf;Ptpre;Irf7;Ripk2;Scarf1;Tnfrsf1b;P2rx4;Sri;Rgs16;Sgms2;Il15
prerank,IL-2/STAT5 Signaling,-0.36412046676323784,-1.5538362854976608,0.0,0.08060904612628751,0.151,30/120,11.79%,Phlda1;Gsto1;Capg;Emp1;Ecm1;Smpdl3a;Rhob;Pnp;Coch;Bhlhe40;Itih5;Bcl2;Tnfrsf1b;Nfkbiz;Penk;P2rx4;Dcps;Lrrc8c;Cd44;Twsg1;Tiam1;Tnfrsf21;Spry4;Prkch;Plpp1;Rgs16;Muc1;Hopx;Pim1;Pdcd2l
prerank,mTORC1 Signaling,0.39042677156121824,1.5181269058511115,0.002551020408163265,0.11693350090159006,0.346,68/162,28.36%,Ddit3;Asns;Abcf2;M6pr;Sc5d;Tuba4a;Got1;Serpinh1;Sytl2;Phgdh;Hmgcr;Hspa5;Rpn1;Psme3;Fkbp2;Dhcr24;Psmc2;Hspe1;Ykt6;Xbp1;Hmbs;Arpc5l;Ppa1;Hmgcs1;Ldha;Rdh11;Vldlr;Ppia;Aldoa;Ebp;Pgm1;Cacybp;Gapdh;Igfbp5;Mllt11;Acly;Cct6a;Actr3;Sord;Psmd13;Ak4;Hsp90b1;Psmb5;Psma4;Stip1;Nupr1;Edem1;Fads1;Slc37a4;Insig1;Hspd1;Pik3r3;Pgk1;Psmc4;Calr;Tpi1;Dhcr7;Fdxr;Pdk1;Tubg1;Mthfd2;Lgmn;Psat1;Qdpr;Hk2;Canx;Gclc;Slc7a11
prerank,Apical Surface,-0.4741446461238155,-1.4712794221138739,0.0453257790368272,0.11494252873563218,0.259,5/24,5.05%,Mal;Atp8b1;Lyn;Crybg1;Ephb4
prerank,Pancreas Beta Cells,0.5156907814751736,1.4125308654379802,0.08018867924528301,0.24322441396644992,0.675,8/21,14.03%,Pcsk2;Mafb;Chga;Scgn;Isl1;Srprb;Neurod1;Syt13
prerank,Unfolded Protein Response,0.38661012153405777,1.3957373520280076,0.028150134048257374,0.22933173050652972,0.744,31/93,22.12%,Hyou1;Asns;Exosc4;Tubb2a;Atf4;Hspa5;Rps14;Gosr2;Dkc1;Eif4a3;Nfyb;Shc1;Xbp1;Yif1a;Npm1;Srprb;Herpud1;Pdia5;Wfs1;Nolc1;Eef2;Atp6v0d1;Hsp90b1;Lsm4;Dcp2;Edem1;Cxxc1;Eif4a2;Dctn1;Ifit1;Ern1
prerank,Pperoxisome,0.3961085292754223,1.3873079674341582,0.048846675712347354,0.20545325392055078,0.766,25/74,22.29%,Pex11a;Dhcr24;Nudt19;Hras;Pex6;Rdh11;Cln6;Pex11b;Ercc1;Pex13;Abcb4;Slc25a4;Fdps;Crat;Itgb1bp1;Ech1;Ywhah;Abcd2;Scp2;Aldh9a1;Fads1;Sod1;Pex5;Msh2;Slc25a19
prerank,Apoptosis,-0.30474906508695104,-1.2898032744721968,0.04291845493562232,0.3155694879832811,0.641,14/115,6.59%,Rnasel;Bik;Gch1;Ier3;Emp1;Gadd45a;Il1a;Tnf;Rhob;Birc3;Satb1;Gpx3;Lgals3;Cd44
prerank,Cholesterol Homeostasis,0.376823691980162,1.2477046267607663,0.15022091310751104,0.5276058295019007,0.984,20/53,19.47%,Gnai1;Cxcl16;Jag1;Sc5d;Hmgcr;Sema3b;Fabp5;Pnrc1;Pmvk;Hmgcs1;Fdft1;Ebp;Fdps;Clu;Ech1;Gusb;Lss;Aldoc;Pcyt2;Acss2
prerank,Interferon Alpha Response,-0.3358184776454081,-1.245015543324601,0.10877192982456141,0.34632034632034636,0.748,15/60,10.02%,Rtp4;Ifitm1;Psme2;Ly6e;Irf7;Ripk2;Psmb8;Isg15;Lpar6;Parp9;Lap3;Cmtr1;Psme1;Cnp;Il15
prerank,Allograft Rejection,-0.32022178458500483,-1.2384071795978397,0.12111801242236025,0.31305312093489435,0.758,7/64,3.29%,Capg;Icam1;Tnf;Ly75;Lyn;Irf7;Ripk2
prerank,KRAS Signaling Dn,0.3489009685806082,1.2173414537211482,0.15159944367176634,0.5604201956177258,0.995,11/78,6.51%,Krt15;Rsad2;P2rx6;Tfap2b;Clstn3;Klhdc8a;Adra2c;Ypel1;Cacna1f;Sncb;Pde6b
prerank,Apical Junction,0.32439346778999084,1.209472152895255,0.142668428005284,0.5263828934666595,0.997,24/127,13.75%,Amigo2;Gnai1;Thbs3;Wnk4;Nrxn2;Atp1a3;Vcam1;Map4k2;Cx3cl1;Amigo1;Zyx;Dhx16;Shc1;Calb2;Cadm3;Hras;Nlgn3;Pkd1;Lima1;Mdk;Nectin1;Fscn1;Crat;Hadh
prerank,PI3K/AKT/mTOR Signaling,0.3386726873417797,1.207274642244992,0.149519890260631,0.4798918091907546,0.997,26/85,26.39%,Ddit3;Adcy2;Akt1;Arhgdia;Mknk2;Ywhab;Akt1s1;Hras;Pla2g12a;Gngt1;Cfl1;Ecsit;Actr3;Ap2m1;Smad2;Hsp90b1;Pin1;Ralb;Arf1;Pik3r3;Traf2;Calr;Atf1;Tsc2;Cdkn1b;Pdk1
prerank,Adipogenesis,0.3103336011002346,1.2036701731093677,0.1354679802955665,0.44565373973603034,0.997,52/166,22.80%,Sult1a1;Acads;Ddt;Apoe;Cyc1;Uqcr10;Uqcr11;Sparcl1;Ndufb7;Chchd10;Pfkfb3;Tkt;Aldoa;Uqcrq;Cd151;Cd302;Jagn1;Reep6;Pgm1;Prdx3;Cd36;Agpat3;Crat;Hadh;Grpel1;Ndufa5;Dgat1;Suclg1;Ech1;Cox7b;Acly;Cox8a;Fzd4;Elmod3;Aplp2;Itga7;Scp2;Slc19a1;Sod1;Riok3;Bckdha;Ndufab1;Coq9;Arl4a;Gphn;Uck1;Acaa2;Dhrs7;Ubc;Gpx4;Mdh2;Gpat4
prerank,Bile Acid Metabolism,0.32422523142678733,1.1264256218871074,0.2492836676217765,0.6421097207802852,1.0,19/69,22.90%,Pex11a;Pxmp2;Pex16;Bmp6;Dhcr24;Pfkm;Pex6;Pex13;Rxra;Bcar3;Abcd2;Optn;Pecr;Scp2;Aldh9a1;Fads1;Sod1;Abca4;Slc29a1
prerank,UV Response Up,0.2966677785269094,1.0961046610037177,0.2777017783857729,0.6931525389533149,1.0,28/105,18.58%,Cck;Cyb5r1;Dnajb1;Asns;Sult1a1;Mapk8ip2;Eno2;Tuba4a;Rpn1;Hspa13;Ykt6;Atp6v1f;Polg2;Aldoa;Slc25a4;Grpel1;Bsg;Dgat1;Nfkbia;Grina;Dnaja1;Cdc34;Mrpl23;Creg1;Furin;Psmc3;Olfm1;Stip1
prerank,Wnt-beta Catenin Signaling,0.3767847453960982,1.093928483905241,0.3344155844155844,0.650549930917124,1.0,3/28,3.29%,Gnai1;Jag1;Hdac11
prerank,DNA Repair,0.29348715283550625,1.0925436304506497,0.2871927554980595,0.6112234304136386,1.0,37/124,23.07%,Bola2;Guk1;Polr2j;Polr2i;Eif1b;Vps28;Surf1;Nt5c;Dad1;Mpg;Polr2f;Nme1;Rfc3;Polr2c;Aprt;Cox17;Edf1;Ercc1;Pde6g;Usp11;Cant1;Ak1;Rbx1;Dut;Polr2e;Ercc5;Polr1c;Mrpl40;Tmed2;Rae1;Taf10;Pom121;Gpx4;Gtf2h5;Ak3;Nelfcd;Gsdme
prerank,Fatty Acid Metabolism,0.2918656522908477,1.0827618425113716,0.31620553359683795,0.6008892956669034,1.0,34/115,22.45%,Hsd17b10;Alad;Acads;Eno2;Uros;Mif;Lgals1;Dhcr24;Odc1;Bphl;Hmgcs1;Hsp90aa1;Ldha;Rdh11;Aldoa;Mcee;Reep6;Ncaph2;Cd36;Crat;Hadh;Suclg1;Ech1;Ywhah;Eci1;Acadvl;Auh;Mdh1;Gabarapl1;Aldh9a1;Grhpr;Acaa2;Ephx1;Mdh2
prerank,Interferon Gamma Response,-0.26040720366549586,-1.0716068902924418,0.3067226890756303,0.7659725332139125,0.978,15/111,7.04%,Rtp4;Gch1;Icam1;Pnp;Psme2;Ly6e;Irf7;Cd274;Ripk2;Psmb8;Isg15;Lap3;Cmtr1;Sri;Psme1
prerank,KRAS Signaling Up,-0.24696967558582184,-1.0505122251081898,0.3333333333333333,0.7694349073659418,0.983,17/106,10.19%,Emp1;Tspan1;Plvap;Hbegf;Galnt3;Psmb8;Tnfrsf1b;Prdm1;Birc3;Satb1;Trib1;Mall;Etv5;Rgs16;Id2;Spry2;Dusp6
prerank,E2F Targets,0.2779511019359184,1.0336016773287777,0.3733509234828496,0.7128347213427746,1.0,43/115,30.16%,Pold2;Kpna2;Ranbp1;Jpt1;Kif22;Naa38;Ran;Lyar;Prdx4;Nme1;Rfc3;Hus1;Tipin;Anp32e;Lmnb1;Hmgb2;Nolc1;Dut;Suv39h1;Orc6;Exosc8;Gspt1;Rfc1;Phf5a;Msh2;Snrpb;Psip1;Stmn1;Smc4;Pcna;Ak2;Cdkn1b;Pnn;Tubg1;Asf1a;Mthfd2;Hnrnpd;Smc1a;Nup107;Pms2;Rpa1;Ssrp1;Pop7
prerank,Myogenesis,0.28333017535848953,1.0288886806852364,0.4122340425531915,0.6883503633681219,1.0,29/98,21.03%,Camk2b;Ckb;Gadd45b;Fabp3;Pfkm;Bin1;Pde4dip;Cacna1h;Ctf1;Fdps;Cd36;Cryab;Bag1;Crat;Chrnb1;Clu;Ak1;Sgcd;Agrn;Gsn;Itga7;Sparc;Igfbp7;Gaa;Ptp4a3;Prnp;Eif4a2;Flii;Plxnb2
prerank,Estrogen Response Early,0.2684396631373968,1.0205378693482765,0.43483870967741933,0.6757611749717445,1.0,25/131,15.64%,Krt15;Cyp26b1;Pex11a;Krt19;Isg20l2;Mreg;Sema3b;Adcy1;Unc119;Podxl;Calb2;Xbp1;Mlph;Sh3bp5;Fdft1;Elovl2;Kazn;Dlc1;Bag1;Slc16a1;Sec14l2;Wfs1;Cant1;Syt12;Klf10
prerank,Complement,0.26655404998480436,1.0050717833384573,0.45257452574525747,0.6830774274629802,1.0,25/118,16.09%,Gngt2;Hpcal4;Kcnip3;Msrb1;Calm3;Hspa5;Plat;Dyrk2;Mt3;Serping1;Cdk5r1;Mmp15;Calm1;Plek;Dgkg;Ctsl;L3mbtl4;Prdm4;Cd36;Clu;Ctsb;Gng2;Rce1;Ppp4c;Ltf
prerank,Glycolysis,0.26477258671521586,1.0041578154539257,0.4641025641025641,0.6527746337046375,1.0,53/139,29.47%,Nol3;Galk1;Pgm2;Eno2;Got1;Mif;Hspa5;Tgfa;Cyb5a;Pkm;Ldha;Cacna1h;Vldlr;Cln6;Pgam1;Ppia;Hax1;Aldoa;Paxip1;Adora2b;B3gat1;Ndufv3;Glce;Irs2;Gusb;Agrn;Ak4;Mdh1;Aldh9a1;Sod1;Slc37a4;Chst2;Mdh2;Ak3;Got2;Copb2;Pgk1;Stmn1;Lhx9;Psmc4;Tpi1;Me2;Idua;Lhpp;Dsc2;Pygl;Ext2;Hk2;P4ha2;Gclc;Fkbp4;Vcan;Sdc3
prerank,heme Metabolism,0.25413406081576373,0.9765203693270862,0.5057766367137355,0.6945224056310882,1.0,20/143,15.06%,Ank1;Asns;Alas2;Alad;Ackr1;Add2;Uros;Ncoa4;Hagh;Slc30a1;Hmbs;Prdx2;Arl2bp;Cdr2;Slc25a38;Rad23a;Bsg;Vezf1;Ctsb;Tyr
prerank,Estrogen Response Late,-0.2237607098494398,-0.9578922324713801,0.5495867768595041,1.0,0.997,31/111,19.84%,Krt13;Tjp3;Prss23;Bcl2;Hr;Frk;Mettl3;Mapk13;Cd44;Tiam1;Papss2;Id2;Myof;Atp2b4;Pdcd4;Etfb;Idh2;Pdlim3;Slc22a5;Dnajc1;Cav1;Tob1;Hmgcs2;Tsta3;Slc9a3r1;Snx10;Slc24a3;Btg3;St6galnac2;Dnajc12;Emp2
prerank,p53 Pathway,0.25053730693159376,0.9534951964260899,0.5473145780051151,0.7224480486929913,1.0,35/130,18.98%,Ddit3;Cdkn2aip;Rack1;Upp1;Prkab1;S100a4;Aen;Mknk2;Hint1;Zmat3;Tgfa;Rpl18;Hras;Nol8;Cdk5r1;Ei24;Jun;Tcn2;Rrp8;Rpl36;Pmm1;Ctsf;Ak1;Epha2;Slc3a2;Rxra;Dnttip2;Acvr1b;Mxd1;Rps12;Ercc5;Ctsd;Ip6k2;Nupr1;Cd81
prerank,Mitotic Spindle,-0.21097370166091295,-0.9333716185248818,0.6682692307692307,1.0,0.998,70/157,32.98%,Brca2;Katnb1;Katna1;Mid1ip1;Cdc42ep1;Tubd1;Tiam1;Tubgcp5;Tubgcp3;Rasa2;Fgd6;Palld;Dock4;Vcl;Alms1;Tbcd;Mid1;Ranbp9;Cd2ap;Synpo;Hdac6;Cdk5rap2;Rasal2;Mark4;Ezr;Shroom2;Tsc1;Pdlim5;Arhgef3;Wasl;Taok2;Arfgef1;Sorbs2;Map1s;Kif3b;Pafah1b1;Nin;Wasf2;Cttn;Nf1;Ssh2;Clasp1;Itsn1;Trio;Sass6;Pcgf5;Capzb;Nedd9;Map3k11;Rasa1;Cdc42;Dst;Cntrl;Ralbp1;Myo1e;Bcl2l11;Pxn;Abi1;Hook3;Tlk1;Kif5b;Fgd4;Pcm1;Arhgef11;Flnb;Arhgef12;Notch2;Rhot2;Tubgcp2;Kptn
prerank,Myc Targets V2,0.30643125809523586,0.931223332857805,0.5837037037037037,0.746987869515327,1.0,10/36,19.05%,Pes1;Hspe1;Bysl;Npm1;Ipo4;Imp4;Nolc1;Sord;Tfb2m;Slc19a1
prerank,Hypoxia,-0.21554093131008698,-0.9300488828773158,0.6209677419354839,1.0,0.998,22/128,14.70%,Plac8;Ier3;Fosl2;Pgf;Anxa2;Bhlhe40;Gck;Bcl2;Ccng2;Gys1;Klf7;Pygm;Atp7a;Tpst2;Ankzf1;Ugp2;Pim1;Tiparp;Ndst1;Nr3c1;Cav1;Noct
prerank,Epithelial Mesenchymal Transition,0.24552779243135303,0.9020088567477812,0.6503957783641161,0.7817605595322659,1.0,13/111,7.43%,Col1a2;Efemp2;Dpysl3;Col8a2;Eno2;Vcam1;Sfrp1;Gadd45b;Serpinh1;Lgals1;Scg2;Aplp1;Colgalt1
prerank,Xenobiotic Metabolism,-0.20943592202044836,-0.8780743437079516,0.7848605577689243,1.0,1.0,19/114,14.43%,Gch1;Gsto1;Aldh3a1;Hes6;Lpin2;Ddah2;Papss2;Hsd17b2;Gsr;Smox;Id2;Cdo1;Pros1;Ccl25;Alas1;Slc35b1;Slc6a12;Mpp2;Cbr1
prerank,Coagulation,-0.2228080859438533,-0.8453862265041424,0.7484662576687117,1.0,1.0,11/61,11.22%,Dusp14;Prss23;A2m;Arf4;Klf7;Dpp4;Capn5;Pros1;Dusp6;Iscu;Gng12
prerank,G2-M Checkpoint,0.22112127868494666,0.8329988615115979,0.7966751918158568,0.901180263373586,1.0,24/122,20.94%,Kpna2;Jpt1;Kif22;Prmt5;Dkc1;Odc1;Cdc7;Snrpd1;Hus1;Hmgn2;Bub3;Upf1;Lmnb1;Hspa8;Chmp1a;Ncl;Nolc1;E2f3;Suv39h1;Orc6;Marcks;Gspt1;Nusap1;Rad23b
prerank,IL-6/JAK/STAT3 Signaling,-0.23835706231542095,-0.830650626613949,0.7395209580838323,1.0,1.0,6/42,7.29%,Tnf;Bak1;Tnfrsf1b;A2m;Cd44;Tnfrsf21
prerank,Androgen Response,0.22575006379262688,0.7998700950145465,0.8477970627503337,0.9302770340418556,1.0,18/83,24.65%,Mak;Krt19;Akt1;Hmgcr;Ncoa4;Dhcr24;Elk4;Hmgcs1;Uap1;Sord;Dbi;Ptpn21;Fads1;Insig1;Herc3;Inpp4b;Tsc22d1;Abcc4
prerank,UV Response Dn,-0.18615944812518,-0.799777346166142,0.8653846153846154,1.0,1.0,50/119,33.36%,Rnd3;Anxa2;Bhlhe40;Pparg;Sri;Col11a1;Rasa2;Atp2b4;Nipbl;Nr3c1;Cav1;Mapk14;Slc7a1;Akt3;Vav2;Sipa1l1;Pdlim5;Lpar1;Lamc1;Plcb4;Dusp1;Grk5;Kcnma1;Met;Fyn;Atp2b1;Mta1;Amph;F3;Nek7;Celf2;Cited2;Nfkb1;Apbb2;Pten;Igf1r;Gja1;Atrx;Map2k5;Scaf8;Phf3;Pdgfrb;Sfmbt1;Mmp16;Prdm2;Notch2;Magi2;Bmpr1a;Ythdc1;Cdk13
prerank,Protein Secretion,0.22045852813665734,0.7872276219921365,0.863031914893617,0.9177484446595424,1.0,37/89,36.47%,M6pr;Ergic3;Gosr2;Ykt6;Anp32e;Ap2m1;Ap1g1;Igf2r;Ica1;Rer1;Sod1;Stx7;Krt18;Tmed2;Rab14;Tmx1;Scrn1;Arf1;Copb2;Bnip3;Ap2s1;Sec22b;Stx16;Clta;Vamp3;Cd63;Arfgap3;Yipf6;Lamp2;Atp1a1;Sh3gl2;Scamp1;Tpd52;Vps45;Cltc;Tmed10;Abca1
prerank,Angiogenesis,-0.27428141084925045,-0.7670751317832225,0.7878787878787878,1.0,1.0,4/17,15.44%,Pglyrp1;Tnfrsf21;Vav2;Vegfa
prerank,Reactive Oxygen Species Pathway,-0.22922053339466036,-0.7658627660860788,0.8409785932721713,1.0,1.0,6/37,8.12%,Junb;Mbp;Txnrd1;Gpx3;Sbno2;Gsr
prerank,Hedgehog Signaling,0.2589699613946461,0.7358574022294828,0.8303303303303303,0.9515025559183689,1.0,3/26,11.07%,Scg2;Cdk5r1;Vldlr
prerank,Notch Signaling,-0.23588243860578473,-0.735161642724472,0.8547008547008547,0.9879714961384654,1.0,14/27,32.76%,Lfng;Notch3;Cul1;Kat2a;Fzd7;Arrb1;Hes1;Tcf7l2;Fbxw11;Ccnd1;Notch1;Dtx1;Maml2;Notch2
prerank,Spermatogenesis,0.17115998246638217,0.5636172630269956,0.9915848527349228,0.9948636686519863,1.0,7/59,14.21%,Gsg1;Gad1;Pcsk1n;Tsn;Pebp1;Snap91;Coil
prerank,TGF-beta Signaling,-0.12918116358539997,-0.4566951529981553,1.0,0.9992536199432751,1.0,8/46,21.76%,Junb;Id2;Tgfbr1;Arid4b;Smurf2;Fnta;Wwtr1;Bmpr2
1 Name Term ES NES NOM p-val FDR q-val FWER p-val Tag % Gene % Lead_genes
2 prerank TNF-alpha Signaling via NF-kB -0.48652809585459444 -2.0870123461387995 0.0 0.0014927601134497683 0.001 27/119 7.15% Phlda1;Gch1;Ier3;Icam1;Plk2;Nr4a1;Gadd45a;Il1a;Tnf;Rel;Serpinb8;Fosl2;Rhob;Sik1;Bhlhe40;Hbegf;Junb;Ptpre;Ehd1;Map2k3;Ripk2;Cebpd;Dusp5;Birc3;Cd44;Trib1;Tank
3 prerank Oxidative Phosphorylation 0.49964240940325544 1.9266059589415796 0.0 0.002458882028304464 0.003 74/169 24.06% Timm8b;Hsd17b10;Ndufs6;Timm10;Ndufs2;Mrpl34;Ndufb5;Ndufv1;Cox4i1;Mrpl11;Surf1;Cyc1;Uqcr10;Ndufb2;Cox7a2l;Ndufa9;Uqcr11;Polr2f;Timm13;Cox6c;Ndufs7;Tomm22;Oat;Uqcrh;Ndufb8;Atp6v1f;Ndufb3;Cyb5a;Cox17;Ndufb7;Ldha;Atp6v0b;Cox6b1;Ndufa8;Uqcrq;Ndufa2;Slc25a4;Uqcrfs1;Cox5b;Prdx3;Ndufs8;Ndufa7;Ndufa3;Grpel1;Ndufa5;Atp1b1;Cox7c;Suclg1;Ech1;Cox7b;Mrpl35;Cox8a;Mrps12;Ndufa1;Ndufb4;Ndufc2;Eci1;Uqcrb;Acadvl;Slc25a5;Mdh1;Ndufc1;Vdac2;Bckdha;Ndufab1;Acaa2;Vdac1;Gpx4;Mdh2;Got2;Cox7a2;Ldhb;Slc25a12;Fdx1
4 prerank Myc Targets V1 0.4716435950002281 1.8345013932087526 0.0 0.0028686956996885416 0.007 83/172 29.36% Pold2;Txnl4a;Kpna2;Snrpb2;Pcbp1;Ranbp1;Rack1;Snrpd2;Cyc1;Ran;Rps2;Snrpa;Odc1;Hspe1;Prdx4;Rpl18;Snrpd1;Nme1;Rps3;Eef1b2;Ppm1g;Hsp90ab1;Psmb3;Npm1;Rpl22;Bub3;Rps5;C1qbp;Ldha;Ppia;Psma7;Ncbp1;Prdx3;Set;Rsl1d1;Rpl34;Eif3d;Nolc1;Dut;Rpl6;Mrpl23;Psmd7;Eif3b;Psmd3;Mrps18b;Cct5;Psma4;Cct3;Hnrnpa1;Gspt1;Rpl14;Tcp1;Cct7;Snrpd3;Rad23b;Ndufab1;Ywhaq;Psma6;Trim28;Vdac1;Srsf3;Hspd1;Glo1;Ywhae;Got2;Mrpl9;Stard7;Pgk1;Psmc4;Pcna;Cox5a;Cnbp;Eif4g2;Eif4a1;Sf3b3;Lsm7;Hnrnpd;Canx;Rps10;Vdac3;Clns1a;U2af1;Serbp1
5 prerank Inflammatory Response -0.3970909218965643 -1.636138093862084 0.0 0.06269592476489028 0.081 18/90 10.02% Rtp4;Gch1;Icam1;Il1a;Ifitm1;Lyn;Ly6e;Hbegf;Ptpre;Irf7;Ripk2;Scarf1;Tnfrsf1b;P2rx4;Sri;Rgs16;Sgms2;Il15
6 prerank IL-2/STAT5 Signaling -0.36412046676323784 -1.5538362854976608 0.0 0.08060904612628751 0.151 30/120 11.79% Phlda1;Gsto1;Capg;Emp1;Ecm1;Smpdl3a;Rhob;Pnp;Coch;Bhlhe40;Itih5;Bcl2;Tnfrsf1b;Nfkbiz;Penk;P2rx4;Dcps;Lrrc8c;Cd44;Twsg1;Tiam1;Tnfrsf21;Spry4;Prkch;Plpp1;Rgs16;Muc1;Hopx;Pim1;Pdcd2l
7 prerank mTORC1 Signaling 0.39042677156121824 1.5181269058511115 0.002551020408163265 0.11693350090159006 0.346 68/162 28.36% Ddit3;Asns;Abcf2;M6pr;Sc5d;Tuba4a;Got1;Serpinh1;Sytl2;Phgdh;Hmgcr;Hspa5;Rpn1;Psme3;Fkbp2;Dhcr24;Psmc2;Hspe1;Ykt6;Xbp1;Hmbs;Arpc5l;Ppa1;Hmgcs1;Ldha;Rdh11;Vldlr;Ppia;Aldoa;Ebp;Pgm1;Cacybp;Gapdh;Igfbp5;Mllt11;Acly;Cct6a;Actr3;Sord;Psmd13;Ak4;Hsp90b1;Psmb5;Psma4;Stip1;Nupr1;Edem1;Fads1;Slc37a4;Insig1;Hspd1;Pik3r3;Pgk1;Psmc4;Calr;Tpi1;Dhcr7;Fdxr;Pdk1;Tubg1;Mthfd2;Lgmn;Psat1;Qdpr;Hk2;Canx;Gclc;Slc7a11
8 prerank Apical Surface -0.4741446461238155 -1.4712794221138739 0.0453257790368272 0.11494252873563218 0.259 5/24 5.05% Mal;Atp8b1;Lyn;Crybg1;Ephb4
9 prerank Pancreas Beta Cells 0.5156907814751736 1.4125308654379802 0.08018867924528301 0.24322441396644992 0.675 8/21 14.03% Pcsk2;Mafb;Chga;Scgn;Isl1;Srprb;Neurod1;Syt13
10 prerank Unfolded Protein Response 0.38661012153405777 1.3957373520280076 0.028150134048257374 0.22933173050652972 0.744 31/93 22.12% Hyou1;Asns;Exosc4;Tubb2a;Atf4;Hspa5;Rps14;Gosr2;Dkc1;Eif4a3;Nfyb;Shc1;Xbp1;Yif1a;Npm1;Srprb;Herpud1;Pdia5;Wfs1;Nolc1;Eef2;Atp6v0d1;Hsp90b1;Lsm4;Dcp2;Edem1;Cxxc1;Eif4a2;Dctn1;Ifit1;Ern1
11 prerank Pperoxisome 0.3961085292754223 1.3873079674341582 0.048846675712347354 0.20545325392055078 0.766 25/74 22.29% Pex11a;Dhcr24;Nudt19;Hras;Pex6;Rdh11;Cln6;Pex11b;Ercc1;Pex13;Abcb4;Slc25a4;Fdps;Crat;Itgb1bp1;Ech1;Ywhah;Abcd2;Scp2;Aldh9a1;Fads1;Sod1;Pex5;Msh2;Slc25a19
12 prerank Apoptosis -0.30474906508695104 -1.2898032744721968 0.04291845493562232 0.3155694879832811 0.641 14/115 6.59% Rnasel;Bik;Gch1;Ier3;Emp1;Gadd45a;Il1a;Tnf;Rhob;Birc3;Satb1;Gpx3;Lgals3;Cd44
13 prerank Cholesterol Homeostasis 0.376823691980162 1.2477046267607663 0.15022091310751104 0.5276058295019007 0.984 20/53 19.47% Gnai1;Cxcl16;Jag1;Sc5d;Hmgcr;Sema3b;Fabp5;Pnrc1;Pmvk;Hmgcs1;Fdft1;Ebp;Fdps;Clu;Ech1;Gusb;Lss;Aldoc;Pcyt2;Acss2
14 prerank Interferon Alpha Response -0.3358184776454081 -1.245015543324601 0.10877192982456141 0.34632034632034636 0.748 15/60 10.02% Rtp4;Ifitm1;Psme2;Ly6e;Irf7;Ripk2;Psmb8;Isg15;Lpar6;Parp9;Lap3;Cmtr1;Psme1;Cnp;Il15
15 prerank Allograft Rejection -0.32022178458500483 -1.2384071795978397 0.12111801242236025 0.31305312093489435 0.758 7/64 3.29% Capg;Icam1;Tnf;Ly75;Lyn;Irf7;Ripk2
16 prerank KRAS Signaling Dn 0.3489009685806082 1.2173414537211482 0.15159944367176634 0.5604201956177258 0.995 11/78 6.51% Krt15;Rsad2;P2rx6;Tfap2b;Clstn3;Klhdc8a;Adra2c;Ypel1;Cacna1f;Sncb;Pde6b
17 prerank Apical Junction 0.32439346778999084 1.209472152895255 0.142668428005284 0.5263828934666595 0.997 24/127 13.75% Amigo2;Gnai1;Thbs3;Wnk4;Nrxn2;Atp1a3;Vcam1;Map4k2;Cx3cl1;Amigo1;Zyx;Dhx16;Shc1;Calb2;Cadm3;Hras;Nlgn3;Pkd1;Lima1;Mdk;Nectin1;Fscn1;Crat;Hadh
18 prerank PI3K/AKT/mTOR Signaling 0.3386726873417797 1.207274642244992 0.149519890260631 0.4798918091907546 0.997 26/85 26.39% Ddit3;Adcy2;Akt1;Arhgdia;Mknk2;Ywhab;Akt1s1;Hras;Pla2g12a;Gngt1;Cfl1;Ecsit;Actr3;Ap2m1;Smad2;Hsp90b1;Pin1;Ralb;Arf1;Pik3r3;Traf2;Calr;Atf1;Tsc2;Cdkn1b;Pdk1
19 prerank Adipogenesis 0.3103336011002346 1.2036701731093677 0.1354679802955665 0.44565373973603034 0.997 52/166 22.80% Sult1a1;Acads;Ddt;Apoe;Cyc1;Uqcr10;Uqcr11;Sparcl1;Ndufb7;Chchd10;Pfkfb3;Tkt;Aldoa;Uqcrq;Cd151;Cd302;Jagn1;Reep6;Pgm1;Prdx3;Cd36;Agpat3;Crat;Hadh;Grpel1;Ndufa5;Dgat1;Suclg1;Ech1;Cox7b;Acly;Cox8a;Fzd4;Elmod3;Aplp2;Itga7;Scp2;Slc19a1;Sod1;Riok3;Bckdha;Ndufab1;Coq9;Arl4a;Gphn;Uck1;Acaa2;Dhrs7;Ubc;Gpx4;Mdh2;Gpat4
20 prerank Bile Acid Metabolism 0.32422523142678733 1.1264256218871074 0.2492836676217765 0.6421097207802852 1.0 19/69 22.90% Pex11a;Pxmp2;Pex16;Bmp6;Dhcr24;Pfkm;Pex6;Pex13;Rxra;Bcar3;Abcd2;Optn;Pecr;Scp2;Aldh9a1;Fads1;Sod1;Abca4;Slc29a1
21 prerank UV Response Up 0.2966677785269094 1.0961046610037177 0.2777017783857729 0.6931525389533149 1.0 28/105 18.58% Cck;Cyb5r1;Dnajb1;Asns;Sult1a1;Mapk8ip2;Eno2;Tuba4a;Rpn1;Hspa13;Ykt6;Atp6v1f;Polg2;Aldoa;Slc25a4;Grpel1;Bsg;Dgat1;Nfkbia;Grina;Dnaja1;Cdc34;Mrpl23;Creg1;Furin;Psmc3;Olfm1;Stip1
22 prerank Wnt-beta Catenin Signaling 0.3767847453960982 1.093928483905241 0.3344155844155844 0.650549930917124 1.0 3/28 3.29% Gnai1;Jag1;Hdac11
23 prerank DNA Repair 0.29348715283550625 1.0925436304506497 0.2871927554980595 0.6112234304136386 1.0 37/124 23.07% Bola2;Guk1;Polr2j;Polr2i;Eif1b;Vps28;Surf1;Nt5c;Dad1;Mpg;Polr2f;Nme1;Rfc3;Polr2c;Aprt;Cox17;Edf1;Ercc1;Pde6g;Usp11;Cant1;Ak1;Rbx1;Dut;Polr2e;Ercc5;Polr1c;Mrpl40;Tmed2;Rae1;Taf10;Pom121;Gpx4;Gtf2h5;Ak3;Nelfcd;Gsdme
24 prerank Fatty Acid Metabolism 0.2918656522908477 1.0827618425113716 0.31620553359683795 0.6008892956669034 1.0 34/115 22.45% Hsd17b10;Alad;Acads;Eno2;Uros;Mif;Lgals1;Dhcr24;Odc1;Bphl;Hmgcs1;Hsp90aa1;Ldha;Rdh11;Aldoa;Mcee;Reep6;Ncaph2;Cd36;Crat;Hadh;Suclg1;Ech1;Ywhah;Eci1;Acadvl;Auh;Mdh1;Gabarapl1;Aldh9a1;Grhpr;Acaa2;Ephx1;Mdh2
25 prerank Interferon Gamma Response -0.26040720366549586 -1.0716068902924418 0.3067226890756303 0.7659725332139125 0.978 15/111 7.04% Rtp4;Gch1;Icam1;Pnp;Psme2;Ly6e;Irf7;Cd274;Ripk2;Psmb8;Isg15;Lap3;Cmtr1;Sri;Psme1
26 prerank KRAS Signaling Up -0.24696967558582184 -1.0505122251081898 0.3333333333333333 0.7694349073659418 0.983 17/106 10.19% Emp1;Tspan1;Plvap;Hbegf;Galnt3;Psmb8;Tnfrsf1b;Prdm1;Birc3;Satb1;Trib1;Mall;Etv5;Rgs16;Id2;Spry2;Dusp6
27 prerank E2F Targets 0.2779511019359184 1.0336016773287777 0.3733509234828496 0.7128347213427746 1.0 43/115 30.16% Pold2;Kpna2;Ranbp1;Jpt1;Kif22;Naa38;Ran;Lyar;Prdx4;Nme1;Rfc3;Hus1;Tipin;Anp32e;Lmnb1;Hmgb2;Nolc1;Dut;Suv39h1;Orc6;Exosc8;Gspt1;Rfc1;Phf5a;Msh2;Snrpb;Psip1;Stmn1;Smc4;Pcna;Ak2;Cdkn1b;Pnn;Tubg1;Asf1a;Mthfd2;Hnrnpd;Smc1a;Nup107;Pms2;Rpa1;Ssrp1;Pop7
28 prerank Myogenesis 0.28333017535848953 1.0288886806852364 0.4122340425531915 0.6883503633681219 1.0 29/98 21.03% Camk2b;Ckb;Gadd45b;Fabp3;Pfkm;Bin1;Pde4dip;Cacna1h;Ctf1;Fdps;Cd36;Cryab;Bag1;Crat;Chrnb1;Clu;Ak1;Sgcd;Agrn;Gsn;Itga7;Sparc;Igfbp7;Gaa;Ptp4a3;Prnp;Eif4a2;Flii;Plxnb2
29 prerank Estrogen Response Early 0.2684396631373968 1.0205378693482765 0.43483870967741933 0.6757611749717445 1.0 25/131 15.64% Krt15;Cyp26b1;Pex11a;Krt19;Isg20l2;Mreg;Sema3b;Adcy1;Unc119;Podxl;Calb2;Xbp1;Mlph;Sh3bp5;Fdft1;Elovl2;Kazn;Dlc1;Bag1;Slc16a1;Sec14l2;Wfs1;Cant1;Syt12;Klf10
30 prerank Complement 0.26655404998480436 1.0050717833384573 0.45257452574525747 0.6830774274629802 1.0 25/118 16.09% Gngt2;Hpcal4;Kcnip3;Msrb1;Calm3;Hspa5;Plat;Dyrk2;Mt3;Serping1;Cdk5r1;Mmp15;Calm1;Plek;Dgkg;Ctsl;L3mbtl4;Prdm4;Cd36;Clu;Ctsb;Gng2;Rce1;Ppp4c;Ltf
31 prerank Glycolysis 0.26477258671521586 1.0041578154539257 0.4641025641025641 0.6527746337046375 1.0 53/139 29.47% Nol3;Galk1;Pgm2;Eno2;Got1;Mif;Hspa5;Tgfa;Cyb5a;Pkm;Ldha;Cacna1h;Vldlr;Cln6;Pgam1;Ppia;Hax1;Aldoa;Paxip1;Adora2b;B3gat1;Ndufv3;Glce;Irs2;Gusb;Agrn;Ak4;Mdh1;Aldh9a1;Sod1;Slc37a4;Chst2;Mdh2;Ak3;Got2;Copb2;Pgk1;Stmn1;Lhx9;Psmc4;Tpi1;Me2;Idua;Lhpp;Dsc2;Pygl;Ext2;Hk2;P4ha2;Gclc;Fkbp4;Vcan;Sdc3
32 prerank heme Metabolism 0.25413406081576373 0.9765203693270862 0.5057766367137355 0.6945224056310882 1.0 20/143 15.06% Ank1;Asns;Alas2;Alad;Ackr1;Add2;Uros;Ncoa4;Hagh;Slc30a1;Hmbs;Prdx2;Arl2bp;Cdr2;Slc25a38;Rad23a;Bsg;Vezf1;Ctsb;Tyr
33 prerank Estrogen Response Late -0.2237607098494398 -0.9578922324713801 0.5495867768595041 1.0 0.997 31/111 19.84% Krt13;Tjp3;Prss23;Bcl2;Hr;Frk;Mettl3;Mapk13;Cd44;Tiam1;Papss2;Id2;Myof;Atp2b4;Pdcd4;Etfb;Idh2;Pdlim3;Slc22a5;Dnajc1;Cav1;Tob1;Hmgcs2;Tsta3;Slc9a3r1;Snx10;Slc24a3;Btg3;St6galnac2;Dnajc12;Emp2
34 prerank p53 Pathway 0.25053730693159376 0.9534951964260899 0.5473145780051151 0.7224480486929913 1.0 35/130 18.98% Ddit3;Cdkn2aip;Rack1;Upp1;Prkab1;S100a4;Aen;Mknk2;Hint1;Zmat3;Tgfa;Rpl18;Hras;Nol8;Cdk5r1;Ei24;Jun;Tcn2;Rrp8;Rpl36;Pmm1;Ctsf;Ak1;Epha2;Slc3a2;Rxra;Dnttip2;Acvr1b;Mxd1;Rps12;Ercc5;Ctsd;Ip6k2;Nupr1;Cd81
35 prerank Mitotic Spindle -0.21097370166091295 -0.9333716185248818 0.6682692307692307 1.0 0.998 70/157 32.98% Brca2;Katnb1;Katna1;Mid1ip1;Cdc42ep1;Tubd1;Tiam1;Tubgcp5;Tubgcp3;Rasa2;Fgd6;Palld;Dock4;Vcl;Alms1;Tbcd;Mid1;Ranbp9;Cd2ap;Synpo;Hdac6;Cdk5rap2;Rasal2;Mark4;Ezr;Shroom2;Tsc1;Pdlim5;Arhgef3;Wasl;Taok2;Arfgef1;Sorbs2;Map1s;Kif3b;Pafah1b1;Nin;Wasf2;Cttn;Nf1;Ssh2;Clasp1;Itsn1;Trio;Sass6;Pcgf5;Capzb;Nedd9;Map3k11;Rasa1;Cdc42;Dst;Cntrl;Ralbp1;Myo1e;Bcl2l11;Pxn;Abi1;Hook3;Tlk1;Kif5b;Fgd4;Pcm1;Arhgef11;Flnb;Arhgef12;Notch2;Rhot2;Tubgcp2;Kptn
36 prerank Myc Targets V2 0.30643125809523586 0.931223332857805 0.5837037037037037 0.746987869515327 1.0 10/36 19.05% Pes1;Hspe1;Bysl;Npm1;Ipo4;Imp4;Nolc1;Sord;Tfb2m;Slc19a1
37 prerank Hypoxia -0.21554093131008698 -0.9300488828773158 0.6209677419354839 1.0 0.998 22/128 14.70% Plac8;Ier3;Fosl2;Pgf;Anxa2;Bhlhe40;Gck;Bcl2;Ccng2;Gys1;Klf7;Pygm;Atp7a;Tpst2;Ankzf1;Ugp2;Pim1;Tiparp;Ndst1;Nr3c1;Cav1;Noct
38 prerank Epithelial Mesenchymal Transition 0.24552779243135303 0.9020088567477812 0.6503957783641161 0.7817605595322659 1.0 13/111 7.43% Col1a2;Efemp2;Dpysl3;Col8a2;Eno2;Vcam1;Sfrp1;Gadd45b;Serpinh1;Lgals1;Scg2;Aplp1;Colgalt1
39 prerank Xenobiotic Metabolism -0.20943592202044836 -0.8780743437079516 0.7848605577689243 1.0 1.0 19/114 14.43% Gch1;Gsto1;Aldh3a1;Hes6;Lpin2;Ddah2;Papss2;Hsd17b2;Gsr;Smox;Id2;Cdo1;Pros1;Ccl25;Alas1;Slc35b1;Slc6a12;Mpp2;Cbr1
40 prerank Coagulation -0.2228080859438533 -0.8453862265041424 0.7484662576687117 1.0 1.0 11/61 11.22% Dusp14;Prss23;A2m;Arf4;Klf7;Dpp4;Capn5;Pros1;Dusp6;Iscu;Gng12
41 prerank G2-M Checkpoint 0.22112127868494666 0.8329988615115979 0.7966751918158568 0.901180263373586 1.0 24/122 20.94% Kpna2;Jpt1;Kif22;Prmt5;Dkc1;Odc1;Cdc7;Snrpd1;Hus1;Hmgn2;Bub3;Upf1;Lmnb1;Hspa8;Chmp1a;Ncl;Nolc1;E2f3;Suv39h1;Orc6;Marcks;Gspt1;Nusap1;Rad23b
42 prerank IL-6/JAK/STAT3 Signaling -0.23835706231542095 -0.830650626613949 0.7395209580838323 1.0 1.0 6/42 7.29% Tnf;Bak1;Tnfrsf1b;A2m;Cd44;Tnfrsf21
43 prerank Androgen Response 0.22575006379262688 0.7998700950145465 0.8477970627503337 0.9302770340418556 1.0 18/83 24.65% Mak;Krt19;Akt1;Hmgcr;Ncoa4;Dhcr24;Elk4;Hmgcs1;Uap1;Sord;Dbi;Ptpn21;Fads1;Insig1;Herc3;Inpp4b;Tsc22d1;Abcc4
44 prerank UV Response Dn -0.18615944812518 -0.799777346166142 0.8653846153846154 1.0 1.0 50/119 33.36% Rnd3;Anxa2;Bhlhe40;Pparg;Sri;Col11a1;Rasa2;Atp2b4;Nipbl;Nr3c1;Cav1;Mapk14;Slc7a1;Akt3;Vav2;Sipa1l1;Pdlim5;Lpar1;Lamc1;Plcb4;Dusp1;Grk5;Kcnma1;Met;Fyn;Atp2b1;Mta1;Amph;F3;Nek7;Celf2;Cited2;Nfkb1;Apbb2;Pten;Igf1r;Gja1;Atrx;Map2k5;Scaf8;Phf3;Pdgfrb;Sfmbt1;Mmp16;Prdm2;Notch2;Magi2;Bmpr1a;Ythdc1;Cdk13
45 prerank Protein Secretion 0.22045852813665734 0.7872276219921365 0.863031914893617 0.9177484446595424 1.0 37/89 36.47% M6pr;Ergic3;Gosr2;Ykt6;Anp32e;Ap2m1;Ap1g1;Igf2r;Ica1;Rer1;Sod1;Stx7;Krt18;Tmed2;Rab14;Tmx1;Scrn1;Arf1;Copb2;Bnip3;Ap2s1;Sec22b;Stx16;Clta;Vamp3;Cd63;Arfgap3;Yipf6;Lamp2;Atp1a1;Sh3gl2;Scamp1;Tpd52;Vps45;Cltc;Tmed10;Abca1
46 prerank Angiogenesis -0.27428141084925045 -0.7670751317832225 0.7878787878787878 1.0 1.0 4/17 15.44% Pglyrp1;Tnfrsf21;Vav2;Vegfa
47 prerank Reactive Oxygen Species Pathway -0.22922053339466036 -0.7658627660860788 0.8409785932721713 1.0 1.0 6/37 8.12% Junb;Mbp;Txnrd1;Gpx3;Sbno2;Gsr
48 prerank Hedgehog Signaling 0.2589699613946461 0.7358574022294828 0.8303303303303303 0.9515025559183689 1.0 3/26 11.07% Scg2;Cdk5r1;Vldlr
49 prerank Notch Signaling -0.23588243860578473 -0.735161642724472 0.8547008547008547 0.9879714961384654 1.0 14/27 32.76% Lfng;Notch3;Cul1;Kat2a;Fzd7;Arrb1;Hes1;Tcf7l2;Fbxw11;Ccnd1;Notch1;Dtx1;Maml2;Notch2
50 prerank Spermatogenesis 0.17115998246638217 0.5636172630269956 0.9915848527349228 0.9948636686519863 1.0 7/59 14.21% Gsg1;Gad1;Pcsk1n;Tsn;Pebp1;Snap91;Coil
51 prerank TGF-beta Signaling -0.12918116358539997 -0.4566951529981553 1.0 0.9992536199432751 1.0 8/46 21.76% Junb;Id2;Tgfbr1;Arid4b;Smurf2;Fnta;Wwtr1;Bmpr2
output/16_gsea/gsea_RGC1-like_Hallmark.csv
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Name,Term,ES,NES,NOM p-val,FDR q-val,FWER p-val,Tag %,Gene %,Lead_genes
prerank,Oxidative Phosphorylation,0.619921119543991,2.3406622674210014,0.0,0.0,0.0,73/159,19.00%,Grpel1;Atp6v1f;Mrpl34;Uqcr11;Ndufs7;Ndufa1;Cox8a;Acadvl;Mdh2;Cox4i1;Phb2;Ndufs6;Cox6c;Ndufa2;Ndufa3;Timm10;Cox6b1;Casp7;Ndufc2;Bax;Htra2;Phyh;Timm13;Ndufb7;Ndufb5;Timm8b;Polr2f;Ndufb2;Ndufa7;Sdhd;Mrps22;Ndufv1;Ndufb8;Cox17;Cox5b;Ndufa4;Atp6ap1;Ndufa8;Cox5a;Ndufc1;Uqcrc1;Ndufb6;Ech1;Uqcr10;Gpx4;Por;Uqcrq;Vdac1;Ldha;Cox7c;Cox7a2;Uqcrb;Cycs;Surf1;Oxa1l;Atp1b1;Tomm22;Mfn2;Mgst3;Aldh6a1;Etfb;Cpt1a;Slc25a4;Cox7a2l;Ndufb3;Atp6v1d;Dld;Vdac2;Sdhb;Uqcrh;Hadhb;Hspa9;Cox7b
prerank,Reactive Oxygen Species Pathway,0.6434005446098667,1.9192917168244554,0.0,0.0033347448655515564,0.006,14/37,11.01%,Prdx4;Srxn1;Lamtor5;Prnp;Sbno2;Ercc2;Prdx6;Junb;Cdkn2d;Atox1;Cat;Glrx;Oxsr1;Gpx4
prerank,p53 Pathway,0.4542903018399436,1.6212700640313085,0.0,0.08448020326063943,0.201,40/102,22.11%,Ddit3;Ctsd;Hras;Ier3;Nupr1;Sat1;Bax;Rpl36;Jun;Rps12;Ctsf;Rack1;Wrap73;Hint1;Rrp8;Rpl18;Ndrg1;Tprkb;Cgrrf1;Pom121;Slc3a2;Fuca1;Tm7sf3;Polh;Cd81;Tob1;Pmm1;Zmat3;Hdac3;Hexim1;Pvt1;Plxnb2;Cdk5r1;Slc35d1;Acvr1b;Jag2;Cdh13;Csrnp2;Rchy1;Apaf1
prerank,Adipogenesis,0.4335678965104166,1.6164400505587286,0.0011695906432748538,0.0664170019055685,0.21,53/148,23.70%,Grpel1;Ccng2;Uqcr11;Cox8a;Chchd10;Apoe;Mdh2;Reep6;Ddt;Vegfb;Phyh;Slc27a1;Ndufb7;Slc5a6;Cat;Lpcat3;Cd151;Dnajb9;Phldb1;Uqcrc1;Ubc;Ech1;Ubqln1;Uqcr10;Gpx4;Por;Uqcrq;Aplp2;Aldoa;Tob1;Col4a1;Mgst3;Rreb1;Etfb;Ptcd3;Lifr;Gpat4;Dld;Rmdn3;Sdhb;Taldo1;Qdpr;Cox7b;Angpt1;Nabp1;Ifngr1;Pparg;Ndufa5;Ndufs3;Idh3g;Lipe;Tkt;Lama4
prerank,Epithelial Mesenchymal Transition,0.4426960476031183,1.5509308565475173,0.011873350923482849,0.10182087656150753,0.369,25/79,17.35%,Spp1;Fbln1;Cald1;Adam12;Sat1;Magee1;Slit3;Jun;Scg2;Gpc1;Bdnf;Fuca1;Lgals1;Fbn1;Plod1;Flna;Col11a1;Col4a1;Colgalt1;Pvr;Pfn2;Tgfbr3;Ppib;Plod3;Slc6a8
prerank,Myc Targets V1,0.40814294683439184,1.5367782955156422,0.0011737089201877935,0.09596654668642812,0.402,80/161,31.35%,Prdx4;Snrpd2;Phb2;Rpl34;Nme1;Rps10;Rps3;Ran;Rack1;Rpl18;Nolc1;Eif3d;Cox5a;Rsl1d1;Psma4;Pa2g4;Dut;Vdac1;Ldha;Cnbp;Rps2;Rplp0;Impdh2;Eef1b2;Pabpc4;Rpl6;Snrpg;Rps5;Rrp9;Psmd8;Eif4g2;Ppm1g;Odc1;Hspe1;Cct7;Rnps1;Tcp1;Ptges3;Set;Snrpd3;Srsf3;Clns1a;Cct5;Ppia;Uba2;Rpl14;Cct2;Psma7;Snrpa1;Ddx21;Hdgf;Xrcc6;Canx;Psmd3;Psma2;Usp1;Glo1;Mrpl23;Ifrd1;Ranbp1;Lsm7;U2af1;Eif3b;Psma6;Ndufab1;G3bp1;Phb;Hsp90ab1;Pcbp1;Psmb3;Sf3b3;Eif4h;Cct4;Cbx3;Hspd1;Rps6;Rpl22;Fbl;Psmc4;Pgk1
prerank,Hypoxia,0.4099888220656954,1.4860976501485275,0.014814814814814815,0.1324370103747618,0.555,28/110,14.94%,Ddit3;Ccng2;Csrp2;Ncan;Cited2;Ier3;Hspa5;Mif;Siah2;Jun;Slc37a4;Ndrg1;Ampd3;Chst2;Pgm2;Gpc1;Ankzf1;Glrx;Hexa;Has1;Gaa;Gapdh;Cdkn1b;Ldha;Aldoa;Anxa2;Aldoc;P4ha2
prerank,UV Response Up,0.42238379894098116,1.4790938295467728,0.012610340479192938,0.12282976921448233,0.58,26/85,17.38%,Grpel1;Atp6v1f;Rpn1;Pdap1;Bcl2l11;Casp3;Hspa13;Junb;Fkbp4;Creg1;Ret;Cebpg;Grina;Asns;Bsg;Ap2s1;Selenow;Kcnh2;Sigmar1;Aldoa;Psmc3;Nptxr;Tmbim6;Rxrb;Slc6a8;Slc25a4
prerank,Unfolded Protein Response,0.4174843795895568,1.4557584958398218,0.015444015444015444,0.13437786717481828,0.652,32/87,24.23%,Yif1a;Slc7a5;Rps14;Lsm4;Calr;Hspa5;Pdia6;Slc1a4;Eef2;Aldh18a1;Dnajb9;Cebpg;Nolc1;Asns;Xbp1;Herpud1;Tubb2a;Dcp2;Hyou1;Rrp9;Ssr1;Wipi1;Cxxc1;Hsp90b1;Hspa9;Dnaja4;Nabp1;Atf4;Imp3;Mthfd2;Nfyb;Tspyl2
prerank,IL-2/STAT5 Signaling,0.41711427714794586,1.447972040681071,0.023316062176165803,0.1300550497565107,0.672,27/89,23.53%,Spp1;Etv4;Penk;Wls;Prnp;Casp3;Ptrh2;Ndrg1;P2rx4;Gabarapl1;Xbp1;Nfkbiz;Twsg1;Gpx4;Cd81;Tnfrsf21;Gsto1;Lrig1;Ttc39b;Odc1;Plec;Dennd5a;Hk2;Ifngr1;Ahr;Aplp1;Scn9a
prerank,heme Metabolism,0.38127398011854036,1.3971966225563734,0.028151774785801713,0.17765095374665563,0.814,39/129,23.77%,Lrp10;Khnyn;Endod1;Rad23a;Bpgm;Htra2;Cat;Nfe2l1;Ppp2r5b;Adipor1;Pgls;Asns;Bsg;Blvra;Arl2bp;Slc25a37;Rcl1;Bach1;Gde1;Osbp2;Mgst3;P4ha2;Aldh6a1;Mospd1;Fn3k;Psmd9;Optn;Mpp1;Slc6a8;Fech;Nek7;Isca1;Alas2;Sidt2;Ranbp10;Acsl6;Gclm;Hdgf;Ctsb
prerank,mTORC1 Signaling,0.37340860855558616,1.3903486136073684,0.022592152199762187,0.17201725598136777,0.826,54/146,26.25%,Ddit3;Slc7a5;Rpn1;Pdap1;Calr;Nupr1;Hspa5;Nufip1;Lgmn;Slc1a4;Slc37a4;Asns;Xbp1;Glrx;Psma4;Gapdh;Ldha;Ppa1;Aldoa;Atp2a2;Psmb5;Rrp9;Ssr1;Cacybp;Ufm1;Abcf2;Hspe1;Atp6v1d;Pdk1;Hsp90b1;Ldlr;Gtf2h1;Hspa9;Qdpr;Idi1;Psme3;Hk2;Fkbp2;Ppia;Dhcr24;Sqstm1;Elovl5;Tpi1;Sqle;Sytl2;Hmgcs1;Mthfd2;Slc6a6;Psph;Canx;Ifrd1;Got1;Sc5d;Pgm1
prerank,Spermatogenesis,-0.35372317805680414,-1.3796487152906651,0.05785123966942149,0.4027504911591356,0.43,19/60,20.79%,Dmc1;Cnih2;Oaz3;Cftr;Camk4;Grm8;Pgs1;Slc12a2;Chfr;Clpb;Tle4;Rad17;Gmcl1;Braf;Pacrg;Arl4a;Adcyap1;Snap91;Ip6k1
prerank,Myogenesis,0.3844208121062666,1.3522962249111645,0.05157232704402516,0.21008892652974803,0.9,36/90,28.01%,Fxyd1;Ptp4a3;Adam12;Kifc3;Prnp;Cryab;Igfbp7;Kcnh1;Ckb;Pvalb;Clu;Actn2;Cacna1h;Atp6ap1;Cnn3;Gaa;Kcnh2;Fabp3;Fhl1;Slc6a8;Plxnb2;Akt2;Hdac5;Ppfia4;Psen2;Cdh13;Ptgis;Fdps;Pdlim7;Ifrd1;Adcy9;Flii;Myo1c;Reep1;Ocel1;Camk2b
prerank,Complement,0.38531825394882924,1.3459753948991577,0.06508135168961202,0.20516620791869578,0.911,14/88,7.62%,Gngt2;Ctsd;Prss36;Mmp15;Casp3;Hspa5;Sirt6;Lgmn;Casp7;Dgkg;Atox1;Clu;Actn2;Ctsl
prerank,Xenobiotic Metabolism,0.3831864788226124,1.3293073404406845,0.0678617157490397,0.2148316747829769,0.933,25/85,19.25%,Fbln1;Ptgds;Apoe;Lonp1;Ddt;Vtn;Dhrs1;Cat;Pgrmc1;Gabarapl1;Cbr1;Ech1;Por;Atp2a2;Pmm1;Abcd2;Slc46a3;Gsto1;Tmbim6;Pink1;Entpd5;Acox3;Ptges3;Slc35d1;Ssr3
prerank,DNA Repair,0.3679493401248192,1.3283374335454894,0.05375,0.2025162767308914,0.935,38/116,21.91%,Cetn2;Ercc2;Nme1;Rad51;Aprt;Polr3c;Polr2j;Guk1;Arl6ip1;Polr2e;Polr2f;Cant1;Pde6g;Polr2k;Pole4;Polr1d;Cox17;Pom121;Edf1;Polh;Gpx4;Dut;Surf1;Impdh2;Vps37b;Ercc8;Polr2c;Dad1;Eif1b;Nt5c;Gtf2h1;Dgcr8;Rbx1;Nelfcd;Nudt21;Adrm1;Taf10;Ssrp1
prerank,KRAS Signaling Up,0.377718191111655,1.304695877054277,0.10533159947984395,0.22885503979275384,0.961,15/78,16.19%,Spp1;Etv1;Gpnmb;Etv4;Nap1l2;Bpgm;Pcsk1n;Glrx;Fuca1;Dock2;Pcp4;Mmd;Gadd45g;Etv5;Ptcd2
prerank,Apoptosis,0.3798835725067369,1.3014662345553973,0.08122503328894808,0.22101947914461148,0.968,22/74,21.55%,Ddit3;Bcl2l11;Casp3;Ier3;Sat1;Casp7;Bax;Jun;Clu;Dpyd;Ppp2r5b;Gpx4;Cdkn1b;Nefh;Timp2;Tgfbr3;Vdac2;Mcl1;Psen2;Smad7;Sqstm1;Ifngr1
prerank,Glycolysis,0.3533024969072083,1.2934477159942654,0.07627118644067797,0.22313538451251994,0.972,40/124,22.18%,Chpf;Mdh2;Cited2;Copb2;Ier3;Hspa5;Slc25a13;Mif;Prps1;Fkbp4;Slc37a4;Pgls;Pgam1;Cacna1h;Chst2;Pgm2;Xylt2;Gpc1;Ankzf1;Glrx;Ldha;Ndufv3;Plod1;Gne;Aldoa;Hax1;Nol3;P4ha2;Chst12;Gmppa;Pkm;Dld;Taldo1;Ppfia4;B4galt4;Hk2;Ppia;Dpysl4;Tpi1;Polr3k
prerank,Bile Acid Metabolism,0.3696117390314927,1.20662816405311,0.17753120665742025,0.3670998306161339,0.998,18/57,23.27%,Ttr;Rbp1;Bmp6;Pex6;Phyh;Cat;Cyp46a1;Abca2;Abcd2;Prdx5;Optn;Slc29a1;Pex19;Idi1;Nudt12;Dhcr24;Gclm;Lipe
prerank,Apical Surface,-0.3985625019149619,-1.200579777651937,0.22969187675070027,0.6155861165684349,0.834,2/20,2.65%,Pkhd1;Crybg1
prerank,Hedgehog Signaling,-0.3537478281913628,-1.1829352572062866,0.22674418604651161,0.44968347522375024,0.862,4/27,8.33%,Cdk6;Shh;Unc5c;Tle1
prerank,Angiogenesis,0.45006017307214286,1.152849605704238,0.2857142857142857,0.4696167690027525,1.0,4/17,13.50%,Spp1;Vtn;Lrpap1;Tnfrsf21
prerank,Estrogen Response Early,0.3202577761175286,1.1457554653526965,0.24015247776365947,0.46539901449386945,1.0,18/103,13.09%,Celsr2;Slc7a5;Endod1;Podxl;Siah2;Hes1;Slc1a4;Fkbp4;Slc26a2;Cant1;Ret;Syt12;Slc1a1;Unc119;Xbp1;Slc22a5;Tob1;Syngr1
prerank,Inflammatory Response,0.343677014173989,1.1128593248951348,0.27034482758620687,0.5261164145854238,1.0,17/52,24.87%,Slc7a1;Chst2;Slc31a1;P2rx4;Atp2a2;Ly6e;Pvr;Tapbp;Ldlr;Acvr1b;Itga5;Selenos;Adrm1;Ahr;Ptger4;Cx3cl1;Gabbr1
prerank,Apical Junction,-0.2356305465622273,-1.0356495552232514,0.33163265306122447,0.75,0.982,36/117,25.49%,Col9a1;Egfr;Mmp9;Vav2;Itga10;Inppl1;Sorbs3;Map4k2;Epb41l2;Mpp5;Pik3r3;Jup;Cadm2;Lama3;Exoc4;Dlg1;Crat;Gnai1;Rsu1;Pik3cb;Actn1;Rasa1;Pbx2;Cdh11;Tjp1;Nf1;Baiap2;Pard6g;Akt3;Lima1;Wasl;Vcl;Ptk2;Adam9;Nf2;Itga9
prerank,Notch Signaling,0.399847827918841,1.0197119168626385,0.4358974358974359,0.7571260321829346,1.0,8/17,21.85%,Notch3;Hes1;Psenen;Dtx4;St3gal6;Rbx1;Psen2;Aph1a
prerank,Estrogen Response Late,0.28712203186271557,1.006757252677638,0.463254593175853,0.7633897946220622,1.0,15/81,12.51%,Celsr2;Slc7a5;Cox6c;Siah2;Ckb;Slc1a4;Fkbp4;Slc26a2;Fabp5;Ret;Mocs2;Xbp1;Slc22a5;Pcp4;Tob1
prerank,Androgen Response,0.2884323946031561,1.0015961386380297,0.45611702127659576,0.7471538624407563,1.0,24/74,25.55%,Dbi;Sat1;Slc26a2;Abcc4;Uap1;Ndrg1;Rab4a;Dnajb9;Pgm3;Pa2g4;Lifr;Srp19;Abhd2;Bmpr1b;Idi1;Adrm1;Dhcr24;Elovl5;Ube2j1;Lman1;Hmgcs1;Xrcc6;Sgk1;Iqgap2
prerank,Fatty Acid Metabolism,0.2808233490668568,0.9984402645691206,0.45591939546599497,0.7281271320017879,1.0,31/97,22.90%,Acadvl;Erp29;Mdh2;Reep6;Mif;Prdx6;Sdhd;Ncaph2;Gabarapl1;Blvra;Lgals1;Cbr1;Ech1;Hsdl2;Slc22a5;Ldha;Serinc1;Aldoa;Cpt1a;Odc1;Dld;Hadhb;Bmpr1b;Idi1;Hsp90aa1;Dhcr24;Eci2;Elovl5;Fasn;Idh3g;Hmgcs1
prerank,Pperoxisome,0.29520251049695345,0.97821913372473,0.5116598079561042,0.757661973322041,1.0,15/65,21.45%,Ttr;Pex6;Hras;Cat;Ech1;Cnbp;Nudt19;Pex2;Abcd2;Prdx5;Slc25a4;Idi1;Dhcr24;Eci2;Elovl5
prerank,Myc Targets V2,0.3284418751910447,0.9696695222900162,0.5,0.7518508107792389,1.0,19/33,33.29%,Tfb2m;Nolc1;Pa2g4;Rcl1;Rabepk;Rrp9;Hspe1;Hk2;Prmt3;Ndufaf4;Las1l;Mrto4;Pprc1;Phb;Cbx3;Hspd1;Npm1;Supv3l1;Mphosph10
prerank,TNF-alpha Signaling via NF-kB,0.26011523232554457,0.894600418023324,0.6771752837326608,0.9148687275037052,1.0,8/73,5.95%,Plpp3;Ier3;Sat1;Smad3;Hes1;Jun;Junb;Litaf
prerank,Coagulation,0.2927301625154501,0.8806583834528166,0.6666666666666666,0.9175568426300942,1.0,4/39,6.01%,Dct;Mmp15;Lgmn;Clu
prerank,PI3K/AKT/mTOR Signaling,0.24582824201503325,0.8558574210392627,0.6987620357634112,0.9455043801609148,1.0,19/79,24.71%,Ddit3;Hras;Calr;Gngt1;Csnk2b;Cdkn1b;Them4;Dusp3;Ralb;Arpc3;Ap2m1;Pdk1;Hsp90b1;Sqstm1;Pin1;Plcg1;Ripk1;Pla2g12a;Rps6ka1
prerank,Cholesterol Homeostasis,0.2739145845269427,0.8547575112449235,0.6930555555555555,0.9192443169788077,1.0,20/43,28.29%,Lgmn;Clu;Fabp5;Ech1;Abca2;Aldoc;Ldlr;Idi1;Pparg;Fasn;Sqle;Hmgcs1;Pmvk;Fdps;Pnrc1;Sc5d;Pcyt2;Acat2;Ctnnb1;Atf5
prerank,G2-M Checkpoint,0.23397119797899435,0.8494276587838939,0.7564259485924113,0.9039120200271512,1.0,39/117,30.73%,Slc7a5;Smad3;Slc7a1;Kif22;Jpt1;Nolc1;Pttg1;Chmp1a;Cdkn1b;Arid4a;E2f4;Lmnb1;Hspa8;Polq;Odc1;Lbr;Hira;Katna1;Meis2;Sqle;Ss18;Cdc25b;Ncl;Nsd2;Nasp;Pml;Tmpo;Smc2;G3bp1;Orc6;Atf5;Hmgn2;Prmt5;Upf1;Nup50;Uck2;Tle3;Brca2;Odf2
prerank,Pancreas Beta Cells,-0.27556487299059124,-0.8479661202906396,0.648876404494382,1.0,1.0,3/20,5.53%,Foxo1;Mafb;Slc2a2
prerank,E2F Targets,0.23035715984259164,0.8269813074733565,0.784409257003654,0.9224857082362905,1.0,17/108,11.77%,Prdx4;Prps1;Nme1;Spag5;Ncapd2;Ran;Kif22;Jpt1;Pole4;Lyar;Nolc1;Mms22l;Pttg1;Pa2g4;Cdkn1b;Dut;Phf5a
prerank,Interferon Gamma Response,0.23937713363156826,0.8015886390583518,0.803840877914952,0.9457706965911498,1.0,17/62,25.09%,Gpr18;Rnf213;Casp3;Bpgm;Casp7;Ifi27;Psme2;Rnf31;Ly6e;Cmtr1;Tapbp;Tor1b;Trim26;Mthfd2;Slc25a28;Ripk1;Psma2
prerank,Protein Secretion,0.2216951127676127,0.7759364123245033,0.871536523929471,0.9645524203008853,1.0,9/85,11.32%,Cd63;Copb2;Vamp7;Cope;Vamp3;Ap2s1;Ergic3;Rer1;Tmx1
prerank,Allograft Rejection,0.23999552665871182,0.7643178683577514,0.8465829846582985,0.9564574813064844,1.0,9/51,14.75%,Nme1;Rps9;Rpl39;Csk;Rps19;Eif3d;Degs1;Flna;Eif5a
prerank,KRAS Signaling Dn,0.22613743228164682,0.7504661223471343,0.8862433862433863,0.9500602615677779,1.0,7/61,10.85%,Celsr2;Kcnmb1;Arhgdig;Entpd7;Chst2;Kcnd1;Sncb
prerank,IL-6/JAK/STAT3 Signaling,0.2691815278013144,0.7328446756427207,0.8408736349453978,0.9478456489519373,1.0,11/23,37.58%,Jun;Hax1;Tnfrsf21;Acvr1b;Ifngr1;Ptpn11;Grb2;Stat2;Csf2ra;Ptpn1;Ptpn2
prerank,Interferon Alpha Response,0.2679035902985367,0.7317911311561915,0.8115501519756839,0.9259474910014821,1.0,7/23,23.06%,Ifi27;Psme2;Rnf31;Ly6e;Cmtr1;Trim26;Slc25a28
prerank,UV Response Dn,0.1840611345380614,0.6682328764160463,0.9852034525277436,0.9571776413296633,1.0,19/106,21.77%,Pik3cd;Plpp3;Cited2;Smad3;Slc7a1;Bdnf;Synj2;Mta1;Cdkn1b;Anxa2;Col11a1;Tgfbr3;Dbp;Ldlr;Nek7;Smad7;Abcc1;Pparg;Pias3
prerank,Mitotic Spindle,-0.13654734956699302,-0.6225645742263682,1.0,1.0,1.0,47/143,31.64%,Smc4;Mid1;Epb41l2;Alms1;Dock4;Arl8a;Dlg1;Stk38l;Nck2;Rasa1;Cdc42bpa;Tuba4a;Cd2ap;Kif1b;Wasf1;Clip2;Rapgef6;Nf1;Ssh2;Als2;Hook3;Ccdc88a;Pcgf5;Clip1;Wasl;Cdk5rap2;Rock1;Ckap5;Numa1;Vcl;Rasa2;Cttn;Tubgcp5;Fgd4;Pcnt;Incenp;Bin1;Sorbs2;Pafah1b1;Rictor;Fscn1;Cntrl;Rapgef5;Kif5b;Pcm1;Tiam1;Tbcd
prerank,TGF-beta Signaling,-0.17788089584593306,-0.6052933201645828,0.9689655172413794,1.0,1.0,5/37,13.38%,Bcar3;Fnta;Cdk9;Hdac1;Klf10
prerank,Wnt-beta Catenin Signaling,-0.19113300492610838,-0.5890794535568823,0.9709302325581395,0.9900130975769482,1.0,23/23,80.93%,Hdac2;Ncstn;Gnai1;Cul1;Nkd1;Axin1;Wnt5b;Numb;Ncor2;Rbpj;Dvl2;Ptch1;Adam17;Ppard;Skp2;Maml1;Hdac11;Csnk1e;Ctnnb1;Axin2;Psen2;Jag2;Hdac5
1 Name Term ES NES NOM p-val FDR q-val FWER p-val Tag % Gene % Lead_genes
2 prerank Oxidative Phosphorylation 0.619921119543991 2.3406622674210014 0.0 0.0 0.0 73/159 19.00% Grpel1;Atp6v1f;Mrpl34;Uqcr11;Ndufs7;Ndufa1;Cox8a;Acadvl;Mdh2;Cox4i1;Phb2;Ndufs6;Cox6c;Ndufa2;Ndufa3;Timm10;Cox6b1;Casp7;Ndufc2;Bax;Htra2;Phyh;Timm13;Ndufb7;Ndufb5;Timm8b;Polr2f;Ndufb2;Ndufa7;Sdhd;Mrps22;Ndufv1;Ndufb8;Cox17;Cox5b;Ndufa4;Atp6ap1;Ndufa8;Cox5a;Ndufc1;Uqcrc1;Ndufb6;Ech1;Uqcr10;Gpx4;Por;Uqcrq;Vdac1;Ldha;Cox7c;Cox7a2;Uqcrb;Cycs;Surf1;Oxa1l;Atp1b1;Tomm22;Mfn2;Mgst3;Aldh6a1;Etfb;Cpt1a;Slc25a4;Cox7a2l;Ndufb3;Atp6v1d;Dld;Vdac2;Sdhb;Uqcrh;Hadhb;Hspa9;Cox7b
3 prerank Reactive Oxygen Species Pathway 0.6434005446098667 1.9192917168244554 0.0 0.0033347448655515564 0.006 14/37 11.01% Prdx4;Srxn1;Lamtor5;Prnp;Sbno2;Ercc2;Prdx6;Junb;Cdkn2d;Atox1;Cat;Glrx;Oxsr1;Gpx4
4 prerank p53 Pathway 0.4542903018399436 1.6212700640313085 0.0 0.08448020326063943 0.201 40/102 22.11% Ddit3;Ctsd;Hras;Ier3;Nupr1;Sat1;Bax;Rpl36;Jun;Rps12;Ctsf;Rack1;Wrap73;Hint1;Rrp8;Rpl18;Ndrg1;Tprkb;Cgrrf1;Pom121;Slc3a2;Fuca1;Tm7sf3;Polh;Cd81;Tob1;Pmm1;Zmat3;Hdac3;Hexim1;Pvt1;Plxnb2;Cdk5r1;Slc35d1;Acvr1b;Jag2;Cdh13;Csrnp2;Rchy1;Apaf1
5 prerank Adipogenesis 0.4335678965104166 1.6164400505587286 0.0011695906432748538 0.0664170019055685 0.21 53/148 23.70% Grpel1;Ccng2;Uqcr11;Cox8a;Chchd10;Apoe;Mdh2;Reep6;Ddt;Vegfb;Phyh;Slc27a1;Ndufb7;Slc5a6;Cat;Lpcat3;Cd151;Dnajb9;Phldb1;Uqcrc1;Ubc;Ech1;Ubqln1;Uqcr10;Gpx4;Por;Uqcrq;Aplp2;Aldoa;Tob1;Col4a1;Mgst3;Rreb1;Etfb;Ptcd3;Lifr;Gpat4;Dld;Rmdn3;Sdhb;Taldo1;Qdpr;Cox7b;Angpt1;Nabp1;Ifngr1;Pparg;Ndufa5;Ndufs3;Idh3g;Lipe;Tkt;Lama4
6 prerank Epithelial Mesenchymal Transition 0.4426960476031183 1.5509308565475173 0.011873350923482849 0.10182087656150753 0.369 25/79 17.35% Spp1;Fbln1;Cald1;Adam12;Sat1;Magee1;Slit3;Jun;Scg2;Gpc1;Bdnf;Fuca1;Lgals1;Fbn1;Plod1;Flna;Col11a1;Col4a1;Colgalt1;Pvr;Pfn2;Tgfbr3;Ppib;Plod3;Slc6a8
7 prerank Myc Targets V1 0.40814294683439184 1.5367782955156422 0.0011737089201877935 0.09596654668642812 0.402 80/161 31.35% Prdx4;Snrpd2;Phb2;Rpl34;Nme1;Rps10;Rps3;Ran;Rack1;Rpl18;Nolc1;Eif3d;Cox5a;Rsl1d1;Psma4;Pa2g4;Dut;Vdac1;Ldha;Cnbp;Rps2;Rplp0;Impdh2;Eef1b2;Pabpc4;Rpl6;Snrpg;Rps5;Rrp9;Psmd8;Eif4g2;Ppm1g;Odc1;Hspe1;Cct7;Rnps1;Tcp1;Ptges3;Set;Snrpd3;Srsf3;Clns1a;Cct5;Ppia;Uba2;Rpl14;Cct2;Psma7;Snrpa1;Ddx21;Hdgf;Xrcc6;Canx;Psmd3;Psma2;Usp1;Glo1;Mrpl23;Ifrd1;Ranbp1;Lsm7;U2af1;Eif3b;Psma6;Ndufab1;G3bp1;Phb;Hsp90ab1;Pcbp1;Psmb3;Sf3b3;Eif4h;Cct4;Cbx3;Hspd1;Rps6;Rpl22;Fbl;Psmc4;Pgk1
8 prerank Hypoxia 0.4099888220656954 1.4860976501485275 0.014814814814814815 0.1324370103747618 0.555 28/110 14.94% Ddit3;Ccng2;Csrp2;Ncan;Cited2;Ier3;Hspa5;Mif;Siah2;Jun;Slc37a4;Ndrg1;Ampd3;Chst2;Pgm2;Gpc1;Ankzf1;Glrx;Hexa;Has1;Gaa;Gapdh;Cdkn1b;Ldha;Aldoa;Anxa2;Aldoc;P4ha2
9 prerank UV Response Up 0.42238379894098116 1.4790938295467728 0.012610340479192938 0.12282976921448233 0.58 26/85 17.38% Grpel1;Atp6v1f;Rpn1;Pdap1;Bcl2l11;Casp3;Hspa13;Junb;Fkbp4;Creg1;Ret;Cebpg;Grina;Asns;Bsg;Ap2s1;Selenow;Kcnh2;Sigmar1;Aldoa;Psmc3;Nptxr;Tmbim6;Rxrb;Slc6a8;Slc25a4
10 prerank Unfolded Protein Response 0.4174843795895568 1.4557584958398218 0.015444015444015444 0.13437786717481828 0.652 32/87 24.23% Yif1a;Slc7a5;Rps14;Lsm4;Calr;Hspa5;Pdia6;Slc1a4;Eef2;Aldh18a1;Dnajb9;Cebpg;Nolc1;Asns;Xbp1;Herpud1;Tubb2a;Dcp2;Hyou1;Rrp9;Ssr1;Wipi1;Cxxc1;Hsp90b1;Hspa9;Dnaja4;Nabp1;Atf4;Imp3;Mthfd2;Nfyb;Tspyl2
11 prerank IL-2/STAT5 Signaling 0.41711427714794586 1.447972040681071 0.023316062176165803 0.1300550497565107 0.672 27/89 23.53% Spp1;Etv4;Penk;Wls;Prnp;Casp3;Ptrh2;Ndrg1;P2rx4;Gabarapl1;Xbp1;Nfkbiz;Twsg1;Gpx4;Cd81;Tnfrsf21;Gsto1;Lrig1;Ttc39b;Odc1;Plec;Dennd5a;Hk2;Ifngr1;Ahr;Aplp1;Scn9a
12 prerank heme Metabolism 0.38127398011854036 1.3971966225563734 0.028151774785801713 0.17765095374665563 0.814 39/129 23.77% Lrp10;Khnyn;Endod1;Rad23a;Bpgm;Htra2;Cat;Nfe2l1;Ppp2r5b;Adipor1;Pgls;Asns;Bsg;Blvra;Arl2bp;Slc25a37;Rcl1;Bach1;Gde1;Osbp2;Mgst3;P4ha2;Aldh6a1;Mospd1;Fn3k;Psmd9;Optn;Mpp1;Slc6a8;Fech;Nek7;Isca1;Alas2;Sidt2;Ranbp10;Acsl6;Gclm;Hdgf;Ctsb
13 prerank mTORC1 Signaling 0.37340860855558616 1.3903486136073684 0.022592152199762187 0.17201725598136777 0.826 54/146 26.25% Ddit3;Slc7a5;Rpn1;Pdap1;Calr;Nupr1;Hspa5;Nufip1;Lgmn;Slc1a4;Slc37a4;Asns;Xbp1;Glrx;Psma4;Gapdh;Ldha;Ppa1;Aldoa;Atp2a2;Psmb5;Rrp9;Ssr1;Cacybp;Ufm1;Abcf2;Hspe1;Atp6v1d;Pdk1;Hsp90b1;Ldlr;Gtf2h1;Hspa9;Qdpr;Idi1;Psme3;Hk2;Fkbp2;Ppia;Dhcr24;Sqstm1;Elovl5;Tpi1;Sqle;Sytl2;Hmgcs1;Mthfd2;Slc6a6;Psph;Canx;Ifrd1;Got1;Sc5d;Pgm1
14 prerank Spermatogenesis -0.35372317805680414 -1.3796487152906651 0.05785123966942149 0.4027504911591356 0.43 19/60 20.79% Dmc1;Cnih2;Oaz3;Cftr;Camk4;Grm8;Pgs1;Slc12a2;Chfr;Clpb;Tle4;Rad17;Gmcl1;Braf;Pacrg;Arl4a;Adcyap1;Snap91;Ip6k1
15 prerank Myogenesis 0.3844208121062666 1.3522962249111645 0.05157232704402516 0.21008892652974803 0.9 36/90 28.01% Fxyd1;Ptp4a3;Adam12;Kifc3;Prnp;Cryab;Igfbp7;Kcnh1;Ckb;Pvalb;Clu;Actn2;Cacna1h;Atp6ap1;Cnn3;Gaa;Kcnh2;Fabp3;Fhl1;Slc6a8;Plxnb2;Akt2;Hdac5;Ppfia4;Psen2;Cdh13;Ptgis;Fdps;Pdlim7;Ifrd1;Adcy9;Flii;Myo1c;Reep1;Ocel1;Camk2b
16 prerank Complement 0.38531825394882924 1.3459753948991577 0.06508135168961202 0.20516620791869578 0.911 14/88 7.62% Gngt2;Ctsd;Prss36;Mmp15;Casp3;Hspa5;Sirt6;Lgmn;Casp7;Dgkg;Atox1;Clu;Actn2;Ctsl
17 prerank Xenobiotic Metabolism 0.3831864788226124 1.3293073404406845 0.0678617157490397 0.2148316747829769 0.933 25/85 19.25% Fbln1;Ptgds;Apoe;Lonp1;Ddt;Vtn;Dhrs1;Cat;Pgrmc1;Gabarapl1;Cbr1;Ech1;Por;Atp2a2;Pmm1;Abcd2;Slc46a3;Gsto1;Tmbim6;Pink1;Entpd5;Acox3;Ptges3;Slc35d1;Ssr3
18 prerank DNA Repair 0.3679493401248192 1.3283374335454894 0.05375 0.2025162767308914 0.935 38/116 21.91% Cetn2;Ercc2;Nme1;Rad51;Aprt;Polr3c;Polr2j;Guk1;Arl6ip1;Polr2e;Polr2f;Cant1;Pde6g;Polr2k;Pole4;Polr1d;Cox17;Pom121;Edf1;Polh;Gpx4;Dut;Surf1;Impdh2;Vps37b;Ercc8;Polr2c;Dad1;Eif1b;Nt5c;Gtf2h1;Dgcr8;Rbx1;Nelfcd;Nudt21;Adrm1;Taf10;Ssrp1
19 prerank KRAS Signaling Up 0.377718191111655 1.304695877054277 0.10533159947984395 0.22885503979275384 0.961 15/78 16.19% Spp1;Etv1;Gpnmb;Etv4;Nap1l2;Bpgm;Pcsk1n;Glrx;Fuca1;Dock2;Pcp4;Mmd;Gadd45g;Etv5;Ptcd2
20 prerank Apoptosis 0.3798835725067369 1.3014662345553973 0.08122503328894808 0.22101947914461148 0.968 22/74 21.55% Ddit3;Bcl2l11;Casp3;Ier3;Sat1;Casp7;Bax;Jun;Clu;Dpyd;Ppp2r5b;Gpx4;Cdkn1b;Nefh;Timp2;Tgfbr3;Vdac2;Mcl1;Psen2;Smad7;Sqstm1;Ifngr1
21 prerank Glycolysis 0.3533024969072083 1.2934477159942654 0.07627118644067797 0.22313538451251994 0.972 40/124 22.18% Chpf;Mdh2;Cited2;Copb2;Ier3;Hspa5;Slc25a13;Mif;Prps1;Fkbp4;Slc37a4;Pgls;Pgam1;Cacna1h;Chst2;Pgm2;Xylt2;Gpc1;Ankzf1;Glrx;Ldha;Ndufv3;Plod1;Gne;Aldoa;Hax1;Nol3;P4ha2;Chst12;Gmppa;Pkm;Dld;Taldo1;Ppfia4;B4galt4;Hk2;Ppia;Dpysl4;Tpi1;Polr3k
22 prerank Bile Acid Metabolism 0.3696117390314927 1.20662816405311 0.17753120665742025 0.3670998306161339 0.998 18/57 23.27% Ttr;Rbp1;Bmp6;Pex6;Phyh;Cat;Cyp46a1;Abca2;Abcd2;Prdx5;Optn;Slc29a1;Pex19;Idi1;Nudt12;Dhcr24;Gclm;Lipe
23 prerank Apical Surface -0.3985625019149619 -1.200579777651937 0.22969187675070027 0.6155861165684349 0.834 2/20 2.65% Pkhd1;Crybg1
24 prerank Hedgehog Signaling -0.3537478281913628 -1.1829352572062866 0.22674418604651161 0.44968347522375024 0.862 4/27 8.33% Cdk6;Shh;Unc5c;Tle1
25 prerank Angiogenesis 0.45006017307214286 1.152849605704238 0.2857142857142857 0.4696167690027525 1.0 4/17 13.50% Spp1;Vtn;Lrpap1;Tnfrsf21
26 prerank Estrogen Response Early 0.3202577761175286 1.1457554653526965 0.24015247776365947 0.46539901449386945 1.0 18/103 13.09% Celsr2;Slc7a5;Endod1;Podxl;Siah2;Hes1;Slc1a4;Fkbp4;Slc26a2;Cant1;Ret;Syt12;Slc1a1;Unc119;Xbp1;Slc22a5;Tob1;Syngr1
27 prerank Inflammatory Response 0.343677014173989 1.1128593248951348 0.27034482758620687 0.5261164145854238 1.0 17/52 24.87% Slc7a1;Chst2;Slc31a1;P2rx4;Atp2a2;Ly6e;Pvr;Tapbp;Ldlr;Acvr1b;Itga5;Selenos;Adrm1;Ahr;Ptger4;Cx3cl1;Gabbr1
28 prerank Apical Junction -0.2356305465622273 -1.0356495552232514 0.33163265306122447 0.75 0.982 36/117 25.49% Col9a1;Egfr;Mmp9;Vav2;Itga10;Inppl1;Sorbs3;Map4k2;Epb41l2;Mpp5;Pik3r3;Jup;Cadm2;Lama3;Exoc4;Dlg1;Crat;Gnai1;Rsu1;Pik3cb;Actn1;Rasa1;Pbx2;Cdh11;Tjp1;Nf1;Baiap2;Pard6g;Akt3;Lima1;Wasl;Vcl;Ptk2;Adam9;Nf2;Itga9
29 prerank Notch Signaling 0.399847827918841 1.0197119168626385 0.4358974358974359 0.7571260321829346 1.0 8/17 21.85% Notch3;Hes1;Psenen;Dtx4;St3gal6;Rbx1;Psen2;Aph1a
30 prerank Estrogen Response Late 0.28712203186271557 1.006757252677638 0.463254593175853 0.7633897946220622 1.0 15/81 12.51% Celsr2;Slc7a5;Cox6c;Siah2;Ckb;Slc1a4;Fkbp4;Slc26a2;Fabp5;Ret;Mocs2;Xbp1;Slc22a5;Pcp4;Tob1
31 prerank Androgen Response 0.2884323946031561 1.0015961386380297 0.45611702127659576 0.7471538624407563 1.0 24/74 25.55% Dbi;Sat1;Slc26a2;Abcc4;Uap1;Ndrg1;Rab4a;Dnajb9;Pgm3;Pa2g4;Lifr;Srp19;Abhd2;Bmpr1b;Idi1;Adrm1;Dhcr24;Elovl5;Ube2j1;Lman1;Hmgcs1;Xrcc6;Sgk1;Iqgap2
32 prerank Fatty Acid Metabolism 0.2808233490668568 0.9984402645691206 0.45591939546599497 0.7281271320017879 1.0 31/97 22.90% Acadvl;Erp29;Mdh2;Reep6;Mif;Prdx6;Sdhd;Ncaph2;Gabarapl1;Blvra;Lgals1;Cbr1;Ech1;Hsdl2;Slc22a5;Ldha;Serinc1;Aldoa;Cpt1a;Odc1;Dld;Hadhb;Bmpr1b;Idi1;Hsp90aa1;Dhcr24;Eci2;Elovl5;Fasn;Idh3g;Hmgcs1
33 prerank Pperoxisome 0.29520251049695345 0.97821913372473 0.5116598079561042 0.757661973322041 1.0 15/65 21.45% Ttr;Pex6;Hras;Cat;Ech1;Cnbp;Nudt19;Pex2;Abcd2;Prdx5;Slc25a4;Idi1;Dhcr24;Eci2;Elovl5
34 prerank Myc Targets V2 0.3284418751910447 0.9696695222900162 0.5 0.7518508107792389 1.0 19/33 33.29% Tfb2m;Nolc1;Pa2g4;Rcl1;Rabepk;Rrp9;Hspe1;Hk2;Prmt3;Ndufaf4;Las1l;Mrto4;Pprc1;Phb;Cbx3;Hspd1;Npm1;Supv3l1;Mphosph10
35 prerank TNF-alpha Signaling via NF-kB 0.26011523232554457 0.894600418023324 0.6771752837326608 0.9148687275037052 1.0 8/73 5.95% Plpp3;Ier3;Sat1;Smad3;Hes1;Jun;Junb;Litaf
36 prerank Coagulation 0.2927301625154501 0.8806583834528166 0.6666666666666666 0.9175568426300942 1.0 4/39 6.01% Dct;Mmp15;Lgmn;Clu
37 prerank PI3K/AKT/mTOR Signaling 0.24582824201503325 0.8558574210392627 0.6987620357634112 0.9455043801609148 1.0 19/79 24.71% Ddit3;Hras;Calr;Gngt1;Csnk2b;Cdkn1b;Them4;Dusp3;Ralb;Arpc3;Ap2m1;Pdk1;Hsp90b1;Sqstm1;Pin1;Plcg1;Ripk1;Pla2g12a;Rps6ka1
38 prerank Cholesterol Homeostasis 0.2739145845269427 0.8547575112449235 0.6930555555555555 0.9192443169788077 1.0 20/43 28.29% Lgmn;Clu;Fabp5;Ech1;Abca2;Aldoc;Ldlr;Idi1;Pparg;Fasn;Sqle;Hmgcs1;Pmvk;Fdps;Pnrc1;Sc5d;Pcyt2;Acat2;Ctnnb1;Atf5
39 prerank G2-M Checkpoint 0.23397119797899435 0.8494276587838939 0.7564259485924113 0.9039120200271512 1.0 39/117 30.73% Slc7a5;Smad3;Slc7a1;Kif22;Jpt1;Nolc1;Pttg1;Chmp1a;Cdkn1b;Arid4a;E2f4;Lmnb1;Hspa8;Polq;Odc1;Lbr;Hira;Katna1;Meis2;Sqle;Ss18;Cdc25b;Ncl;Nsd2;Nasp;Pml;Tmpo;Smc2;G3bp1;Orc6;Atf5;Hmgn2;Prmt5;Upf1;Nup50;Uck2;Tle3;Brca2;Odf2
40 prerank Pancreas Beta Cells -0.27556487299059124 -0.8479661202906396 0.648876404494382 1.0 1.0 3/20 5.53% Foxo1;Mafb;Slc2a2
41 prerank E2F Targets 0.23035715984259164 0.8269813074733565 0.784409257003654 0.9224857082362905 1.0 17/108 11.77% Prdx4;Prps1;Nme1;Spag5;Ncapd2;Ran;Kif22;Jpt1;Pole4;Lyar;Nolc1;Mms22l;Pttg1;Pa2g4;Cdkn1b;Dut;Phf5a
42 prerank Interferon Gamma Response 0.23937713363156826 0.8015886390583518 0.803840877914952 0.9457706965911498 1.0 17/62 25.09% Gpr18;Rnf213;Casp3;Bpgm;Casp7;Ifi27;Psme2;Rnf31;Ly6e;Cmtr1;Tapbp;Tor1b;Trim26;Mthfd2;Slc25a28;Ripk1;Psma2
43 prerank Protein Secretion 0.2216951127676127 0.7759364123245033 0.871536523929471 0.9645524203008853 1.0 9/85 11.32% Cd63;Copb2;Vamp7;Cope;Vamp3;Ap2s1;Ergic3;Rer1;Tmx1
44 prerank Allograft Rejection 0.23999552665871182 0.7643178683577514 0.8465829846582985 0.9564574813064844 1.0 9/51 14.75% Nme1;Rps9;Rpl39;Csk;Rps19;Eif3d;Degs1;Flna;Eif5a
45 prerank KRAS Signaling Dn 0.22613743228164682 0.7504661223471343 0.8862433862433863 0.9500602615677779 1.0 7/61 10.85% Celsr2;Kcnmb1;Arhgdig;Entpd7;Chst2;Kcnd1;Sncb
46 prerank IL-6/JAK/STAT3 Signaling 0.2691815278013144 0.7328446756427207 0.8408736349453978 0.9478456489519373 1.0 11/23 37.58% Jun;Hax1;Tnfrsf21;Acvr1b;Ifngr1;Ptpn11;Grb2;Stat2;Csf2ra;Ptpn1;Ptpn2
47 prerank Interferon Alpha Response 0.2679035902985367 0.7317911311561915 0.8115501519756839 0.9259474910014821 1.0 7/23 23.06% Ifi27;Psme2;Rnf31;Ly6e;Cmtr1;Trim26;Slc25a28
48 prerank UV Response Dn 0.1840611345380614 0.6682328764160463 0.9852034525277436 0.9571776413296633 1.0 19/106 21.77% Pik3cd;Plpp3;Cited2;Smad3;Slc7a1;Bdnf;Synj2;Mta1;Cdkn1b;Anxa2;Col11a1;Tgfbr3;Dbp;Ldlr;Nek7;Smad7;Abcc1;Pparg;Pias3
49 prerank Mitotic Spindle -0.13654734956699302 -0.6225645742263682 1.0 1.0 1.0 47/143 31.64% Smc4;Mid1;Epb41l2;Alms1;Dock4;Arl8a;Dlg1;Stk38l;Nck2;Rasa1;Cdc42bpa;Tuba4a;Cd2ap;Kif1b;Wasf1;Clip2;Rapgef6;Nf1;Ssh2;Als2;Hook3;Ccdc88a;Pcgf5;Clip1;Wasl;Cdk5rap2;Rock1;Ckap5;Numa1;Vcl;Rasa2;Cttn;Tubgcp5;Fgd4;Pcnt;Incenp;Bin1;Sorbs2;Pafah1b1;Rictor;Fscn1;Cntrl;Rapgef5;Kif5b;Pcm1;Tiam1;Tbcd
50 prerank TGF-beta Signaling -0.17788089584593306 -0.6052933201645828 0.9689655172413794 1.0 1.0 5/37 13.38% Bcar3;Fnta;Cdk9;Hdac1;Klf10
51 prerank Wnt-beta Catenin Signaling -0.19113300492610838 -0.5890794535568823 0.9709302325581395 0.9900130975769482 1.0 23/23 80.93% Hdac2;Ncstn;Gnai1;Cul1;Nkd1;Axin1;Wnt5b;Numb;Ncor2;Rbpj;Dvl2;Ptch1;Adam17;Ppard;Skp2;Maml1;Hdac11;Csnk1e;Ctnnb1;Axin2;Psen2;Jag2;Hdac5
output/16_gsea/gsea_RGC2-like_Hallmark.csv
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Name,Term,ES,NES,NOM p-val,FDR q-val,FWER p-val,Tag %,Gene %,Lead_genes
prerank,Angiogenesis,0.53348658037593,1.403777958054414,0.07099143206854346,1.0,0.689,9/20,22.66%,Spp1;Vtn;Pdgfa;Lpl;Vav2;Pglyrp1;Vcan;Ccnd2;Tnfrsf21
prerank,Estrogen Response Early,0.40924452238115533,1.3651709930010778,0.024539877300613498,0.8729694537537089,0.812,37/122,23.81%,Slc1a1;Jak2;Siah2;Mreg;Fkbp5;Dynlt3;Fos;Syt12;Mybbp1a;Rasgrp1;Slc39a6;Slc22a5;Dhcr7;Svil;Med13l;Scarb1;Slc26a2;Klf10;Hspb8;Rapgefl1;B4galt1;Inhbb;Pdzk1;Sec14l2;Hr;Snx24;Gfra1;Ccnd1;Mast4;Mybl1;Nbl1;Elovl2;Itpk1;Flnb;Cbfa2t3;Slc2a1;Ncor2
prerank,Coagulation,0.4372011834902983,1.3281612715850242,0.0795964125560538,0.8330095843917433,0.893,22/46,28.04%,Plek;Sparc;Mmp15;Dct;Gng12;Htra1;Bmp1;Casp9;Furin;Wdr1;Cpq;P2ry1;Lrp1;Cd9;Sh2b2;Fbn1;Usp11;Lgmn;Lta4h;Msrb2;Apoa1;Timp3
prerank,Epithelial Mesenchymal Transition,0.40876654140559116,1.3276191057404756,0.045785639958376693,0.626806412363652,0.893,32/92,22.67%,Spp1;Sgcb;Tpm4;Calu;Fuca1;Mylk;Sparc;Col4a2;Matn2;Htra1;Pvr;Itga5;Pmp22;Bmp1;Igfbp2;Col16a1;Bdnf;Lama1;Tnfaip3;Col12a1;Grem1;Dcn;Vcan;Lrp1;Tpm1;Ppib;Vegfc;Fbn1;Edil3;Fermt2;Spock1;Colgalt1
prerank,Androgen Response,0.39425579008851097,1.2729752981059004,0.09653725078698845,0.8200994727517236,0.965,20/79,21.18%,Pdlim5;Idi1;Fkbp5;Bmpr1b;Abcc4;Plpp1;Srp19;Pa2g4;Zbtb10;Iqgap2;Spcs3;Maf;Slc26a2;Srf;B4galt1;Ccnd3;Ccnd1;Pias1;Ube2i;Map7
prerank,Cholesterol Homeostasis,0.4050281342028105,1.2587348683162332,0.12039045553145336,0.7787051465408671,0.974,19/55,25.01%,Sqle;Errfi1;Idi1;Mvk;Tm7sf2;Dhcr7;Ebp;Fads2;Lpl;Fbxo6;Nfil3;Cd9;Trib3;Lgmn;Gldc;Atxn2;Atf5;Gnai1;Mal2
prerank,Unfolded Protein Response,0.3796960341384746,1.254761091433047,0.09573361082206035,0.6927841087552489,0.979,26/95,24.27%,Asns;Arfgap1;Sec11a;Exosc4;Cebpg;Imp3;Nfyb;Aldh18a1;Dkc1;Gosr2;Hyou1;Eif4a3;Spcs3;Serp1;Banf1;Yif1a;Nfya;Cebpb;Xpot;Dcp1a;Cnot6;Psat1;Preb;Parn;Nabp1;Eif2s1
prerank,Wnt-beta Catenin Signaling,0.4542121440679544,1.2539740081458988,0.15421686746987953,0.6104126198048968,0.979,13/26,28.77%,Adam17;Ncstn;Axin2;Wnt5b;Maml1;Frat1;Ccnd2;Ppard;Gnai1;Ncor2;Hey1;Numb;Hdac11
prerank,Complement,0.35553928599317514,1.1816125363443366,0.1640706126687435,0.9610860887570175,1.0,32/104,23.44%,Usp8;Dgkg;Xpnpep1;Jak2;Plek;Rnf4;Rasgrp1;Col4a2;Mmp15;Fdx1;Gng2;Prdm4;Casp9;Prss36;Prkcd;Gnb4;Tnfaip3;Kcnip2;Rce1;Cebpb;Cpq;Dock10;Lrp1;Zfpm2;Hspa1a;Casp7;Lgmn;Pla2g7;Gngt2;Psen1;Timp2;Lta4h
prerank,TNF-alpha Signaling via NF-kB,0.3577631425361456,1.1734454781575867,0.18039624608967675,0.916003159220441,1.0,30/99,21.34%,Slc16a6;Plek;Rcan1;Pdlim5;Phlda1;Btg2;Fos;Bcl6;Ripk2;Zbtb10;Nfkbia;Nr4a3;Klf10;B4galt1;Nfil3;Tnfaip3;Snn;Cflar;Mcl1;Plk2;Cebpb;Dnajb4;Ccnd1;Sik1;Mxd1;Tnip1;Smad3;Rel;Kdm6b;Slc2a3
prerank,Estrogen Response Late,0.3528414770924949,1.1620970171698506,0.20841889117043122,0.8992367804671687,1.0,27/100,25.60%,Atp2b4;Jak2;Siah2;Idh2;Fkbp5;Dynlt3;Fos;Mettl3;Slc22a5;Dhcr7;Scarb1;Slc26a2;Hspb8;Rapgefl1;Pdzk1;Hr;Mdk;Ccnd1;Cd9;Nbl1;Plk4;Itpk1;Flnb;Snx10;Ncor2;Igfbp4;Nrip1
prerank,IL-2/STAT5 Signaling,0.3486153076699693,1.146962989292761,0.20956256358087488,0.9066962665698978,1.0,37/108,29.33%,Spp1;Mapkapk2;Prkch;Phlda1;Plpp1;Coch;Dcps;Spry4;Etv4;Furin;Arl4a;Nfil3;Eomes;Eno3;Ifngr1;Ccnd3;Col6a1;Slc29a2;Umps;Pou2f1;Pnp;Nop2;Rgs16;Mxd1;Ccnd2;Slc2a3;P4ha1;Socs2;Tnfrsf21;Snx14;Cdc42se2;Plec;Lrrc8c;Prnp;Adam19;Ctsz;Ahr
prerank,TGF-beta Signaling,0.3754677673816051,1.1281299248736145,0.31077981651376146,0.9469779691992428,1.0,12/40,29.35%,Skil;Smad1;Klf10;Furin;Smad3;Ppp1ca;Hdac1;Ncor2;Id3;Smad7;Id1;Tjp1
prerank,DNA Repair,0.3328337211604492,1.1167079504371535,0.25,0.9459364679549537,1.0,26/127,18.15%,Pola1;Taf6;Ell;Gtf2f1;Polr1d;Gtf2h1;Snapc4;Sec61a1;Ncbp2;Tmed2;Rfc2;Vps37b;Rad51;Ercc8;Pole4;Pold3;Taf12;Tyms;Vps37d;Polr2d;Ercc1;Aaas;Bcam;Umps;Pnp;Ddb1
prerank,UV Response Dn,0.31760519297212486,1.0583673198146495,0.3874614594039055,1.0,1.0,55/111,44.91%,Atp2b4;Sdc2;Pdlim5;Rnd3;Igfbp5;Pmp22;Ltbp1;Mgmt;Bdnf;Vav2;Runx1;Prkca;Bckdhb;Smad3;Syne1;Nr3c1;Dbp;Slc7a1;Magi2;Smad7;Id1;Mmp16;Dlc1;Scaf8;Tjp1;Celf2;Anxa2;Ptprm;Zmiz1;Amph;Kalrn;Add3;Kcnma1;Ythdc1;Arhgef9;Rbpms;Dlg1;Adgrl2;Nfib;Insig1;Tgfbr3;Sri;Irs1;Plcb4;Col11a1;Atxn1;Cap2;Schip1;Mios;Cacna1a;Pik3r3;Aggf1;Nek7;Akt3;Mta1
prerank,Myogenesis,0.3201305797487376,1.0534554124295128,0.37743589743589745,1.0,1.0,39/101,33.95%,Kcnh1;Flii;Myh9;Mylk;Cacna1h;Sparc;Igf1;Igfbp7;Col4a2;Svil;Hspb8;Ephb3;Eno3;Nos1;Cox6a2;Gaa;Dmd;Acsl1;Kifc3;Pick1;Gpx3;Sh2b1;Spdef;Cryab;Rb1;Dmpk;Prnp;Mef2a;Adam12;Ppfia4;Sorbs1;Speg;Mapk12;Mapre3;Agl;Cdh13;Rit1;Mras;Fabp3
prerank,Apoptosis,0.3130421309343167,1.0202804114624435,0.4786680541103018,1.0,1.0,26/91,23.31%,Ddit3;Dffa;Tgfb2;Btg2;Hmgb2;Hspb1;Cdc25b;Ebp;Dpyd;Gstm1;Casp9;Mgmt;Dap;Ifngr1;Casp2;Cflar;Mcl1;Dcn;Ccnd1;Nefh;Casp7;Ccnd2;Bax;Psen1;Timp2;Gpx3
prerank,Hypoxia,0.3026639944627072,1.011669902518209,0.47443762781186094,1.0,1.0,36/127,26.77%,Ddit3;Errfi1;Xpnpep1;Sdc2;Siah2;Ndst1;Myh9;Fos;Slc37a4;Gapdhs;Scarb1;Tpst2;Efna3;Ncan;Hs3st1;Ndst2;Nfil3;Eno3;Tnfaip3;Dcn;Gcnt2;Prkca;Map3k1;Csrp2;Dpysl4;Gaa;Slc2a3;Klhl24;Nr3c1;P4ha1;Slc2a1;Mxi1;Btg1;Pgm2;Hk1;Nedd4l
prerank,KRAS Signaling Dn,0.3125851692675281,0.9921374254438956,0.505933117583603,1.0,1.0,21/71,24.25%,Cntfr;Skil;Tgfb2;Bmpr1b;Btg2;Slc38a3;Mast3;Sidt1;Igfbp2;Cacna1f;Gpr19;Snn;Kcnn1;Slc16a7;Nos1;Rgs11;Tnni3;Kcnmb1;Dtnb;Thrb;Htr1d
prerank,Hedgehog Signaling,0.35214766248193635,0.9917514589053601,0.5065868263473053,1.0,1.0,3/30,4.21%,L1cam;Cntfr;Myh9
prerank,UV Response Up,0.29950708861737435,0.9916384101546034,0.5231243576567317,1.0,1.0,41/105,33.32%,Asns;Cebpg;Maoa;Rxrb;Dnajb1;Btg2;Fos;Rasgrp1;Cnp;Clcn2;Ppat;Nfkbia;Igfbp2;Furin;Prkcd;Ccnd3;Ggh;Tgfbrap1;Lhx2;Ephx1;Tyro3;Plcl1;Mark2;Stk25;Pole3;Gpx3;E2f5;Dgat1;Btg1;Prpf3;Lyn;Sod2;Eif5;Psmc3;Bsg;Hnrnpu;Stip1;Tfrc;Pdap1;Gls;Rab27a
prerank,Interferon Gamma Response,0.3097780425657622,0.9889580641917307,0.5176470588235295,1.0,1.0,27/74,29.77%,Jak2;Gpr18;Tapbp;Slc25a28;Ripk2;Nfkbia;Lats2;Mvp;Eif2ak2;Arl4a;Psme2;Tnfaip3;Pnp;Parp12;Casp7;Trim14;St8sia4;Stat2;Rapgef6;P2ry14;Ifi35;Btg1;Bpgm;St3gal5;Hif1a;Sod2;Pfkp
prerank,Inflammatory Response,0.31399487263650533,0.9833488916594735,0.520855614973262,1.0,1.0,11/63,9.81%,Slc4a4;Btg2;Rasgrp1;Tapbp;Kcnmb2;Ripk2;Nfkbia;Pvr;Slc31a1;Eif2ak2;Itga5
prerank,Fatty Acid Metabolism,0.2828826384022362,0.9306847687853264,0.6642636457260556,1.0,1.0,25/111,24.24%,Prdx6;Reep6;Maoa;Idi1;Kmt5a;Bmpr1b;Alad;Ostc;Gapdhs;Slc22a5;Hadhb;Acot8;Nbn;Eci1;Eno3;Cpt1a;Bckdhb;Ehhadh;Ephx1;Ccdc58;Acaa2;Acsl1;Hsph1;Erp29;Idh3g
prerank,Apical Junction,0.2766828367773016,0.9275540805241054,0.6646525679758308,1.0,1.0,39/131,30.57%,Pik3cb;Gtf2f1;Pbx2;Myh9;Map3k20;Baiap2;Nexn;Col9a1;Epb41l2;Lima1;Arhgef6;Bmp1;Inppl1;Col16a1;Nectin3;B4galt1;Vav2;Amigo2;Rhof;Mdk;Vcan;Msn;Itga9;Fbn1;Jam3;Tro;Gnai1;Mpp5;Vasp;Skap2;Pcdh1;Tspan4;Thbs3;Cdh6;Actn1;Cadm3;Tjp1;Map4k2;Speg
prerank,Allograft Rejection,0.2924310850804264,0.9159129454727992,0.6414686825053996,1.0,1.0,14/59,20.61%,Jak2;Tgfb2;Csk;Tapbp;Ripk2;Ifngr1;Ccnd3;Inhbb;Galnt1;Ube2d1;St8sia4;Ccnd2;Mrpl3;Ube2n
prerank,p53 Pathway,0.2754205407197828,0.9144637542699577,0.6837782340862423,1.0,1.0,27/127,19.90%,Ddit3;Nupr1;Sec61a1;Ralgds;Ctsf;Btg2;Fuca1;Fos;Dnttip2;Pdgfa;Csrnp2;Hdac3;Baiap2;Rap2b;Abhd4;Nol8;Rad51c;Stom;Ccnd3;Inhbb;Plk2;Rgs16;Mxd1;Ephx1;Trib3;Fgf13;Ccnd2
prerank,heme Metabolism,0.2708913499628705,0.9049291665440555,0.7029501525940997,1.0,1.0,43/139,34.37%,Ezh1;Adipor1;Asns;Agpat4;Alad;Btg2;Aldh6a1;Khnyn;Ppox;Lrp10;Fbxo7;Narf;Ccnd3;Tns1;Slc25a38;Fbxo34;Bcam;Abcg2;Cast;Daam1;Pigq;Rbm5;Nr3c1;Slc2a1;Mxi1;Htatip2;Glrx5;Bpgm;Btrc;Snca;Mgst3;Sec14l1;Dcaf11;Bsg;Dcaf10;Prdx2;Tfrc;Synj1;Sdcbp;Gmps;Blvra;Ncoa4;Dmtn
prerank,Mitotic Spindle,0.26485815216350855,0.8957356918283002,0.7439271255060729,1.0,1.0,49/155,33.17%,Pdlim5;Pcnt;Tubgcp2;Hdac6;Arhgap10;Stk38l;Myh9;Cntrob;Cep192;Epb41l2;Dock2;Llgl1;Kptn;Alms1;Fgd6;Sass6;Pxn;Cttn;Arhgap4;Rhof;Katna1;Arhgap27;Rfc1;Cep131;Nin;Arhgap5;Wasf2;Mark4;Itsn1;Flnb;Rapgef6;Kif15;Rictor;Palld;Als2;Lmnb1;Tbcd;Cdc42ep4;Cd2ap;Ckap5;Bcr;Trio;Rhot2;Lrpprc;Pkd2;Smc3;Myo9b;Akap13;Vcl
prerank,KRAS Signaling Up,0.27537769009269786,0.8919075814296813,0.7061909758656874,1.0,1.0,19/89,21.54%,Spp1;Btbd3;Adam17;Fuca1;Gadd45g;Dock2;Etv4;Gpnmb;Fbxo4;Tnfaip3;Gng11;Adam8;Map3k1;Rgs16;Nin;Sox9;Ccnd2;Map7;Sdccag8
prerank,E2F Targets,0.2570713068862008,0.8538255738753947,0.8034623217922607,1.0,1.0,22/126,16.00%,Cse1l;Nup153;Rfc2;Ezh2;Jpt1;Hmgb2;Prim2;Pole4;Ubr7;Pa2g4;Asf1a;Pold3;Cdc25b;Slbp;Nbn;Zw10;Diaph3;Wdr90;Rad51c;Pms2;Dck;Prdx4
prerank,mTORC1 Signaling,0.25133482688401626,0.8492788489434102,0.8272727272727273,1.0,1.0,32/163,23.59%,Sqle;Asns;Ddit3;Sec11a;Nupr1;Gtf2h1;Idi1;Btg2;Slc37a4;Tm7sf2;Dhcr7;Ebp;Igfbp5;Fads2;Serp1;Nfyc;Dhfr;Nfil3;Pno1;Pnp;Arpc5l;Cd9;Srd5a1;Trib3;Lgmn;Prdx1;Slc2a3;Psat1;P4ha1;Lta4h;Slc2a1;Hspd1
prerank,Myc Targets V2,0.2751457839226612,0.840684251835348,0.7600872410032715,1.0,1.0,17/48,27.95%,Ipo4;Mybbp1a;Pa2g4;Gnl3;Tfb2m;Bysl;Slc29a2;Grwd1;Nop2;Srm;Cdk4;Plk4;Las1l;Hspd1;Prmt3;Exosc5;Tcof1
prerank,IL-6/JAK/STAT3 Signaling,0.30441231509690597,0.8367708653615702,0.7283511269276394,1.0,1.0,8/28,22.66%,Itga4;Cntfr;Crlf2;Ifngr1;Hax1;Cd9;Stat2;Tnfrsf21
prerank,Glycolysis,0.2380826941011096,0.8025900339756658,0.8701825557809331,1.0,1.0,40/138,31.76%,Pygb;Sdc2;Aldh7a1;Cacna1h;Xylt2;Rpe;Slc37a4;Slc16a3;Gapdhs;Gne;Chpf;Efna3;B4galt1;Chst12;Dcn;Hax1;Vcan;Gal3st1;Dpysl4;Sox9;Cyb5a;Srd5a3;P4ha1;Mxi1;Phka2;Spag4;Nasp;Galk2;Pgm2;Pfkfb1;Polr3k;Glce;Ppfia4;Me1;Hs6st2;Pfkp;Pam;Ext2;Agl;Ppp2cb
prerank,PI3K/AKT/mTOR Signaling,-0.16989797724619446,-0.792994106446046,0.98,1.0,0.975635593220339,24/80,24.10%,Cdk1;Arpc3;Akt1s1;Nck1;Grk2;Camk4;Arhgdia;Pla2g12a;Prkaa2;Mknk2;Ripk1;Tbk1;Them4;Cfl1;Mapk10;Ptpn11;Ppp2r1b;Cltc;Dusp3;Actr2;Hras;Rps6ka1;Ap2m1;Ywhab
prerank,Interferon Alpha Response,0.2617277618471125,0.7651586429678577,0.8281068524970964,1.0,1.0,12/35,26.52%,Slc25a28;Cnp;Ripk2;Eif2ak2;Psme2;Parp12;Parp9;Trim14;Stat2;Ifi35;Mvb12a;Cd47
prerank,Reactive Oxygen Species Pathway,0.25940746961504046,0.7577542810043145,0.8406779661016949,1.0,1.0,13/39,31.19%,Prdx6;Sbno2;Oxsr1;Prdx4;Msra;Scaf4;Stk25;Prdx1;Gpx3;Prnp;Sod2;Pfkp;Prdx2
prerank,Adipogenesis,0.22271305953928516,0.7501553993349617,0.9516129032258065,1.0,1.0,42/160,29.77%,Idh3a;Lipe;Reep6;Uqcrc1;Esyt1;Apoe;Mylk;Bcl6;Dram2;Dhcr7;Elmod3;Scarb1;Lama4;Lpl;Atp1b3;Hspb8;Stom;Arl4a;Ifngr1;Rab34;Samm50;Acaa2;Sdhb;Uck1;Mrpl15;Map4k3;Itsn1;Preb;Lifr;Nabp1;Gpx3;Idh3g;Angpt1;Dgat1;Miga2;Ephx2;Cpt2;Aifm1;Mgst3;Scp2;Me1;Sorbs1
prerank,Xenobiotic Metabolism,0.22581485882348362,0.7379857783743307,0.9214876033057852,1.0,1.0,17/99,21.09%,Vtn;Ssr3;Maoa;Pdlim5;Ptgds;Tgfb2;Apoe;Igf1;Angptl3;Hes6;Gcnt2;Spint2;Ephx1;Gstt2;Cyb5a;Ppard;Ndrg2
prerank,Bile Acid Metabolism,0.23383489917747427,0.724797364594835,0.9122426868905742,1.0,1.0,24/64,37.65%,Lipe;Abca4;Idi1;Idh2;Cyp7b1;Fads2;Pex26;Pex6;Bmp6;Acsl1;Slc35b2;Dio2;Ephx2;Apoa1;Scp2;Pnpla8;Ttr;Abcd2;Rbp1;Abca5;Fdxr;Pex16;Dhcr24;Aldh9a1
prerank,Apical Surface,0.26397818929785216,0.6967759042203997,0.8865853658536585,1.0,1.0,4/22,16.69%,Sulf2;Hspb1;B4galt1;Atp6v0a4
prerank,Notch Signaling,-0.20281253351939904,-0.6597894207194606,0.9425837320574163,0.9739930225182366,0.9936440677966102,6/19,17.91%,St3gal6;Fzd1;Psenen;Fzd7;Psen2;Rbx1
prerank,Spermatogenesis,0.19400301723491742,0.6220541233712112,0.9756613756613757,1.0,1.0,12/68,21.45%,Tle4;Ezh2;Gapdhs;Gsg1;Arl4a;Phf7;Oaz3;Nos1;Nefh;Jam3;Map7;Tnni3
prerank,G2-M Checkpoint,0.17817142758089224,0.5929783880466872,0.9969418960244648,1.0,1.0,43/131,35.86%,Sqle;Kmt5a;Ezh2;Jpt1;Map3k20;Dkc1;Prim2;Polq;Cdc25b;Ccnt1;Katna1;Ccnd1;Cdk4;Plk4;Smad3;Dmd;Cul5;Tmpo;Mtf2;Atf5;Hmgb3;Slc7a1;E2f3;Wrn;Kif15;Nasp;Lmnb1;Gins2;Hif1a;Slc38a1;Slc7a5;Srsf10;Hnrnpu;Cbx1;Pura;Nup50;Meis1;Lbr;Tle3;Gspt1;Ythdc1;Cul4a;Syncrip
prerank,Pperoxisome,0.18593307537957096,0.5906854012319501,0.9904458598726115,1.0,1.0,15/73,23.88%,Idi1;Idh2;Abcb9;Pex5;Acot8;Mvp;Ercc1;Pex6;Ehhadh;Cnbp;Prdx1;Acsl1;Slc35b2;Cdk7;Cadm1
prerank,Pancreas Beta Cells,0.22680571272756017,0.5899446538939727,0.9673776662484316,1.0,1.0,1/19,1.33%,Sec11a
prerank,Myc Targets V1,0.1581551111635115,0.5409489942964322,1.0,1.0,1.0,23/179,21.40%,Tardbp;Snrpd2;Rps6;Ncbp2;Prpf31;Pa2g4;Txnl4a;Tyms;Pwp1;Tufm;Lsm2;Ruvbl2;Mrpl9;Gnl3;Prdx4;Prps2;Srm;Cdk4;Cnbp;Hddc2;Psmd8;Xpot;Pole3
prerank,Oxidative Phosphorylation,0.15577552122156502,0.5261791902085622,1.0,1.0,1.0,13/166,10.65%,Mfn2;Idh3a;Mrps15;Uqcrc1;Idh2;Aldh6a1;Ndufs8;Mrpl35;Hadhb;Tomm22;Fdx1;Acat1;Eci1
prerank,Protein Secretion,0.11372226561933299,0.36484533819846215,1.0,0.9999786539159391,1.0,31/85,43.12%,Tmed2;Gosr2;Zw10;Cln5;Tsg101;Copb1;Stx16;Vamp4;Rab22a;Vps45;Stx7;Lman1;Sgms1;Pam;Vamp7;M6pr;Sec24d;Atp6v1h;Dst;Snx2;Cope;Gbf1;Arfgef1;Ppt1;Arfgef2;Arcn1;Tpd52;Rps6ka3;Rab14;Arfgap3;Galc
1 Name Term ES NES NOM p-val FDR q-val FWER p-val Tag % Gene % Lead_genes
2 prerank Angiogenesis 0.53348658037593 1.403777958054414 0.07099143206854346 1.0 0.689 9/20 22.66% Spp1;Vtn;Pdgfa;Lpl;Vav2;Pglyrp1;Vcan;Ccnd2;Tnfrsf21
3 prerank Estrogen Response Early 0.40924452238115533 1.3651709930010778 0.024539877300613498 0.8729694537537089 0.812 37/122 23.81% Slc1a1;Jak2;Siah2;Mreg;Fkbp5;Dynlt3;Fos;Syt12;Mybbp1a;Rasgrp1;Slc39a6;Slc22a5;Dhcr7;Svil;Med13l;Scarb1;Slc26a2;Klf10;Hspb8;Rapgefl1;B4galt1;Inhbb;Pdzk1;Sec14l2;Hr;Snx24;Gfra1;Ccnd1;Mast4;Mybl1;Nbl1;Elovl2;Itpk1;Flnb;Cbfa2t3;Slc2a1;Ncor2
4 prerank Coagulation 0.4372011834902983 1.3281612715850242 0.0795964125560538 0.8330095843917433 0.893 22/46 28.04% Plek;Sparc;Mmp15;Dct;Gng12;Htra1;Bmp1;Casp9;Furin;Wdr1;Cpq;P2ry1;Lrp1;Cd9;Sh2b2;Fbn1;Usp11;Lgmn;Lta4h;Msrb2;Apoa1;Timp3
5 prerank Epithelial Mesenchymal Transition 0.40876654140559116 1.3276191057404756 0.045785639958376693 0.626806412363652 0.893 32/92 22.67% Spp1;Sgcb;Tpm4;Calu;Fuca1;Mylk;Sparc;Col4a2;Matn2;Htra1;Pvr;Itga5;Pmp22;Bmp1;Igfbp2;Col16a1;Bdnf;Lama1;Tnfaip3;Col12a1;Grem1;Dcn;Vcan;Lrp1;Tpm1;Ppib;Vegfc;Fbn1;Edil3;Fermt2;Spock1;Colgalt1
6 prerank Androgen Response 0.39425579008851097 1.2729752981059004 0.09653725078698845 0.8200994727517236 0.965 20/79 21.18% Pdlim5;Idi1;Fkbp5;Bmpr1b;Abcc4;Plpp1;Srp19;Pa2g4;Zbtb10;Iqgap2;Spcs3;Maf;Slc26a2;Srf;B4galt1;Ccnd3;Ccnd1;Pias1;Ube2i;Map7
7 prerank Cholesterol Homeostasis 0.4050281342028105 1.2587348683162332 0.12039045553145336 0.7787051465408671 0.974 19/55 25.01% Sqle;Errfi1;Idi1;Mvk;Tm7sf2;Dhcr7;Ebp;Fads2;Lpl;Fbxo6;Nfil3;Cd9;Trib3;Lgmn;Gldc;Atxn2;Atf5;Gnai1;Mal2
8 prerank Unfolded Protein Response 0.3796960341384746 1.254761091433047 0.09573361082206035 0.6927841087552489 0.979 26/95 24.27% Asns;Arfgap1;Sec11a;Exosc4;Cebpg;Imp3;Nfyb;Aldh18a1;Dkc1;Gosr2;Hyou1;Eif4a3;Spcs3;Serp1;Banf1;Yif1a;Nfya;Cebpb;Xpot;Dcp1a;Cnot6;Psat1;Preb;Parn;Nabp1;Eif2s1
9 prerank Wnt-beta Catenin Signaling 0.4542121440679544 1.2539740081458988 0.15421686746987953 0.6104126198048968 0.979 13/26 28.77% Adam17;Ncstn;Axin2;Wnt5b;Maml1;Frat1;Ccnd2;Ppard;Gnai1;Ncor2;Hey1;Numb;Hdac11
10 prerank Complement 0.35553928599317514 1.1816125363443366 0.1640706126687435 0.9610860887570175 1.0 32/104 23.44% Usp8;Dgkg;Xpnpep1;Jak2;Plek;Rnf4;Rasgrp1;Col4a2;Mmp15;Fdx1;Gng2;Prdm4;Casp9;Prss36;Prkcd;Gnb4;Tnfaip3;Kcnip2;Rce1;Cebpb;Cpq;Dock10;Lrp1;Zfpm2;Hspa1a;Casp7;Lgmn;Pla2g7;Gngt2;Psen1;Timp2;Lta4h
11 prerank TNF-alpha Signaling via NF-kB 0.3577631425361456 1.1734454781575867 0.18039624608967675 0.916003159220441 1.0 30/99 21.34% Slc16a6;Plek;Rcan1;Pdlim5;Phlda1;Btg2;Fos;Bcl6;Ripk2;Zbtb10;Nfkbia;Nr4a3;Klf10;B4galt1;Nfil3;Tnfaip3;Snn;Cflar;Mcl1;Plk2;Cebpb;Dnajb4;Ccnd1;Sik1;Mxd1;Tnip1;Smad3;Rel;Kdm6b;Slc2a3
12 prerank Estrogen Response Late 0.3528414770924949 1.1620970171698506 0.20841889117043122 0.8992367804671687 1.0 27/100 25.60% Atp2b4;Jak2;Siah2;Idh2;Fkbp5;Dynlt3;Fos;Mettl3;Slc22a5;Dhcr7;Scarb1;Slc26a2;Hspb8;Rapgefl1;Pdzk1;Hr;Mdk;Ccnd1;Cd9;Nbl1;Plk4;Itpk1;Flnb;Snx10;Ncor2;Igfbp4;Nrip1
13 prerank IL-2/STAT5 Signaling 0.3486153076699693 1.146962989292761 0.20956256358087488 0.9066962665698978 1.0 37/108 29.33% Spp1;Mapkapk2;Prkch;Phlda1;Plpp1;Coch;Dcps;Spry4;Etv4;Furin;Arl4a;Nfil3;Eomes;Eno3;Ifngr1;Ccnd3;Col6a1;Slc29a2;Umps;Pou2f1;Pnp;Nop2;Rgs16;Mxd1;Ccnd2;Slc2a3;P4ha1;Socs2;Tnfrsf21;Snx14;Cdc42se2;Plec;Lrrc8c;Prnp;Adam19;Ctsz;Ahr
14 prerank TGF-beta Signaling 0.3754677673816051 1.1281299248736145 0.31077981651376146 0.9469779691992428 1.0 12/40 29.35% Skil;Smad1;Klf10;Furin;Smad3;Ppp1ca;Hdac1;Ncor2;Id3;Smad7;Id1;Tjp1
15 prerank DNA Repair 0.3328337211604492 1.1167079504371535 0.25 0.9459364679549537 1.0 26/127 18.15% Pola1;Taf6;Ell;Gtf2f1;Polr1d;Gtf2h1;Snapc4;Sec61a1;Ncbp2;Tmed2;Rfc2;Vps37b;Rad51;Ercc8;Pole4;Pold3;Taf12;Tyms;Vps37d;Polr2d;Ercc1;Aaas;Bcam;Umps;Pnp;Ddb1
16 prerank UV Response Dn 0.31760519297212486 1.0583673198146495 0.3874614594039055 1.0 1.0 55/111 44.91% Atp2b4;Sdc2;Pdlim5;Rnd3;Igfbp5;Pmp22;Ltbp1;Mgmt;Bdnf;Vav2;Runx1;Prkca;Bckdhb;Smad3;Syne1;Nr3c1;Dbp;Slc7a1;Magi2;Smad7;Id1;Mmp16;Dlc1;Scaf8;Tjp1;Celf2;Anxa2;Ptprm;Zmiz1;Amph;Kalrn;Add3;Kcnma1;Ythdc1;Arhgef9;Rbpms;Dlg1;Adgrl2;Nfib;Insig1;Tgfbr3;Sri;Irs1;Plcb4;Col11a1;Atxn1;Cap2;Schip1;Mios;Cacna1a;Pik3r3;Aggf1;Nek7;Akt3;Mta1
17 prerank Myogenesis 0.3201305797487376 1.0534554124295128 0.37743589743589745 1.0 1.0 39/101 33.95% Kcnh1;Flii;Myh9;Mylk;Cacna1h;Sparc;Igf1;Igfbp7;Col4a2;Svil;Hspb8;Ephb3;Eno3;Nos1;Cox6a2;Gaa;Dmd;Acsl1;Kifc3;Pick1;Gpx3;Sh2b1;Spdef;Cryab;Rb1;Dmpk;Prnp;Mef2a;Adam12;Ppfia4;Sorbs1;Speg;Mapk12;Mapre3;Agl;Cdh13;Rit1;Mras;Fabp3
18 prerank Apoptosis 0.3130421309343167 1.0202804114624435 0.4786680541103018 1.0 1.0 26/91 23.31% Ddit3;Dffa;Tgfb2;Btg2;Hmgb2;Hspb1;Cdc25b;Ebp;Dpyd;Gstm1;Casp9;Mgmt;Dap;Ifngr1;Casp2;Cflar;Mcl1;Dcn;Ccnd1;Nefh;Casp7;Ccnd2;Bax;Psen1;Timp2;Gpx3
19 prerank Hypoxia 0.3026639944627072 1.011669902518209 0.47443762781186094 1.0 1.0 36/127 26.77% Ddit3;Errfi1;Xpnpep1;Sdc2;Siah2;Ndst1;Myh9;Fos;Slc37a4;Gapdhs;Scarb1;Tpst2;Efna3;Ncan;Hs3st1;Ndst2;Nfil3;Eno3;Tnfaip3;Dcn;Gcnt2;Prkca;Map3k1;Csrp2;Dpysl4;Gaa;Slc2a3;Klhl24;Nr3c1;P4ha1;Slc2a1;Mxi1;Btg1;Pgm2;Hk1;Nedd4l
20 prerank KRAS Signaling Dn 0.3125851692675281 0.9921374254438956 0.505933117583603 1.0 1.0 21/71 24.25% Cntfr;Skil;Tgfb2;Bmpr1b;Btg2;Slc38a3;Mast3;Sidt1;Igfbp2;Cacna1f;Gpr19;Snn;Kcnn1;Slc16a7;Nos1;Rgs11;Tnni3;Kcnmb1;Dtnb;Thrb;Htr1d
21 prerank Hedgehog Signaling 0.35214766248193635 0.9917514589053601 0.5065868263473053 1.0 1.0 3/30 4.21% L1cam;Cntfr;Myh9
22 prerank UV Response Up 0.29950708861737435 0.9916384101546034 0.5231243576567317 1.0 1.0 41/105 33.32% Asns;Cebpg;Maoa;Rxrb;Dnajb1;Btg2;Fos;Rasgrp1;Cnp;Clcn2;Ppat;Nfkbia;Igfbp2;Furin;Prkcd;Ccnd3;Ggh;Tgfbrap1;Lhx2;Ephx1;Tyro3;Plcl1;Mark2;Stk25;Pole3;Gpx3;E2f5;Dgat1;Btg1;Prpf3;Lyn;Sod2;Eif5;Psmc3;Bsg;Hnrnpu;Stip1;Tfrc;Pdap1;Gls;Rab27a
23 prerank Interferon Gamma Response 0.3097780425657622 0.9889580641917307 0.5176470588235295 1.0 1.0 27/74 29.77% Jak2;Gpr18;Tapbp;Slc25a28;Ripk2;Nfkbia;Lats2;Mvp;Eif2ak2;Arl4a;Psme2;Tnfaip3;Pnp;Parp12;Casp7;Trim14;St8sia4;Stat2;Rapgef6;P2ry14;Ifi35;Btg1;Bpgm;St3gal5;Hif1a;Sod2;Pfkp
24 prerank Inflammatory Response 0.31399487263650533 0.9833488916594735 0.520855614973262 1.0 1.0 11/63 9.81% Slc4a4;Btg2;Rasgrp1;Tapbp;Kcnmb2;Ripk2;Nfkbia;Pvr;Slc31a1;Eif2ak2;Itga5
25 prerank Fatty Acid Metabolism 0.2828826384022362 0.9306847687853264 0.6642636457260556 1.0 1.0 25/111 24.24% Prdx6;Reep6;Maoa;Idi1;Kmt5a;Bmpr1b;Alad;Ostc;Gapdhs;Slc22a5;Hadhb;Acot8;Nbn;Eci1;Eno3;Cpt1a;Bckdhb;Ehhadh;Ephx1;Ccdc58;Acaa2;Acsl1;Hsph1;Erp29;Idh3g
26 prerank Apical Junction 0.2766828367773016 0.9275540805241054 0.6646525679758308 1.0 1.0 39/131 30.57% Pik3cb;Gtf2f1;Pbx2;Myh9;Map3k20;Baiap2;Nexn;Col9a1;Epb41l2;Lima1;Arhgef6;Bmp1;Inppl1;Col16a1;Nectin3;B4galt1;Vav2;Amigo2;Rhof;Mdk;Vcan;Msn;Itga9;Fbn1;Jam3;Tro;Gnai1;Mpp5;Vasp;Skap2;Pcdh1;Tspan4;Thbs3;Cdh6;Actn1;Cadm3;Tjp1;Map4k2;Speg
27 prerank Allograft Rejection 0.2924310850804264 0.9159129454727992 0.6414686825053996 1.0 1.0 14/59 20.61% Jak2;Tgfb2;Csk;Tapbp;Ripk2;Ifngr1;Ccnd3;Inhbb;Galnt1;Ube2d1;St8sia4;Ccnd2;Mrpl3;Ube2n
28 prerank p53 Pathway 0.2754205407197828 0.9144637542699577 0.6837782340862423 1.0 1.0 27/127 19.90% Ddit3;Nupr1;Sec61a1;Ralgds;Ctsf;Btg2;Fuca1;Fos;Dnttip2;Pdgfa;Csrnp2;Hdac3;Baiap2;Rap2b;Abhd4;Nol8;Rad51c;Stom;Ccnd3;Inhbb;Plk2;Rgs16;Mxd1;Ephx1;Trib3;Fgf13;Ccnd2
29 prerank heme Metabolism 0.2708913499628705 0.9049291665440555 0.7029501525940997 1.0 1.0 43/139 34.37% Ezh1;Adipor1;Asns;Agpat4;Alad;Btg2;Aldh6a1;Khnyn;Ppox;Lrp10;Fbxo7;Narf;Ccnd3;Tns1;Slc25a38;Fbxo34;Bcam;Abcg2;Cast;Daam1;Pigq;Rbm5;Nr3c1;Slc2a1;Mxi1;Htatip2;Glrx5;Bpgm;Btrc;Snca;Mgst3;Sec14l1;Dcaf11;Bsg;Dcaf10;Prdx2;Tfrc;Synj1;Sdcbp;Gmps;Blvra;Ncoa4;Dmtn
30 prerank Mitotic Spindle 0.26485815216350855 0.8957356918283002 0.7439271255060729 1.0 1.0 49/155 33.17% Pdlim5;Pcnt;Tubgcp2;Hdac6;Arhgap10;Stk38l;Myh9;Cntrob;Cep192;Epb41l2;Dock2;Llgl1;Kptn;Alms1;Fgd6;Sass6;Pxn;Cttn;Arhgap4;Rhof;Katna1;Arhgap27;Rfc1;Cep131;Nin;Arhgap5;Wasf2;Mark4;Itsn1;Flnb;Rapgef6;Kif15;Rictor;Palld;Als2;Lmnb1;Tbcd;Cdc42ep4;Cd2ap;Ckap5;Bcr;Trio;Rhot2;Lrpprc;Pkd2;Smc3;Myo9b;Akap13;Vcl
31 prerank KRAS Signaling Up 0.27537769009269786 0.8919075814296813 0.7061909758656874 1.0 1.0 19/89 21.54% Spp1;Btbd3;Adam17;Fuca1;Gadd45g;Dock2;Etv4;Gpnmb;Fbxo4;Tnfaip3;Gng11;Adam8;Map3k1;Rgs16;Nin;Sox9;Ccnd2;Map7;Sdccag8
32 prerank E2F Targets 0.2570713068862008 0.8538255738753947 0.8034623217922607 1.0 1.0 22/126 16.00% Cse1l;Nup153;Rfc2;Ezh2;Jpt1;Hmgb2;Prim2;Pole4;Ubr7;Pa2g4;Asf1a;Pold3;Cdc25b;Slbp;Nbn;Zw10;Diaph3;Wdr90;Rad51c;Pms2;Dck;Prdx4
33 prerank mTORC1 Signaling 0.25133482688401626 0.8492788489434102 0.8272727272727273 1.0 1.0 32/163 23.59% Sqle;Asns;Ddit3;Sec11a;Nupr1;Gtf2h1;Idi1;Btg2;Slc37a4;Tm7sf2;Dhcr7;Ebp;Igfbp5;Fads2;Serp1;Nfyc;Dhfr;Nfil3;Pno1;Pnp;Arpc5l;Cd9;Srd5a1;Trib3;Lgmn;Prdx1;Slc2a3;Psat1;P4ha1;Lta4h;Slc2a1;Hspd1
34 prerank Myc Targets V2 0.2751457839226612 0.840684251835348 0.7600872410032715 1.0 1.0 17/48 27.95% Ipo4;Mybbp1a;Pa2g4;Gnl3;Tfb2m;Bysl;Slc29a2;Grwd1;Nop2;Srm;Cdk4;Plk4;Las1l;Hspd1;Prmt3;Exosc5;Tcof1
35 prerank IL-6/JAK/STAT3 Signaling 0.30441231509690597 0.8367708653615702 0.7283511269276394 1.0 1.0 8/28 22.66% Itga4;Cntfr;Crlf2;Ifngr1;Hax1;Cd9;Stat2;Tnfrsf21
36 prerank Glycolysis 0.2380826941011096 0.8025900339756658 0.8701825557809331 1.0 1.0 40/138 31.76% Pygb;Sdc2;Aldh7a1;Cacna1h;Xylt2;Rpe;Slc37a4;Slc16a3;Gapdhs;Gne;Chpf;Efna3;B4galt1;Chst12;Dcn;Hax1;Vcan;Gal3st1;Dpysl4;Sox9;Cyb5a;Srd5a3;P4ha1;Mxi1;Phka2;Spag4;Nasp;Galk2;Pgm2;Pfkfb1;Polr3k;Glce;Ppfia4;Me1;Hs6st2;Pfkp;Pam;Ext2;Agl;Ppp2cb
37 prerank PI3K/AKT/mTOR Signaling -0.16989797724619446 -0.792994106446046 0.98 1.0 0.975635593220339 24/80 24.10% Cdk1;Arpc3;Akt1s1;Nck1;Grk2;Camk4;Arhgdia;Pla2g12a;Prkaa2;Mknk2;Ripk1;Tbk1;Them4;Cfl1;Mapk10;Ptpn11;Ppp2r1b;Cltc;Dusp3;Actr2;Hras;Rps6ka1;Ap2m1;Ywhab
38 prerank Interferon Alpha Response 0.2617277618471125 0.7651586429678577 0.8281068524970964 1.0 1.0 12/35 26.52% Slc25a28;Cnp;Ripk2;Eif2ak2;Psme2;Parp12;Parp9;Trim14;Stat2;Ifi35;Mvb12a;Cd47
39 prerank Reactive Oxygen Species Pathway 0.25940746961504046 0.7577542810043145 0.8406779661016949 1.0 1.0 13/39 31.19% Prdx6;Sbno2;Oxsr1;Prdx4;Msra;Scaf4;Stk25;Prdx1;Gpx3;Prnp;Sod2;Pfkp;Prdx2
40 prerank Adipogenesis 0.22271305953928516 0.7501553993349617 0.9516129032258065 1.0 1.0 42/160 29.77% Idh3a;Lipe;Reep6;Uqcrc1;Esyt1;Apoe;Mylk;Bcl6;Dram2;Dhcr7;Elmod3;Scarb1;Lama4;Lpl;Atp1b3;Hspb8;Stom;Arl4a;Ifngr1;Rab34;Samm50;Acaa2;Sdhb;Uck1;Mrpl15;Map4k3;Itsn1;Preb;Lifr;Nabp1;Gpx3;Idh3g;Angpt1;Dgat1;Miga2;Ephx2;Cpt2;Aifm1;Mgst3;Scp2;Me1;Sorbs1
41 prerank Xenobiotic Metabolism 0.22581485882348362 0.7379857783743307 0.9214876033057852 1.0 1.0 17/99 21.09% Vtn;Ssr3;Maoa;Pdlim5;Ptgds;Tgfb2;Apoe;Igf1;Angptl3;Hes6;Gcnt2;Spint2;Ephx1;Gstt2;Cyb5a;Ppard;Ndrg2
42 prerank Bile Acid Metabolism 0.23383489917747427 0.724797364594835 0.9122426868905742 1.0 1.0 24/64 37.65% Lipe;Abca4;Idi1;Idh2;Cyp7b1;Fads2;Pex26;Pex6;Bmp6;Acsl1;Slc35b2;Dio2;Ephx2;Apoa1;Scp2;Pnpla8;Ttr;Abcd2;Rbp1;Abca5;Fdxr;Pex16;Dhcr24;Aldh9a1
43 prerank Apical Surface 0.26397818929785216 0.6967759042203997 0.8865853658536585 1.0 1.0 4/22 16.69% Sulf2;Hspb1;B4galt1;Atp6v0a4
44 prerank Notch Signaling -0.20281253351939904 -0.6597894207194606 0.9425837320574163 0.9739930225182366 0.9936440677966102 6/19 17.91% St3gal6;Fzd1;Psenen;Fzd7;Psen2;Rbx1
45 prerank Spermatogenesis 0.19400301723491742 0.6220541233712112 0.9756613756613757 1.0 1.0 12/68 21.45% Tle4;Ezh2;Gapdhs;Gsg1;Arl4a;Phf7;Oaz3;Nos1;Nefh;Jam3;Map7;Tnni3
46 prerank G2-M Checkpoint 0.17817142758089224 0.5929783880466872 0.9969418960244648 1.0 1.0 43/131 35.86% Sqle;Kmt5a;Ezh2;Jpt1;Map3k20;Dkc1;Prim2;Polq;Cdc25b;Ccnt1;Katna1;Ccnd1;Cdk4;Plk4;Smad3;Dmd;Cul5;Tmpo;Mtf2;Atf5;Hmgb3;Slc7a1;E2f3;Wrn;Kif15;Nasp;Lmnb1;Gins2;Hif1a;Slc38a1;Slc7a5;Srsf10;Hnrnpu;Cbx1;Pura;Nup50;Meis1;Lbr;Tle3;Gspt1;Ythdc1;Cul4a;Syncrip
47 prerank Pperoxisome 0.18593307537957096 0.5906854012319501 0.9904458598726115 1.0 1.0 15/73 23.88% Idi1;Idh2;Abcb9;Pex5;Acot8;Mvp;Ercc1;Pex6;Ehhadh;Cnbp;Prdx1;Acsl1;Slc35b2;Cdk7;Cadm1
48 prerank Pancreas Beta Cells 0.22680571272756017 0.5899446538939727 0.9673776662484316 1.0 1.0 1/19 1.33% Sec11a
49 prerank Myc Targets V1 0.1581551111635115 0.5409489942964322 1.0 1.0 1.0 23/179 21.40% Tardbp;Snrpd2;Rps6;Ncbp2;Prpf31;Pa2g4;Txnl4a;Tyms;Pwp1;Tufm;Lsm2;Ruvbl2;Mrpl9;Gnl3;Prdx4;Prps2;Srm;Cdk4;Cnbp;Hddc2;Psmd8;Xpot;Pole3
50 prerank Oxidative Phosphorylation 0.15577552122156502 0.5261791902085622 1.0 1.0 1.0 13/166 10.65% Mfn2;Idh3a;Mrps15;Uqcrc1;Idh2;Aldh6a1;Ndufs8;Mrpl35;Hadhb;Tomm22;Fdx1;Acat1;Eci1
51 prerank Protein Secretion 0.11372226561933299 0.36484533819846215 1.0 0.9999786539159391 1.0 31/85 43.12% Tmed2;Gosr2;Zw10;Cln5;Tsg101;Copb1;Stx16;Vamp4;Rab22a;Vps45;Stx7;Lman1;Sgms1;Pam;Vamp7;M6pr;Sec24d;Atp6v1h;Dst;Snx2;Cope;Gbf1;Arfgef1;Ppt1;Arfgef2;Arcn1;Tpd52;Rps6ka3;Rab14;Arfgap3;Galc
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group,cell_UP,cell_DOWN,cell_ratio,pseudo_UP,pseudo_DOWN,pseudo_ratio
Muller,9,48,0.19,1690,769,2.2
RGC2-like,0,0,0.0,1748,1391,1.26
RGC1-like,13,162,0.08,1243,629,1.98
Rod,60,49,1.22,462,214,2.16
1 group cell_UP cell_DOWN cell_ratio pseudo_UP pseudo_DOWN pseudo_ratio
2 Muller 9 48 0.19 1690 769 2.2
3 RGC2-like 0 0 0.0 1748 1391 1.26
4 RGC1-like 13 162 0.08 1243 629 1.98
5 Rod 60 49 1.22 462 214 2.16
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### N E U R O S C I E N C E
# Disrupted energy metabolism is associated with retinal ganglion cell degeneration in autosomal dominant optic atrophy
**Eugene Yu- Chuan Kang****1,2,3,4** **, Yun- Ju Tseng****1** **, Wei- Hao Peng****5** **, Hui- Chuan Hung****6** **, Pei- Hsuan Lin****1,7** **, Katrina P. Montales****8** **, Emmet Sherman****9** **, John Peregrin****1** **, Ethan Hunghsi Wang****1,10** **, Chunya Kang****11** **, Yu- Chuan Teng****12** **, Chen- Yang Huang****4,12,13** **, Chia- Lung Tsai****12** **, Ian Yi- Feng Chang****12,14** **, Jiazhang Chen****15** **, Gülgün Tezel****1** **, Ye He****15,16,17** **, Tai- De Li****9,18** **, Linsey Stiles****8** **, Orian Shirihai****8** **, Stephen H. Tsang****1,6** **, Chi- Chun Lai****4,19** **, Chi- Neu Tsai****3,20** ***, Chyuan- Sheng Lin****6** ***, Nan- Kai Wang****1,2,4** *****
copyright © 2026 the Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. no claim to original U.S. Government Works. distributed under a creative commons Attribution noncommercial license 4.0 (cc BY- nc).
**Autosomal dominant optic atrophy (ADOA) is a hereditary optic neuropathy caused by** **_OPA1_ variants, leading to retinal ganglion cell (RGC) degeneration and vision loss. The mechanisms behind RGC vulnerability to mitochondrial dysfunction remain unclear. We developed a patient- specific** **_Opa1_****_V291D/_**_+_ **knock- in mouse model to investigate mitochondrial dysfunction and retinal metabolism in ADOA. We observed that** **_Opa1_****_V291D/_**_+_ **mice exhibited anatomical and functional RGC abnormalities recapitulating the ADOA phenotypes. Reduced optic atrophy 1 (OPA1) protein levels were noted in** **_Opa1_****_V291D/_**_+_ **mice, accompanied by decreased protein stability. Moreover, mitochondrial function was compromised, as indicated by reduced Complex I activity, increased oxidative stress, and diminished adenosine triphosphate production in the retinas of** **_Opa1_****_V291D/_**_+_ **mice. Spatial metabolomics revealed energy deficits in the inner retina and heightened glycolysis in the outer retina. Immunostaining showed decreased expression of glycolytic proteins in the ganglion cell layer. Single- nucleus RNA sequencing disclosed significant down- regulation of energy- production genes in RGCs, while other retinal cell types remained unaffected. These findings emphasize the specific vulnerability of RGCs to bioenergetic crises, connecting disrupted energy homeostasis to their degeneration. By increasing the nicotinamide adenine dinucleotide (NAD****+** **)/reduced form of NAD****+** **(NADH) redox ratio through the overexpression of mitochondrial- targeted** **_Lactobacillus brevis_ NADH oxidase (** **_MitoLbNOX_ ) in RGCs, we demonstrated improved RGC function and survival through enhanced energy metabolism and reduced oxidative stress. These findings confirm that disrupted energy metabolism leads to RGC degeneration and emphasize the enhancement of the NAD****+** **/NADH redox ratio as a promising treatment strategy to protect RGCs from degeneration in ADOA.**
### INTRODUCTION
Autosomal dominant optic atrophy (ADOA) is the most common inherited optic neuropathy, with incidence rates ranging from 1 in 12,000 to 50,000 individuals ( _1_ , _2_ ). It is a mitochondrial eye disease primarily characterized by the degeneration of retinal ganglion cells (RGCs) ( _3_ ). This degeneration leads to progressive vision loss and is associated with variants in the nuclear DNA–encoded OPA1 mitochondrial dynamin like GTPase ( _OPA1_ ) gene, which compromise mitochondrial function ( _4_ – _6_ ). Variants in the _OPA1_ gene alter the optic atrophy 1 (OPA1) protein, a key component of the inner mitochondrial membrane responsible for mitochondrial dynamics and fusion ( _7_ ). In mammalian mitochondria, the OPA1 protein plays a vital role not only in the regulation of the fusion of the inner mitochondrial
membrane but also in the shaping of mitochondrial cristae ( _7_ , _8_ ), which are essential for the regulation of mitochondrial respiration, the stabilization of the electron transport chain (ETC), and the maintenance of oxidative stress homeostasis ( _9_ , _10_ ). Understanding the impact of the _OPA1_ variant on RGC is crucial for clarifying the pathogenic mechanisms underlying ADOA.
The impact of _OPA1_ variants on cells has been studied in previous in vitro research. Those observations revealed that HeLa cells transfected with _OPA1_ variants exhibited fragmented mitochondria and impaired oxidative phosphorylation (OXPHOS) ( _11_ , _12_ ). However, a separate study found that there was no decrease in mitochondrial adenosine triphosphate (ATP) production in ADOA human fibroblasts carrying different _OPA1_ variants ( _13_ ), indicating a disparity
> 1department of Ophthalmology, vagelos college of Physicians and Surgeons, columbia University irving Medical center, new York, nY 10032, USA. 2department of Ophthalmology, chang Gung Memorial hospital, linkou Medical center, taoyuan 333, taiwan.3 Graduate institute of clinical Medical Sciences, college of Medicine, chang Gung University, taoyuan 333, taiwan.4 School of Medicine, chang Gung University, taoyuan 333, taiwan.5 School of Medicine, national tsing hua University, hsinchu 300, taiwan. 6department of Pathology and cell Biology, herbert irving comprehensive cancer center, columbia University Medical center, new York, nY 10032, USA. 7department of Ophthalmology, national taiwan University Yunlin Branch, Yunlin 640, taiwan.8 division of endocrinology, department of Medicine, david Geffen School of Medicine, University of california, los Angeles, los Angeles, cA 90095, USA.9 nanoscience initiative at Advanced Science Research center, Graduate center of the city University of new York, new York, nY 10031, USA.10 college of Arts and Sciences, University of Miami, coral Gables, Fl 33146, USA.11 department of education, Keelung chang Gung Memorial hospital, Keelung 204, taiwan.12 Genomic Medicine core laboratory, chang Gung Memorial hospital, taoyuan 333, taiwan.13 department of Medical Oncology, chang Gung Memorial hospital, linkou Medical center, taoyuan 333, taiwan.14 Molecular Medicine Research center, chang Gung University, taoyuan 333, taiwan.15 Advanced Science Research center (ASRc), Graduate center of the city University of new York, new York, nY 10031, USA.16 Ph.d. Program in Biology, Graduate center of the city University of new York, new York, nY 10031, USA.17 department of Biology, city college of new York, city University of new York, new York, nY 10031, USA.18 department of Physics, city college of new York, city University of new York, new York, nY 10031, USA.19 department of Ophthalmology, new taipei Municipal tucheng hospital, new taipei city 23652, taiwan.20 department of Surgery, new taipei Municipal tucheng hospital, new taipei city 23652, taiwan. *corresponding author. email: pink7@ mail. cgu. edu. tw (c.- n.t.); csl5@ cumc. columbia. edu (c.- S.l.); wang. nankai@ gmail. com (n.- K.W.)
between these observations and the in vitro findings collected from non- RGC cells. Recent studies demonstrated that introducing the _Opa1__K301A_ and _Opa1__R905*_ variants into mouse RGC cultures resulted in the autophagic degradation of mitochondria and a subsequent decrease in mitochondrial activity content ( _14_ ). Despite these findings connecting the _OPA1_ variant to mitochondrial dysfunction, it is still unclear whether the degeneration of RGCs in ADOA is primarily due to a bioenergetic crisis, decreased antioxidant capacity, or a combination of both factors ( _15_ , _16_ ).
In vivo models provide advantages over in vitro models regarding the evaluation of the impact of these variants on visual function. Three _Opa1_ gene–modified ADOA mouse models have been reported. These include mice carrying a nonsense variant ( _Opa1__Q285STOP_ ) ( _17_ – _20_ ), a 4– base pair deletion causing a frameshift ( _Opa1__c.2708_2711delTTAG_ ) ( _21_ , _22_ ), and a splice- site variant ( _Opa1__c.1065+5G>A_ ) ( _23_ ). It is important to emphasize that all three mouse models express a truncated OPA1 protein. Currently, there are no reports of _Opa1_ mouse models containing missense variants, which are the most common protein- coding mutations identified in patients with ADOA, according to ClinVar and the Leiden Open Variation Database ( _24_ , _25_ ). Furthermore, no studies have examined transcriptomes at the single- cell level in the _Opa1_ mouse model to understand why RGCs are more vulnerable than other retinal cells, especially since this nuclear- encoded protein is expressed universally in all cells. In addition, while photoreceptors have the highest density of mitochondria in the retina ( _26_ ), the mitochondrial dysfunction associated with ADOA primarily affects RGCs, leaving photoreceptors largely unaffected. This disparity underscores the urgent need to investigate the effects of _Opa1_ variants on different retinal cells. Comprehensive studies are crucial to uncover the underlying disease mechanisms, identify factors contributing to RGC vulnerability, and develop targeted therapeutic interventions for RGC degeneration.
Nicotinamide has garnered attention in RGC degeneration as it is depleted in the plasma signatures of patients with ADOA and glaucoma ( _27_ – _30_ ). While nicotinamide adenine dinucleotide (NAD+ ) itself plays a crucial role in glycolysis, the tricarboxylic acid (TCA) cycle, and OXPHOS, the NAD+ /reduced form of NAD+ (NADH) redox ratio serves as a key regulator of cellular energy metabolism and an indicator of cellular stress levels ( _30_ – _32_ ). Previous studies have demonstrated that oral administration of the NAD+ precursor nicotinamide and gene therapy promoting _Nmant1_ expression, a key NAD+ - producing enzyme, halted RGC degeneration in the DBA/2J mouse model of glaucoma ( _33_ – _35_ ). Although these promising results highlight the antioxidant properties of vitamin B3 and its role in NAD+ synthesis, it remains uncertain whether similar strategies to increase NAD+ levels could enhance energy metabolism and support RGC survival in the _Opa1_ mouse model. Moreover, it is unclear whether directly converting NADH to NAD+ to boost the NAD+ /NADH redox ratio would be an effective and efficient strategy for protecting RGCs.
In this study, we developed a novel mouse model by introducing a patient- derived missense variant of the _OPA1_ gene to investigate the pathophysiology of ADOA. We evaluated whether the model accurately replicated the clinical phenotypes of ADOA, focusing on functional deficits, anatomical alterations, OPA1 protein characteristics, and mitochondrial phenotypes. To evaluate the effect of the _Opa1_ variant on mitochondrial function, we analyzed energy metabolism and oxidative stress throughout the retina. Immunostaining and spatial metabolomics were used to assess histological changes and metabolic adaptations, particularly in the ganglion cell layer where RGCs reside. We used single- nucleus RNA sequencing (snRNA- seq)
to uncover transcriptomic changes linked to ADOA at the single- cell level, aiming to identify the mechanisms contributing to the selective vulnerability of RGCs in ADOA. In addition, we examined the impact of increasing the NAD+ /NADH redox ratio in RGCs on their survival in our ADOA mouse model.
### RESULTS
## Clinical and genetic profile of the patient with ADOA carrying an** **_OPA1_ missense variant
A 32- year- old woman visited our institution with a history of gradually declining vision. The results of an eye examination performed on this patient are displayed in Fig. 1. Fundus imaging indicated temporal pallor of the optic disc (Fig. 1A). Optical coherence tomography (OCT) of the optic disc revealed a reduction in retinal nerve fiber layer (RNFL) thickness (Fig. 1B). Electrophysiological testing showed normal rod and cone responses on full- field electroretinography (ERG), albeit with reduced pattern ERG (PERG) responses (Fig. 1C). Genetic testing confirmed the presence of a heterozygous variant in the _OPA1_ gene, i.e., c.1037T>A, p.V346D (NM_130837.3), thereby confirming the diagnosis of ADOA. This variant is classified as a missense variant, which is the most common type of mutation in ADOA, according to reports in ClinVar and the Leiden Open Variation Database ( _24_ , _25_ ). This _OPA1_ missense variant was recently submitted by a reporter to the ClinVar database (ID: 447907). The _OPA1__V346D_ variant is classified as likely pathogenic, on the basis of aggregated data from public databases, following American College of Medical Genetics and Genomics guidelines (table S1).
## Generation and characterization of a patient- specific knock- in ADOA mouse model (** **_Opa1_****_V291D/+_** **)
Because of the absence of _OPA1_ mouse models carrying missense variants, we developed a patient- specific knock- in mouse model ( _Opa1__V291D/+_ ) carrying a V291D variant equivalent to the V346D variant found in our patient with ADOA (Fig. 1D). The resulting knock- in mouse harbored the _Opa1_ c.871T>A variant, which changes the 291st amino acid of OPA1 from valine to aspartic acid. Mice that were homozygous for this variant exhibited embryonic lethality, consistent with observations in other ADOA mouse models. We verified the presence of the V291D variant through the polymerase chain reaction (PCR) amplification of exon 9 using forward and reverse primers (table S2), which confirmed variant heterozygosity in the mutant mice (Fig. 1E); this was further validated using Sanger sequencing (Fig. 1F). The mutant mice had lower body weights than their _WT_ littermate controls after 180 days (Fig. 1G). Moreover, the mutant mice exhibited a hunched- back posture (fig. S1), which was suggestive of an illness or aging condition associated with the specific variant.
## The visual functional phenotype of the novel** **_Opa1_****_V291D/+_** **mice recapitulated the features of ADOA
To analyze the visual functional presentation of the _Opa1__V291D/+_ mouse model, we performed several electrophysiological tests, including PERG, photopic negative responses (PhNRs), scotopic threshold response (STR), and serial- intensity scotopic and photopic flash ERGs. At 180 days, the PERG revealed a significant decrease in amplitude between P1 and N2, which continued to decline up to 630 days, indicating the presence of a degenerative process in this mouse model (Fig. 2A). _Opa1__V291D/+_ mice exhibited a significantly reduced PhNR amplitude at
**Fig. 1. Optic atrophy and visual function impairment in a patient with ADOA and the generation of an** **_Opa1_****_V291D/+_** **mouse model.** ( **A** ) Fundus photography showing temporal disc pallor in the left eye, representative of both eyes. ( **B** ) Oct demonstrating decreased RnFl thickness, averaging 69.8 μm in the left eye. ( **C** ) Full- field eRG indicating normal rod and cone responses, with decreased PeRG responses. ( **D** ) targeting strategy used for generating the _Opa1__V291D/+_ knock- in mouse, with primers (F1 and R1) designed to detect exon 9 of _Opa1_ . ( **E** ) Genotyping results for _Opa1_+/+ and _Opa1__V291D/+_ tissues using the indicated primers. the knock- in allele includes an additional 83 nucleotides compared with the _wildtype_ ( _WT_ ) allele, incorporating the _LoxP_ site and adjacent sequences. ( **F** ) Sequencing of the region between the indicated primers confirming the heterozygous t- to- A variant. ( **G** ) Body weight measurements of mice at different ages (total _n_ = 306; independent _t_ tests _P_ = 0.8096, 0.3582, 0.2501, 0.0191, 0.0011, <0.0001, and < 0.0001 at P30, P90, P120, P180, P270, P360, and P450, respectively). data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, **** _P_ < 0.0001. n.s., not significant; ex, exon; neoR, neomycin resistance.
180 days (Fig. 2B). In terms of STR, a significant reduction in the negative STR was observed at all three intensities (−5.6, −5.3, and −5.0 log cd·s/m2 ) at 180 days (Fig. 2C). The scotopic and photopic ERGs did not display significant differences in both a- and b- wave amplitudes across all intensities between the _Opa1__V291D/+_ and their littermate- control _WT_ mice at 360 days (Fig. 2D). Our _Opa1__V291D/+_ mice exhibited abnormal results in electrophysiological tests specific to RGC, whereas the function of photoreceptors remained unaffected. These findings were consistent with those observed in human patients with ADOA.
## Reduction in RNFL thickness and RGC count in the retinas of the** **_Opa1_****_V291D/+_** **mice
In addition to assessing their functional phenotype, we used in vivo spectral domain (SD)–OCT and immunostaining to elucidate the anatomical phenotype in the retinas of the _Opa1__V291D/+_ mice. The SD- OCT examination revealed a decreased RNFL thickness in _Opa1__V291D/+_ mice compared with their littermate _WT_ controls. This was observed in both female and male mice at 90 days (Fig. 3A). The number of RGCs was examined by immunostaining of wholemounted retinas using an anti- BRN3A antibody, as shown in Fig. 3B. This analysis revealed a reduction in RGC numbers in _Opa1__V291D/+_ mice compared with their littermate _WT_ controls, which was correlated with the decrease in RNFL thickness. Notably, the RGC counts
in mutant mice were significantly decreased after 180 days and continued to decline up to 420 days.
## The optic nerves of** **_Opa1_****_V291D/+_** **mice showed alterations in axonal and mitochondrial structure
To examine in greater detail the anatomical features of the myelinated sheath and mitochondrial morphology in the optic nerve, which contains the axons of RGCs, super- resolution imaging, including spinning disk confocal microscopy (SDCM) with super- resolution radial fluctuations (SRRFs) and transmission electron microscopy (TEM), was applied to mouse optic nerves. Our SDCM with SRRF imaging analysis detected the presence of altered mitochondrial shapes in _Opa1__V291D/+_ mice at 360 days, which exhibited a greater number of spherical and less variable mitochondria compared with control mice (Fig. 3C), indicating the presence of mitochondrial fragmentation caused by impaired mitochondrial fusion. To further delineate regional differences in mitochondrial dynamics, we performed additional imaging to assess mitochondria across the prelaminar region, the unmyelinated optic nerve head, and the myelinated optic nerve ( _36_ ). Increased mitochondrial sphericity was consistently observed in mutant mice across all three regions (fig. S2A). TEM analysis revealed a loosened myelinated sheath and a significantly reduced number of myelinated axons in mutant mice at ages 50 and 360 days (Fig. 3D), reflecting chronic RGC
**Fig. 2.** **_Opa1_****_V291D/+_** **variant in mice recapitulates the RGC- specific visual function deficits of patients with ADOA.** ( **A** ) Representative PeRG recordings showing the amplitude measured from n2 to P1 (total _n_ = 161; independent _t_ tests _P_ = 0.8478, 0.0097, 0.0021, <0.0001, 0.0265, and 0.0462 at P90, P180, P270, P460, P450, and P630, respectively). ( **B** ) Representative PhnR recordings showing the amplitude measured from the baseline to the trough (total _n_ = 15; independent _t_ tests _P_ = 0.0026). ( **C** ) Representative StR recordings showing the amplitude measured from the baseline to the positive StR (pStR) and negative StR (nStR) (total _n_ = 51; independent _t_ tests _P_ = 0.0021, <0.0001, and 0.0006 at nStR –5.6, −5.3, and −5.0 log cd·s/m2 , respectively; _P_ = 0.6499, 0.9336, and 0.9042 at pStR –5.6, −5.3, and −5.0 log cd·s/m2 , respectively). ( **D** ) Representative serial scotopic and photopic eRG recordings at different intensities at 360 days (total _n_ = 9; linear regression model _P_ for interaction = 0.420, 0.887, 0.201, and 0.117 in scotopic a- wave, photopic a- wave, scotopic b- wave, and photopic b- wave, respectively). data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, *** _P_ < 0.001, **** _P_ < 0.0001.
degeneration ( _37_ ). Moreover, TEM imaging of the optic nerve revealed changes in mitochondrial morphology in the _Opa1__V291D/+_ mice, including the separation of the inner mitochondrial membranes, the loss of cristae, and mitochondrial vacuolation (Fig. 3E). Examination of RGC somata in the ganglion cell layer also revealed disrupted mitochondria and the accumulation of mitophagosomes (fig. S2B). To determine whether mitochondrial genomic alterations accompanied these structural abnormalities, we analyzed mitochondrial DNA (mtDNA) copy number and integrity in retinal tissues. Quantitative PCR (qPCR) revealed a significant increase in mtDNA copy
number in _Opa1__V291D/+_ retinas compared with _WT_ controls, possibly reflecting impaired fusion, accumulation of mitophagosome, and compensatory mitochondrial turnover (fig. S3A). In contrast, qPCR- based mtDNA damage assays and long- extension PCR showed no detectable differences in mtDNA deletions or damage between _Opa1__V291D/+_ and _WT_ mice (fig. S3B). These results indicate that, although mtDNA copy number is elevated, the overall integrity of the mitochondrial genome remains intact, suggesting that the observed mitochondrial defects are primarily functional and structural rather than due to mtDNA instability. Together, these findings indicate that
**Fig. 3. The** **_Opa1_****_V291D/+_** **variant leads to RGC loss and mitochondrial ultrastructure alterations in the retina and optic nerve.** ( **A** ) Sd- Oct at 90 days (total _n_ = 32; independent _t_ tests _P_ = 0.0360, 0.0197, and 0.0228 in male, female, and total groups, respectively). ( **B** ) Representative images showing BRn3A- positive RGc counts in 20 squares from three different zones of a whole- mounted retina. Analysis of the RGc counts per 20 squares at 180, 360, and 420 days ( _n_ = 4, 6, and 3 mice per group at P180, P360, and P420, respectively; independent _t_ tests _P_ = 0.0105, 0.0031, and 0.0028 at P180, P360, and P420, respectively). ( **C** ) Representative image showing confocal microscopy with super- resolution imaging of the optic nerves. violin plot of the mitochondrial sphericity in the optic nerves at 360 days ( _n_ = 4 mice in each group; independent _t_ tests _P_ = 0.0356). ( **D** ) Representative images showing optic nerve ultrastructure in teM. Analysis of the number of myelinated axons in optic nerves at 50 days ( _n_ = 3 mice in each group; independent _t_ tests _P_ = 0.0009) and 360 days ( _n_ = 4 mice in each group; independent _t_ tests _P_ < 0.0001). ( **E** ) Separation of the inner mitochondrial membranes, loss of cristae, and mitochondrial vacuolation were also observed in _Opa1__V291D/+_ mice. data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, *** _P_ < 0.001, **** _P_ < 0.0001.
_Opa1__V291D/+_ mice exhibit reduced RGC numbers in the retina, thinner RNFL on SD- OCT, fewer myelinated axons in the optic nerve, and mitochondrial structural abnormalities accompanied by increased mtDNA copy number but preserved mtDNA integrity. These combined changes demonstrate RGC degeneration associated with impaired mitochondrial fusion and respiratory dysfunction.
## _Opa1_****_V291D/+_** **mice showed decreased stability of the OPA1 protein
To investigate whether the V291D variant affects the expression of the OPA1 protein in the retina, we performed Western blot analyses, which revealed a significantly reduced level of the OPA1 protein in the retinas of _Opa1__V291D/+_ mice (Fig. 4A). In contrast, qPCR analyses
**Fig. 4. Decreased OPA1 protein levels in the** **_Opa1_****_V291D/+_** **mouse retinas and reduced OPA1 protein stability in** **_Opa1_****_V291D_** **_-_ transfected cells.** ( **A** ) Western blot (WB) showing the OPA1 protein expression in retinas ( _n_ = 6 in each group, independent _t_ tests _P_ < 0.0001). ( **B** ) qPcR of the _Opa1_ mRnA expression in retinas ( _n_ = 6 in each group, independent _t_ tests _P_ = 0.7744). ( **C** ) lysates from heK293 cells transfected with _Opa1__WT_ and _Opa1__V291D_ were treated with MG132. immunoprecipitation (iP) revealed the presence of polyubiquitinated OPA1 in the _Opa1__V291D_ - transfected cells. ( **D** ) levels of the OPA1 protein after treatment with MG132 (25 μM) at baseline, 4 hours, and 6 hours in the _Opa1__V291D_ - transfected heK293 cells ( _n_ = 3 in each group; one- way analysis of variance (AnOvA) with tukey’s test _P_ = 0.9431 and 0.0241 in 0 versus 4 hours and 0 versus 6 hours). data are presented as means ± SeM. * _P_ < 0.05, **** _P_ < 0.0001.
revealed no differences in _Opa1_ mRNA expression in retinal cells between the mutant and littermate- control _WT_ mice, indicating that the down- regulation of OPA1 protein levels was not due to a decrease in the transcription of the corresponding gene (Fig. 4B). Because reduced protein levels are often linked to protein destabilization and degradation via the ubiquitin- proteasome system ( _38_ ), we further examined OPA1 protein expression and ubiquitination in _Opa1__WT_ _-_ and _Opa1__V291D_ _-_ transfected human embryonic kidney (HEK) 293 cells. In the presence of _N_ - carbobenzyloxy- l- leucyl- l- leucyl- l- leucinal (MG132) (25 μM), which is a proteasome inhibitor, we observed polyubiquitinated OPA1 substrates in the lysates of _Opa1__V291D_ - transfected cells (Fig. 4C), suggesting that increased degradation through the ubiquitin- proteasome pathway contributed to the down- regulation of the OPA1 protein. Moreover, treating HEK293 cells with MG132 (25 μM) for 4 and 6 hours resulted in a mild restoration of OPA1 accumulation after 6 hours, especially the short form of the protein, in _Opa1__V291D_ _-_ transfected cells (Fig. 4D), indicating that the ubiquitin- proteasome pathway contributes to, but does not fully account for, OPA1 depletion. To further assess the impact of the V291D variant on OPA1 isoform processing, we analyzed the ratio of long OPA1 (l- OPA1) and short OPA1 (s- OPA1) in retinal lysates. Both isoforms were significantly reduced in _Opa1__V291D/+_ mice compared with _WT_ controls, with a disproportionately greater decrease in the short (soluble) form (fig. S4). This pattern suggested that the V291D variant caused reduced overall OPA1 protein stability
and impaired proteolytic processing, leading to selective depletion of s- OPA1. Because s- OPA1 acts together with l- OPA1 in crista remodeling, mitochondrial fusion, and restoration of energy efficiency ( _39_ ), its preferential loss likely aggravates crista disorganization and compromises OXPHOS efficiency. Together, these results demonstrate that the _Opa1__V291D_ variant leads to decreased protein stability, enhanced proteasomal degradation, and impaired isoform processing, resulting in reduced OPA1 function. This combination of effects provides a mechanistic link between the mutation, disrupted mitochondrial structure, and the OXPHOS dysfunction underlying RGC degeneration in ADOA.
## Reduction of mitochondrial Complex I activity (NADH/ ubiquinone oxidoreductase) in the retinas of
## _Opa1_****_V291D/+_** **mice
On the basis of our examination of the changes in the shape and structure of mitochondria in _Opa1__V291D/+_ mice, we investigated how these alterations affect mitochondrial function in the retinas of these mice. Specifically, we performed tests to measure mitochondrial respiration and ATP hydrolysis in the retinas using frozen tissue samples [referred to as the respirometry in frozen sample (RIFS) and hydrolysis in frozen sample (HyFS) assays, respectively (Fig. 5A) ( _40_ – _42_ ). The results of these assays revealed a significant decrease in the activity of Complex I in terms of both the protein- normalized and the MitoTracker Deep Red (MTDR)–normalized oxygen consumption rates in _Opa1__V291D/+_
**Fig. 5. Mitochondrial dysfunction, oxidative stress, reduced energy production, and glycolytic shift in** **_Opa1_****_V291D/+_** **mouse retinas.** ( **A** ) Representative bioenergetic profile, as determined using the RiFS protocol in frozen retinas. ( **B** ) Optimized RiFS analysis of mitochondrial complex i, ii, and iv activities normalized to total protein and mitochondrial content (MtdR; _n_ = 6 per group). ( **C** ) Ratios of complex i/iv, ii/iv, and i/ii activities. Optimized RiFS results normalized to total protein and mitochondrial content using MtdR ( _n_ = 6 per group). ( **D** ) AtP hydrolytic capacity assessed by hyFS ( _n_ = 6 per group) ( **E** ) the GSh/GSSG ratio and total GSh level in retinal lysate ( _n_ = 7 per group. ( **F** ) the SOd activity in mouse retinas ( _n_ = 7 per group). ( **G** ) Representative immunostaining of 4- hne in retinal sections showing increased fluorescence intensity in the _Opa1__V291D/+_ mouse retina, particularly in the ganglion cell layer. Bar chart of the 4- hne fluorescence intensity in retinal immunostaining ( _n_ = 5 per group). ( **H** ) the nAd+ /nAdh ratio, the quantity (picomol) of nAd+ per amount (milligram), and the quantity (picomol) of nAdh per amount (milligram) of protein in mouse retinas ( _n_ = 6 per group). ( **I** ) the quantity (nmol) of AtP per amount (milligram) of protein in mouse retinas ( _n_ = 6 per group). ( **J** ) the level of lactate per amount (milligram) of protein in mouse retinas ( _n_ = 5 per group). ( **K** ) Western blot of the phospho- PFKFB3, phospho- GlUt1, hK1, and hK2 in mouse retinas lysates with quantification ( _n_ = 6 to 8 per group). data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, *** _P_ < 0.001. AA, antimycin A; Rot, rotenone; Asc, ascorbate.
mice (Fig. 5B). This normalization helped account for potential variations in mitochondrial content between samples, thereby ensuring that the observed differences reflect true functional deficits rather than changes in mitochondrial abundance. In addition to Complex I dysfunction, we also observed a decrease in Complex IV activity, as evidenced by the increased Complex II/IV activity ratio without significant change in Complex II activity in _Opa1__V291D/+_ mice (Fig. 5C). This suggests that both Complex I and IV activities are diminished in _Opa1__V291D/+_ retinas, consistent with the role of OPA1 in maintaining mitochondrial crista integrity, which is essential for the stability and function of respiratory complexes. The altered ratio further indicates a compensatory adjustment in the respiratory chain to preserve energy production despite dual impairment. In the HyFS assay, a trend toward reduced protein- normalized ATP hydrolytic capacity was observed in _Opa1__V291D/+_ mice (Fig. 5D), although this result did not reach statistical significance. These findings underscore the presence of ETC defects with Complex I dysfunction in the retina of _Opa1__V291D/+_ mice.
## Reduced antioxidant capacity and increased oxidative stress in the retinas of** **_Opa1_****_V291D/+_** **mice
Because of the potential for the induction of oxidative stress by Complex I impairment ( _43_ ), we also examined the antioxidant and oxidative stress profiles in mouse retinas. The glutathione (GSH) levels and the ratio of GSH to its oxidized form (GSSG) were significantly lower in _Opa1__V291D/+_ retinas (Fig. 5E), indicating a disrupted redox balance and heightened oxidative stress. In addition, superoxide dismutase (SOD) activity was significantly lower in the _Opa1__V291D/+_ retinas (Fig. 5F), suggesting a diminished antioxidant capacity that may worsen oxidative stress by facilitating the accumulation of superoxide radicals. Immunostaining of retinal tissues for 4- hydroxynonenal (4- HNE), which is a crucial marker of oxidative stress ( _44_ ), showed significantly elevated 4- HNE levels in _Opa1__V291D/+_ mice, particularly in the inner retina (Fig. 5G), further confirming the accumulation of oxidative stress. These findings link oxidative stress with the defective ETC and impaired Complex I activity observed in _Opa1__V291D/+_ retinas.
## Decreased NAD****+** **/NADH redox ratio and ATP levels but increased glycolysis in the retinas of** **_Opa1_****_V291D/+_** **mice
Given the defects in ETC observed in _Opa1__V291D/+_ mouse retinas, we next examined retinal energy metabolism by measuring NAD+ and its reduced form (NADH) and ATP levels using assay kits. The results indicated that the NAD+ /NADH redox ratio and NAD+ levels were reduced in _Opa1__V291D/+_ mouse retinas, whereas NADH levels remained comparable between the mutant and control groups (Fig. 5H), which was consistent with the impairment in Complex I activity noted in _Opa1__V291D/+_ mice. The ATP levels were significantly decreased in _Opa1__V291D/+_ mouse retinas (Fig. 5I), thus corroborating ETC dysfunction and the resulting bioenergetic crisis within the retina. Since glycolysis serves as an alternative energy source when OXPHOS is impaired in the retina ( _45_ , _46_ ), we further assessed lactate levels in mouse retinas. Lactate assays revealed increased lactate production in _Opa1__V291D/+_ retinas (Fig. 5J), indicating an adaptive metabolic response. In addition, immunoblot analysis demonstrated significant up- regulation of phospho–6- phosphofructo- 2- kinase/fructose- 2,6bisphosphatase 3 (PFKFB3), phospho–glucose transporter 1 (GLUT1), hexokinase 1 (HK1), and HK2 proteins in _Opa1__V291D/+_ retinas (Fig. 5K), suggesting a metabolic shift toward glycolysis to compensate for impaired ETC function.
## Decreased ATP with accumulation of adenosine monophosphate in the inner retinas, while increased glycolytic metabolites in the outer retinas of
**_Opa1_****_V291D/+_** **mice**
To further characterize metabolic alterations in _Opa1__V291D/+_ mouse retinas, we conducted matrix- assisted laser desorption/ionization time- of- flight (MALDI- TOF) mass spectrometry (MS) analysis, which revealed substantial ATP depletion and adenosine monophosphate (AMP) accumulation, particularly in the inner retinal layers where RGCs reside, indicating a severe energy crisis in these regions (Fig. 6A). In contrast, MALDI results demonstrated significantly elevated signal intensities of glycolysis metabolites, including glucose- 6- phosphate (G6P) and pyruvate, predominantly in the outer retinal layers, where photoreceptors are located (Fig. 6B). These findings suggest a metabolic shift toward glycolysis as a compensatory mechanism in response to energy deficits in _Opa1__V291D/+_ retinas, particularly in the photoreceptor- rich outer retina, while the inner retinal layers, including RGCs, do not exhibit this change.
## Reduced glycolytic activity in the ganglion cell layer contrasted with the photoreceptor layer
To assess cellular responses to the bioenergetic crisis at the histological level, we analyzed phospho–AMP- activated protein kinase α (AMPKα) expression using immunostaining. We observed increased phosphoAMPKα fluorescence intensity in both the ganglion cell and photoreceptor layers of _Opa1__V291D/+_ retinas, indicating AMPK pathway activation under metabolic stress (Fig. 6C). Previous studies have demonstrated distinct preferences regarding the energy metabolism between the retinal layers, with outer retinal layers relying on glycolysis to compensate for ATP deficiencies ( _45_ – _48_ ), whereas inner retinal cells, including RGCs, primarily depend on mitochondrial ETC and OXPHOS, exhibiting lower glycolytic activity ( _45_ , _46_ ). Given these differences and building on the results of our immunoblot analysis, which indicate a glycolytic shift in retinal metabolism in response to a bioenergetic crisis, we further investigated the expression of glycolytic enzymes at the histological level to evaluate metabolic changes across different retinal layers. Immunostaining revealed a significantly reduced fluorescence intensity for phospho- PFKFB3, HK1, lactate dehydrogenase B (LDHB), and isocitrate dehydrogenase 3 (IDH3) in the ganglion cell layer of _Opa1__V291D/+_ mice. In contrast, the fluorescence intensities of phospho- PFKFB3, phospho- GLUT1, and HK1 were significantly elevated in the photoreceptor inner and outer segment layers (Fig. 6C). These findings suggest that, in response to the bioenergetic crisis caused by defective ETC, the compensatory energy metabolism via glycolysis and TCA cycle was impaired in the ganglion cell layer, where RGCs reside. This disruption of energy homeostasis in the ganglion cell layer may suggest the selective vulnerability of RGCs in _Opa1__V291D/+_ mice.
## snRNA- seq and spatial transcriptomics revealed the down- regulation of energy metabolism–related genes in the RGCs of** **_Opa1_****_V291D/+_** **mice
To understand further the molecular mechanisms underlying ADOA at the single- cell resolution, we used snRNA- seq to analyze transcriptomic changes in RGCs and other retinal cell types between _Opa1__V291D/+_ and _WT_ mouse retinas at 360 days. In a total of 19,315 nuclei, the snRNA- seq and unsupervised clustering analysis identified 10 clusters corresponding to nine retinal cell types, as assessed on the basis of the expression of specific cell markers (table S3), together with an additional cluster comprising other cells, as shown in Fig. 7A. Two
**Fig. 6. Reduced energy production and metabolic shift toward glycolysis in** **_Opa1_****_V291D/+_** **mouse retinas, with decreased glycolytic activity in the ganglion cell layer.** ( **A** ) Representative hematoxylin and eosin (h&e)–stained retinal sections, corresponding MAldi MS images, and manual image segmentation from _WT_ and _Opa1__V291D/+_ mice at 180 days. Bar charts of AtP signal intensity in positive ion mode ( _n_ = 3 per group; independent _t_ test _P_ = 0.0423, 0.0496, and 0.0649 in whole retina, inner retinal layer, and outer retinal layer) and AMP signal intensity in negative ion mode ( _P_ = 0.0451, 0.0280, and 0.0697). ( **B** ) Representative MAldi MS images of G6P and pyruvate in _WT_ and _Opa1__V291D/+_ mouse retinas at 180 days. Bar chart of G6P signal intensities in negative ion mode ( _P_ = 0.0188, 0.0744, and 0.0260 in whole retina, inner retinal layer, and outer retinal layer) and pyruvate signal intensities in negative ion mode ( _P_ = 0.0335, 0.0502, and 0.0367). ( **C** ) Representative immunostaining of phosphoAMPKα, phospho- PFKFB3, phospho- GlUt1, hK1, ldhB, and idh3 in retinal sections from _WT_ and _Opa1__V291D/+_ mice. Bar charts of the fluorescence intensity of phosphoAMPKα ( _n_ = 5 per group; independent _t_ test _P_ = 0.0013 and 0.0396 in ganglion cell layer and photoreceptor layer, respectively), phospho- PFKFB3 ( _P_ = 0.0454 and 0.0029), phospho- GlUt1 ( _P_ = 0.0595 and 0.0029), hK1 ( _P_ = 0.0018 and 0.0394), ldhB ( _P_ = 0.0038 and 0.8227), and idh3 ( _P_ = 0.0006 and 0.3595) in mouse retinas. data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, *** _P_ < 0.001.
**Fig. 7. Down- regulation of genes involved in the ETC, complex I biogenesis, and glycolysis in RGCs of** **_Opa1_****_V291D/+_** **mice.** ( **A** ) A cluster analysis of the results from snRnA- seq of retinal cells from _WT_ and _Opa1__V291D/+_ mice at 360 days identified 10 retinal cell types, including two distinct RGc clusters, RGc- 1 and RGc- 2, via unsupervised clustering. ( **B** ) A heatmap of the RGc markers in the RGc- 1 and RGc- 2 clusters in _WT_ and _Opa1__V291D/+_ mice. Both clusters expressed pan- RGc markers, with no statistically significant differences in _Pou4_ markers between the clusters. ( **C** ) heatmaps of pathway analyses highlighting multiple down- regulated genes in RGc- 2 from _Opa1__V291D/+_ mice compared with _WT_ controls, particularly in pathways related to etc ( _n_ = 5 mice per group; adjusted _P_ < 0.0001, _q_ < 0.0001, WikiPathways database), complex i biogenesis (adjusted _P_ < 0.0001, _q_ < 0.0001, ReActOMe database), and glycolysis (adjusted _P_ = 0.0081, _q_ = 0.0064, ReActOMe database). ( **D** ) A dot plot illustrating differential gene expression in the etc and glycolysis pathways across various retinal cell types. RGc- 2 displayed more significant differences in gene expression between _Opa1__V291D/+_ and _WT_ mice compared with other retinal cell clusters. ( **E** ) Representative image of high- resolution spatial transcriptomics analyzed using QuPath cell segmentation. cells from the ganglion cell layer (Gcl) were selected and clustering distinguished Gcl- derived cell populations in _WT_ and _Opa1__V291D/+_ retinas at 280 days. ( **F** ) heatmaps from spatial transcriptomic pathway analysis showing decreased expression of etc genes (adjusted _P_ = 0.0079, _q_ = 0.1215; WikiPathways database) and glycolysis genes (adjusted _P_ < 0.0001, _q_ < 0.0001; WikiPathways database) in RGc- rich regions of _Opa1__V291D/+_ retinas.
distinct RGC clusters, RGC- 1 and RGC- 2, were identified. A comparative analysis revealed that both clusters expressed pan- RGC markers, including _Rbpms_ , _Slc17a6_ , and _Thy1_ ( _49_ , _50_ ). There were no statistically significant differences between RGC- 1 and RGC- 2 in the expression of _Pou4_ genes, despite minor variations in relative expression levels observed in the plots (Fig. 7B). The differential gene expression analysis of these 10 clusters showed significant down- regulation of genes linked to the ETC, Complex I biogenesis, and glycolysis, particularly in the RGC- 2 cluster of _Opa1__V291D/+_ mice compared with the littermate _WT_ mice (Fig. 7C). In contrast, no significant changes in the expression of genes associated with energy- production pathways were detected in other retinal cell types, such as rods and cones, between _Opa1__V291D/+_ and _WT_ mice. A dot plot analysis displayed the log expression and percent expression of genes related to ETC and glycolysis across different cell types in _Opa1__V291D/+_ and _WT_ mice (Fig. 7D). It revealed that RGC- 2 had high energy demands in _WT_ mice and showed more pronounced differences between _Opa1__V291D/+_ and _WT_ compared with the other cell clusters. These snRNA- seq findings aligned with the results of the spatial metabolomics and immunostaining reported above, which indicated energy depletion and impaired
glycolysis predominantly in the ganglion cell layer. In addition, the snRNA- seq revealed decreased expression of genes related to mitophagy and autophagy pathways specifically in the RGC- 2 cluster (fig. S5A), whereas these pathways were preserved in photoreceptors and other retinal cells. To spatially validate these observations, we performed high- resolution spatial transcriptomics on mouse retinal sections. Consistent with the snRNA- seq data, spatial transcriptomic analysis revealed markedly reduced expression of ETC- and glycolysis- related genes in cells within the ganglion cell layer of _Opa1__V291D/+_ retinas compared with _WT_ (Fig. 7, E and F). Spatial transcriptomics also confirmed decreased expression of autophagy- related genes specifically in RGCrich regions, whereas mitophagy- related transcripts showed a downward trend but did not reach statistical significance (fig. S5B). These pathway- specific deficits were not observed in photoreceptors. Together, these integrated transcriptomic datasets demonstrate that RGCs exhibit coordinated down- regulation of ETC, glycolysis, and mitochondrial turnover pathways. This cell type–specific impairment in metabolic and mitochondrial quality- control responses likely contributes to the selective vulnerability of RGCs in _Opa1__V291D/+_ mice and underlies their progressive degeneration in ADOA.
## Enhanced RGC function and survival in** **_Opa1_****_V291D/+_** **mice following** **_MitoLbNOX_ overexpression
Considering that our _Opa1__V291D/+_ mouse model displayed Complex I dysfunction, a reduced NAD+ /NADH redox ratio, elevated oxidative stress, and decreased ATP production, we investigated whether increasing the NAD+ /NADH redox ratio could promote RGC survival in our _Opa1__V291D/+_ mice. To increase the NAD+ /NADH ratio, our approach was to use _Lactobacillus brevis_ ( _Lb_ ) NOX ( _51_ ), a bacterial water- forming NADH oxidase, to directly increase NAD+ by oxidization of NADH to NAD+ . Both _LbNOX_ and _MitoLbNOX_ , the latter containing the mitochondrial targeting sequence, have been shown to lower cytosolic NADH levels in HeLa cells, as demonstrated by the SoNar sensor and lactate/pyruvate ratios ( _51_ ). However, _MitoLbNOX_ not only had a more significant effect on the mitochondrial NAD+ /NADH ratio but also doubled the total cellular NAD+ /NADH ratio, while _LbNOX_ does not significantly affect the total cellular NAD+ /NADH ratio because most of the NADH within the cell is located in the mitochondria, and _MitoLbNOX_ directly acts in this compartment ( _51_ , _52_ ). Therefore, we used _MitoLbNOX_ to effectively boost the NAD+ /NADH redox ratio in RGC mitochondria. We generated _Opa1__V291D/+_ _; Rosa26__LSL- MitoLbNOX/+_ (hereafter, _V291D- MitoLbNOX_ ) mice that could conditionally overexpress _MitoLbNOX_ when crossed with an RGC- specific _Cre_ reporter line ( _Vglut2__Cre_ _;Rosa26__LSL- MitoTag_ ; hereafter _VG2- MitoTag_ ) (Fig. 8A). To verify the specificity of Cre- loxP–mediated conditional overexpression, we examined green fluorescent protein (GFP) expression within the _MitoTag_ cassette in _VG2- MitoTag_ mice, confirming localized GFP expression in RGCs (Fig. 8B). GFP expression remained stable in both _Opa1__V291D/+_ _;Vglut2__Cre/+_ _;Rosa26__LSL- MitoTag/LSL- MitoLbNOX_ ( _V291D- VG2MitoTag- MitoLbNOX_ ) and _Opa1__V291D/+_ _;Vglut2__Cre/+_ _;Rosa26__LSL- MitoTag/+_ ( _V291D- VG2- MitoTag_ ) mouse retinas (Fig. 8B). Next, we analyzed the functional outcomes and survival of RGCs from the _V291D- VG2MitoTag- MitoLbNOX_ and _V291D- VG2- MitoTag_ mice. PERG recordings at 180 days demonstrated significantly larger amplitudes in the _V291D- VG2- MitoTag- MitoLbNOX_ mice compared with their littermate control _V291D- VG2- MitoTag_ mice, indicating improved RGC function (Fig. 8C). In addition, whole- mounted retina immunostaining revealed a greater count of RGCs per peripheral square in _V291D- VG2MitoTag- MitoLbNOX_ mice (Fig. 8D), suggesting that _MitoLbNOX_ overexpression enhances RGC survival. On the basis of prior studies, boosting the NAD+ /NADH redox ratio through the overexpression of _MitoLbNOX_ could improve energy metabolism via the TCA cycle while also playing a crucial role in oxidative stress regulation ( _31_ , _53_ ). Thus, we evaluated TCA cycle activity and oxidative stress levels at the histological level. Immunostaining of the retinal section revealed elevated pyruvate dehydrogenase E1 component (PDHE1) and IDH3 expression in the ganglion cell layer of _V291D- VG2- MitoTag- MitoLbNOX_ mice, indicating heightened TCA cycle activity. In addition, the fluorescence intensity of 4- HNE in the ganglion cell layer significantly decreased in _V291D- VG2- MitoTag- MitoLbNOX_ mice, indicating a reduction in oxidative stress following _MitoLbNOX_ overexpression (Fig. 8E). To further explore whether the integrated stress response (ISR) contributes to the pathological phenotype, we performed additional immunofluorescence staining for eukaryotic translation initiation factor 2A (eIF2α), phosphorylated eIF2α (p- eIF2α), and activating transcription factor 4 (ATF4). No significant differences in either marker were detected among _WT_ , _V291D- VG2- MitoTag_ , and _V291D- VG2MitoTag- MitoLbNOX_ retinas, suggesting that the canonical ISR pathway is not prominently activated under these conditions. In contrast, nuclear factor erythroid 2- related factor 2 (NRF2) expression was markedly
reduced in the ganglion cell layer of _V291D- VG2- MitoTag_ retinas and restored to near- normal levels following _MitoLbNOX_ overexpression (fig. S6). This NRF2 restoration aligns with the 4- HNE findings and indicates that _MitoLbNOX_ mitigates oxidative stress by normalizing redox signaling rather than suppressing the ISR. To further assess the metabolic impact of _MitoLbNOX_ overexpression, we performed MALDI analysis on _V291D- VG2- MitoTag_ and _V291D- VG2- MitoTagMitoLbNOX_ retinas (fig. S7). These analyses revealed a trend toward increased ATP abundance in the inner retinal layer of _V291D- VG2MitoTag- MitoLbNOX_ mice, consistent with improved mitochondrial energy output. Together, these findings demonstrate that _MitoLbNOX_ overexpression restores mitochondrial redox balance, enhances metabolic capacity, reduces oxidative stress, and ultimately protects RGCs from degeneration in _Opa1__V291D/+_ mice.
### DISCUSSION
In this study, we developed a novel patient- specific _Opa1__V291D/+_ knock- in mouse model to replicate the most common type of mutation, the missense mutation, found in human patients with ADOA. This model accurately recapitulated the anatomical and functional phenotypes of ADOA, reflecting those observed in patients. Our findings showed that the V291D variant affected mitochondrial structure, disrupted OXPHOS complexes and redox state, and increased oxidative stress in _Opa1__V291D/+_ mice. Furthermore, our study revealed that the RGCs in the _Opa1__V291D/+_ mouse model did not shift their energy metabolism to glycolysis, unlike other retinal cells, which adapted to compensate for the bioenergetic crisis caused by the defective ETC function. These findings provide a potential explanation for the selective vulnerability of RGCs observed in ADOA. To explore potential therapeutic strategies, we overexpressed _MitoLbNOX_ in RGC mitochondria and observed enhanced TCA cycle activity, reduced oxidative stress, and restored RGC function and survival in _Opa1__V291D/+_ mice. These findings highlight the critical role of bioenergetic crisis and oxidative stress in RGC degeneration and suggest that targeting NAD+ /NADH homeostasis with _MitoLbNOX_ overexpression could serve as a promising therapeutic strategy for ADOA.
The genetics of _OPA1_ - related ADOA are more complex and diverse than initially recognized. Many of these variants lead to the premature truncation of the open reading frame, pinpointing haploinsufficiency as the primary disease mechanism. In contrast, missense variants, which are primarily clustered in the guanosine triphosphatase (GTPase) domain, are believed to exert a dominant- negative effect and are strongly associated with an increased risk of developing the more severe ADOA “plus” phenotype ( _14_ , _54_ – _56_ ). In our study, the V346D variant identified in our patient with ADOA and the V291D variant from our novel mouse model are located within the leading portion of the GTPase domain ( _57_ ). Our _Opa1__V291D/+_ mouse model exhibited significantly reduced OPA1 protein levels. Similarly, cultured cells transfected with the V291D variant showed diminished levels and stability of the OPA1 protein. In turn, treatment with MG132 only partially restored the OPA1 levels, indicating that its degradation is not fully reliant on the ubiquitin- proteasome system and suggesting the involvement of additional regulatory mechanisms that contribute to the instability of the OPA1 protein. Our findings indicate that the OPA1 protein is highly unstable in the presence of this variant, supporting the hypothesis that the V291D missense variant causes haploinsufficiency. Similarly, patient- derived fibroblasts
**Fig. 8.** **_MitoLbNOX_ overexpression enhanced RGC function, survival, TCA cycle, and reduced oxidative stress in** **_V291D- VG2- MitoTag_ -** **_MitoLbNOX_ mice.** ( **A** ) Schematic diagram of the strategy used to generate _V291D- VG2- MitoTag_ and _V291D- VG2- MitoTag_ - _MitoLbNOX_ mice. ( **B** ) immunostaining of retinal sections from the _VG2MitoTag_ , _V291D- VG2- MitoTag_ , and _V291D- VG2- MitoTag- MitoLbNOX_ mouse models, showing GFP fluorescence colocalized with RBPMS+ RGc. ( **C** ) Analysis of PeRG recordings at 180 days ( _n_ = 13 mice per group; one- way AnOvA with tukey’s test _P =_ 0.0023, 0.6653, and 0.0227 for _Opa1__+/+_ ( _WT_ ) compared to _V291D- VG2- MitoTag_ , _WT_ compared to _V291D- VG2- MitoTag_ - _MitoLbNOX_ , and _V291D- VG2- MitoTag_ compared to _V291D- VG2- MitoTag_ - _MitoLbNOX_ , respectively). ( **D** ) Quantification of RGcs in the peripheral zone of whole- mounted retinas at 180 days ( _n_ = 5 mice per group; one- way AnOvA with tukey’s test _P_ = 0.0032, 0.6147, and 0.0175 for _WT_ compared to _V291D- VG2- MitoTag_ , _WT_ compared to _V291D- VG2- MitoTag_ - _MitoLbNOX_ , and _V291D- VG2- MitoTag_ compared to _V291D- VG2- MitoTag_ - _MitoLbNOX_ , respectively). ( **E** ) Representative immunostaining images of Pdhe1, idh3, and 4- hne in retinal sections from _WT_ , _V291D- VG2- MitoTag_ , and _V291D- VG2- MitoTag_ - _MitoLbNOX_ mice. Analysis of the fluorescence intensity of Pdhe1 ( _n_ = 5 mice per group; one- way AnOvA with tukey’s test _P_ = 0.0027, 0.5287, and 0.0192, for _WT_ compared to _V291D- VG2- MitoTag_ , _WT_ compared to _V291D- VG2MitoTag_ - _MitoLbNOX_ , and _V291D- VG2- MitoTag_ compared to _V291D- VG2- MitoTag_ - _MitoLbNOX_ , respectively), idh3 ( _P_ = 0.0028, 0.6565, and 0.0006), and 4- hne ( _P_ = 0.0135, 0.7149, and 0.0033) immunostaining in the ganglion cell layer. data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, *** _P_ < 0.001.
carrying missense variants in the GTPase domain of OPA1 display haploinsufficiency characterized by decreased OPA1 protein expression and a shortened protein half- life ( _58_ , _59_ ). Therefore, missense variants in the GTPase domain could lead to haploinsufficiency or a dominant- negative effect. Further experiments, including an active GTPase pull- down assay, are necessary to confirm this hypothesis.
Changes in OPA1 protein levels can disrupt the communication between mitochondria and the cell nucleus, resulting in significant transcriptional changes in neurons ( _60_ ). These alterations in mitochondrial dynamics can lead to a loss of coordination between mitochondrial and nuclear gene expression, particularly in the context of pathways that are involved in energy metabolism ( _61_ ). The cellular environment and energy- usage status can also affect the expression of both mitochondrial and nuclear- encoded energy metabolism transcripts, thereby highlighting the importance of a synchronized modulation between the nucleus and mitochondria in response to energy deficits and nutrient shifts ( _62_ , _63_ ). Furthermore, when mitochondrial dynamics are disturbed by OPA1 protein mutations, mitochondrial dysfunction can trigger retrograde signaling, in which stress signals are transmitted from the mitochondria to the nucleus ( _64_ , _65_ ). This signaling cascade can lead to changes in the expression of nuclear- encoded mitochondrial genes. A similar phenomenon is observed during the development of neurodegenerative disorders, such as Parkinson’s, Alzheimer’s, and Huntington’s diseases, in which mitochondrial abnormalities are closely associated with a significant down- regulation of the nuclear- encoded ETC and OXPHOS proteins, thus contributing to cellular aging in neural tissues ( _66_ – _68_ ). Increased oxidative stress, which can damage nucleic acids, likely plays a role in this premature aging ( _68_ , _69_ ). Furthermore, cells may activate apoptosis in response to oxidative stress, which can affect the expression of genes related to mitochondrial biogenesis ( _70_ ). The coordinated down- regulation of ETC and OXPHOS regulation in both mitochondria and the nucleus results in impaired mitochondrial metabolism, diminished energy production, and heightened oxidative stress, thereby creating a detrimental cycle that further promotes apoptosis ( _71_ ). This cascade aligns with the downregulation of ETC- related genes indicated by our snRNA- seq results, thus highlighting a potential mechanism for RGC- specific vulnerability in ADOA.
It is widely acknowledged but not well understood that RGCs are more susceptible than other retinal cells to mitochondrial dysfunction, although photoreceptors have the highest density of mitochondria in the retina. In addition, it remains unclear whether this vulnerability is primarily caused by a bioenergetic crisis, oxidative stress, or a combination of both ( _1_ , _15_ , _72_ ). A previous study introduced a mouse model of Leber hereditary optic neuropathy (LHON), which is a mitochondrial optic neuropathy caused by a variant in the _ND6_ gene, a key subunit of Complex I, and found that increased oxidative stress is likely a primary pathogenic factor in this disease, whereas ATP production was not affected ( _73_ ). Consequently, the accumulation of oxidative stress from impaired mitochondria is one of the major causes of RGC degeneration in LHON; thus, many studies have focused on antioxidants as potential treatments ( _16_ , _33_ ). Previous research has shown that idebenone, which bypasses defective Complex I and acts as an antioxidant, is a promising candidate that is currently an approved therapy for LHON ( _74_ , _75_ ). However, about half of the patients did not respond to this treatment, suggesting that oxidative stress alone is not the sole issue in LHON ( _76_ ). Furthermore, another report showed that multiple therapeutic targets affect
mitochondria and demonstrated that pathways beyond oxidative stress, including energy metabolism, mitochondrial biogenesis, and mitophagy, also play significant roles in fibroblasts derived from patients with LHON ( _77_ ). Here, we performed several experiments to assess the energetic and oxidative stress profiles of the _Opa1__V291D/+_ mouse retina. Our findings revealed an increase in oxidative stress and a reduction in ATP levels in _Opa1__V291D/+_ mouse retinas. These results suggest that both a bioenergetic crisis and oxidative stress contribute to the development of RGC degeneration in ADOA. Although both LHON and ADOA lead to RGC degeneration due to mitochondrial dysfunction, their underlying mechanisms may vary.
Furthermore, our results suggest that the increased oxidative stress and reduced ATP production observed in the _Opa1__V291D/+_ retina are likely attributable to compromised Complex I activity. Complex I not only plays a significant role in maintaining the balance of oxidative stress but also functions as the entry point for electrons in the ETC and as a proton pump to create a proton gradient ( _43_ , _78_ ). Although electrons can still enter the ETC through Complex II via the reduced form of flavin adenine dinucleotide (FADH2) if there is damage to Complex I, this affects the efficiency of OXPHOS and ATP production because Complex II does not contribute to proton translocation ( _79_ ). The decrease in Complex I activity observed in the _Opa1__V291D/+_ retina may be partly attributed to abnormalities in the inner mitochondrial membrane. The OPA1 protein, which is primarily responsible for inner mitochondrial membrane fusion, plays a critical role in maintaining the structure of mitochondrial cristae. Defective OPA1 protein can disrupt crista remodeling, destabilize respiratory complexes, and ultimately impair Complex I function ( _80_ ). Consistent with this mechanism, our blue- native polyacrylamide gel electrophoresis revealed a trend toward reduced levels of Complex I–containing supercomplexes in _Opa1__V291D/+_ retinas (fig. S8), suggesting subtle alterations in supercomplex stability. Although these changes did not reach statistical significance, they align with prior in vitro evidence that _OPA1_ variants can affect the structural organization of Complex I ( _81_ , _82_ ). Therefore, the relationship among _OPA1_ variants, mitochondrial structural changes, and Complex I dysfunction is closely interconnected, with each factor influencing the others in the pathogenesis of ADOA.
Although the V291D variant impaired energy production and increased oxidative stress throughout the retina, the functional impairments in _Opa1__V291D/+_ mice were limited to RGCs. This selective degeneration may be attributed to their heightened vulnerability to energy deficits, which are driven by their high energy demands, long axons, and lack of a myelinated sheath before the lamina cribrosa ( _83_ , _84_ ). In contrast, photoreceptors, which have the highest density of mitochondria in the retina, prefer glycolysis for energy production and can use lipids to compensate for ATP deficiencies ( _45_ , _47_ , _48_ , _85_ , _86_ ), whereas inner retinal cells, including RGCs, rely heavily on mitochondrial ETC and OXPHOS and exhibit a lower glycolytic activity ( _45_ , _46_ ). This greater reliance on ETC and OXPHOS renders RGCs particularly sensitive to mitochondrial dysfunction, explaining their susceptibility to degeneration in ADOA ( _83_ , _84_ , _87_ ). Although ATP production was generally decreased in the retina of the _Opa1__V291D/+_ mouse model, a metabolic shift toward glycolysis was observed, particularly in the outer retinal layers, suggesting that photoreceptors compensate for ATP deficiency by up- regulating glycolysis, consistent with previous findings ( _48_ , _88_ , _89_ ). This highlights the relationship between altered energy metabolism and the metabolic flexibility of retinal cell types ( _90_ ). The inability of RGCs
to adapt to defective ETC function, in contrast to the metabolic flexibility of photoreceptors, underscores the significant role of compromised energy metabolism in RGC degeneration associated with ADOA. This deficiency in energy production further elevates oxidative stress, creating a harmful cycle that worsens neuronal degeneration ( _91_ ).
Our _Opa1__V291D/+_ missense variant mouse model showed RGC abnormalities, both anatomically and functionally, closely matching the clinical presentation of human patients with ADOA. Although previous mouse models with truncated OPA1 proteins revealed changes in the shape and structure of mitochondria in the whole mouse retina and optic nerve ( _17_ , _21_ , _23_ ), transcriptomic changes in the retina at single- cell resolution remain unexplored. Moreover, the selective vulnerability of RGCs, with photoreceptors remaining largely unaffected, has yet to be fully understood. Our study revealed a significant downregulation of glycolytic proteins in the ganglion cell layer of the _Opa1__V291D/+_ retinas, as assessed using immunostaining; furthermore, our snRNA- seq analysis identified down- regulated energy production–related genes, including those involved in ETC and glycolysis, specifically in the RGC cluster. However, we did not detect significant changes in these genes related to energy- production pathways in other retinal cell types between _WT_ and _Opa1__V291D/+_ mice, including cones and rods, thus providing a potential explanation for the lack of significant photoreceptor dysfunction in our patient and mouse model. This impaired metabolic adaptation in RGCs likely exacerbates the bioenergetic crisis, ultimately contributing to their selective degeneration.
Although the cause- and- effect relationship between oxidative and metabolic stress is not fully understood in the pathogenesis of ADOA, we believe that both factors contribute to RGC degeneration in ADOA and that interrupting this vicious cycle could serve as a potential therapeutic target for the condition. In our study, we demonstrated that increasing the NAD+ /NADH redox ratio by _MitoLbNOX_ overexpression could improve energy metabolism via the TCA cycle and reduce oxidative stress in the _Opa1__V291D/+_ mouse model. This, in turn, promoted neuronal survival and successfully mitigated the detrimental effects of the _Opa1_ variant, restoring both functional integrity and survival in RGCs of _Opa1__V291D/+_ mice. In mitochondria, NAD+ serves as a coenzyme for three rate- limiting enzymes in the TCA cycle, where it is reduced to NADH, generating ATP for direct energy supply and producing FADH2 as an alternative electron donor for Complex II in the ETC ( _92_ ). Beyond our findings, a previous showed that _MitoLbNOX_ overexpression could activate the TCA cycle by increasing the NAD+ /NADH redox ratio in m.3243A>G fibroblasts ( _53_ ). Moreover, evidence from other disease models has shown that replenishing NAD+ levels can increase energy metabolism, reduce oxidative stress, and prolong survival across various cell types, including those in the heart, liver, and inflammatory cells ( _93_ – _96_ ). Last, our findings following _MitoLbNOX_ overexpression reaffirmed the critical role of bioenergetic crisis and oxidative stress, driven by Complex I dysfunction, in RGC degeneration, highlighting NAD+ /NADH homeostasis as a promising therapeutic target for preventing RGC loss in ADOA.
Despite evidence that _Opa1__V291D/+_ RGCs exhibit impaired metabolic compensation and heightened vulnerability to mitochondrial dysfunction, the precise mechanisms underlying this cell type–specific susceptibility remain incompletely understood. Although our data show that Complex I–driven NAD+ /NADH imbalance selectively disrupts glycolytic and TCA cycle rewiring in RGCs, the reason this effect is confined to inner retinal neurons rather than photoreceptors remains unresolved. A previous publication highlighted that
mitochondria display distinct “mitotypes” across cell types, reflecting specialized structural and functional adaptations to unique energetic demands ( _78_ ). In this context, RGCs may depend more heavily on Complex I–linked redox balance, whereas photoreceptors may have greater metabolic flexibility or alternative substrate usage that buffers against OXPHOS perturbations. Nevertheless, the molecular determinants of this selective vulnerability remain to be fully elucidated.
In conclusion, we developed the _Opa1__V291D/+_ missense mouse model, which recapitulated ADOA phenotypes. The V291D variant reduced _OPA1_ protein stability and expression, supporting a haploinsufficiency mechanism. It impaired mitochondrial morphology and Complex I function, leading to oxidative stress, ATP depletion, and an energetic crisis. As a compensatory response, the retina exhibited a metabolic shift toward glycolysis, but RGCs failed to upregulate glycolytic proteins. Spatial metabolomics, immunostaining, and snRNA- seq revealed pronounced bioenergetic crisis and downregulated energy- production genes in RGCs, highlighting their selective vulnerability in ADOA. Notably, increasing mitochondrial NAD+ /NADH redox ratio by _MitoLbNOX_ overexpression in RGC could improve energy metabolism, reduce oxidative stress, and enhance RGC survival, underscoring the therapeutic potential of targeting mitochondrial metabolism in ADOA.
### MATERIALS AND METHODS
## Study design
The objective of this study was to investigate the impact of a patientderived _Opa1_ missense variant on RGC degeneration, as well as to determine why RGCs are particularly vulnerable to mitochondrial dysfunction in ADOA. To achieve this, we generated a novel patientspecific knock- in _Opa1__V291D/+_ mouse model and conducted survival experiments to analyze functional phenotypes, as well as nonsurvival experiments for anatomical phenotyping and molecular assessments. Immunostaining, spatial metabolomics, and snRNA- seq were performed to examine the impact of the _Opa1_ variant at both the tissue and cellular levels. In addition, we examined how increasing the NAD+ /NADH redox ratio in RGCs affects their survival in our ADOA mouse model. This study was approved by the Institutional Review Board of Columbia University (no. AAAV3523) and adhered to the principles of the Declaration of Helsinki. Because of the retrospective nature of the study and the use of deidentified historical data, the Institutional Review Board granted a waiver of informed consent. All animal experiments were approved by the Institutional Animal Care and Use Committee of Columbia University (no. AC- AABQ7582).
## Patients with ADOA and mouse models
Patients with clinically diagnosed ADOA were reviewed, and their genetic testing reports were assessed at the Columbia University Irving Medical Center. An _OPA1_ missense variant was identified in one patient and was used to generate a knock- in mouse model. The patientspecific _Opa1__V291D/+_ mouse model was created by C.- S.L. The V291D point variant was introduced using the GalK pop- in- pop- out method into a bacterial artificial chromosome (BAC) clone (RP23- 229C8) from the BACPAC Resources Center (https://bacpacresources.org). A gene- targeting vector was prepared using the BAC recombineering method and electroporated into KV1 (129S6 hybrid) embryonic stem (ES) cells, to generate targeted ES clones via homology recombination;
the method showed an absence of aberrant splicing donor or acceptor activity. This knock- in mouse harbored a T- to- A missense variant, which converted the 291st amino acid of OPA1 from valine to aspartic acid. These mice were backcrossed to the _C57BL/6J_ strain (JAX no. 000664, the Jackson Laboratory) for five generations and then genotyped, which confirmed the absence of the _rd8_ variant ( _97_ ). All mice analyzed in this study were heterozygous _Opa1__V291D/+_ mice exhibiting normal longevity and fertility. In subsequent experiments, littermatecontrol _WT_ mice ( _Opa1__+/+_ ) were used for comparisons with _Opa1__V291D/+_ mice. To label mitochondria and assess their morphological features in these mice, we crossed the _Opa1__V291D/+_ mice with _mito::mKate2_ reporter mice (JAX no. 032188, the Jackson Laboratory), to express the fluorescent mKATE2 protein specifically in mitochondria. Housing for these animals was provided by the animal care facility of the Institute of Comparative Medicine at Columbia University.
## Pattern electroretinography
The PERG was conducted as described in prior publications ( _98_ , _99_ ). In brief, we used the PERG Animal System (Jorvec Corp, Miami, FL) for our recordings. The PERG signals from each eye were desynchronized using a phase- locking averaging method with two noncorrelated frequencies (right eye, every 492 ms; left eye, every 496 ms) and then averaged over three consecutive session blocks ( _98_ ). To assess the RGC- specific function, we measured the P1N2 amplitude from the peak positive waves (P1) to the lowest negative waves (N2) recorded in the grand- average PERG waveforms.
retinas were fixed in cold 4% paraformaldehyde in phosphate- buffered saline for 1 hour. To identify RGCs, a mouse anti- BRN3A antibody (1:50, MAB1585, Millipore) was used, followed by incubation with a secondary donkey anti- mouse antibody (1:200, 715- 225- 151; Jackson ImmunoResearch). RGCs were quantified using flat- mounted retinas, as described previously ( _102_ , _104_ ). We obtained 4, 4, and 12 squares with a size of 300 μm by 300 μm from each central, midperipheral, and peripheral retinal zone, respectively. The RGC counts from all squares were then totaled and analyzed. All images were acquired using a Nikon Ti Eclipse inverted confocal microscope. BRN3A+ cells were counted semiautomatically and quantitatively using the ImageJ software (https://imagej.net/ij/).
## Confocal microscopy assessment of mitochondrial morphology in mouse optic nerves
To analyze mitochondrial characteristics, we used SDCM with SRRFs in both _WT_ and _Opa1__V291D/+_ mice. Cryosections of optic nerves were prepared from both groups, and mitochondria were visualized through mKate2 expression, which enabled red fluorescence excitation (561 nm/594 nm) using an SDCM system (Dragonfly 600, Oxford Instruments Andor) with an iXon 888 Life EMCCD camera. Superresolution images were captured using a 100× oil objective and the Andor FUSION software (Oxford Instruments Andor), which operates the SRRF function. After acquiring the images, we applied deconvolution techniques and analyzed the data using the Surface Rendering Model provided in the iMaris software (v10.2) to thoroughly compare mitochondrial characteristics between the mouse models.
## Flash electroretinography
Flash ERG assessments were conducted according to previous publications ( _100_ ) using an Espion system coupled with a Ganzfeld stimulator (Colordome, Diagnosys LLC, Lowell, MA), to measure scotopic and photopic serial intensities. To assess the STR, the light intensities of the stimuli that were used for scotopic serial- intensity ERG were −5.6, −5.3, and −5.0 log cd·s/m2 in sequence. After a 10- min period of light adaptation, PhNRs were elicited using three different stimulus intensities, i.e., 0, 1, and 2 log cd·s/m2 , against a 10- cd·s/m2 rod- saturating green background. For each intensity level, an average of 25 flashes was calculated, with an interstimulus interval of 3000 ms. The positive and negative STRs were measured at 100 and 233 ms, respectively. To specifically evaluate the RGC function, PhNR amplitudes were measured from the baseline to the PhNR trough.
## Spectral domain–optical coherence tomography
We performed live imaging to measure the thickness of the RNFL using an SD- OCT imaging device (Envisu UHR2210, Bioptigen, Durham, NC, USA), which provides an axial resolution of 1.75 μm in tissue, according to previously established protocols ( _101_ ). A rectangular scan of 1.8 mm in length and width was performed, with 0° angle adjustments and no horizontal or vertical offsets. The scan settings included 1000 A- scans per B- scan, 100 B- scans, and 10 frames per B- scan, with 80 inactive A- scan lines per B- scan and one volume captured (fig. S9). The resulting 10- frame OCT images were averaged using the Bioptigen InVivoVue (v2.4) software and then further processed with the Bioptigen Diver (v.3.4.4) software, to obtain measurements of RNFL thickness.
## RGC counting in flat- mounted retinas
Immunolabeling and fluorescent staining of flat- mounted retinas were performed as previously described ( _102_ , _103_ ). Eyecups for flat- mounted
## Transmission electron microscopy
We used TEM to examine the morphology of mitochondria and the myelination of axons. Ultrathin cross sections were obtained from the optic nerve and stained with uranyl acetate and lead citrate for contrast enhancement. These sections were imaged using a Hitachi 7100 transmission electron microscope (TEM instrument; Hitachi, Tokyo, Japan) equipped with an advanced digital camera system for microscopy techniques.
## Immunoblotting
Mouse retinas were dissected at 180 days of age and homogenized with radioimmunoprecipitation assay (RIPA) lysis and extraction buffer (89900, Thermo Fisher Scientific), supplemented with protease and phosphatase inhibitor cocktails (P0044 and P8340, MilliporeSigma). This process was followed by sonication using an SLPe Digital Sonifier (Branson Ultrasonics, Brookfield, CT). The resulting supernatant was collected for protein quantification and subsequent Western blot analysis of total retinal proteins. Protein concentrations were determined with a Pierce BCA assay kit (23225, Thermo Fisher Scientific). For electrophoresis, proteins were denatured and separated using a Mini Blot system (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). The separated proteins were transferred onto polyvinylidene fluoride membranes (PB5240, Invitrogen) with a Power Blotter system (PB0012, Invitrogen). The membranes were incubated in blocking buffer for 30 min, followed by applying primary antibodies and incubating at 4°C overnight. Secondary antibodies were applied at room temperature for 2 hours. Details of the primary and secondary antibodies, as well as other materials, are provided in table S4. Signals were visualized using an iBright 1500 Imaging System (Invitrogen, Thermo Fisher Scientific), and data were analyzed with the iBright Analysis Software (v5.2.1).
## Quantitative real- time PCR for** **_Opa1_
To assess the gene expression levels of _Opa1_ between mutant and control mice, total RNA was extracted from mouse retinas using RNeasy kits (QIAGEN), following the manufacturer’s guidelines. cDNA was synthesized using the SuperScript VILO cDNA Synthesis Kit (Invitrogen), following the provided instructions. qPCR was then conducted using dye- based techniques with specifically designed primers (table S2), and samples were run in technical triplicate. The qPCR mixtures were prepared using the PowerTrack SYBR Green Master Mix (Thermo Fisher Scientific). A CFX Connect Realtime PCR Detection System (Bio- Rad Laboratories, Hercules, CA, USA) was used to monitor and analyze gene expression.
## Cell culture for protein stability testing
HEK293 cells (BCRC 60019; Bioresource Collection and Research Center, Hsinchu, Taiwan) were cultured to form a monolayer in a medium supplemented with 10% fetal bovine serum. Lipid- based transfections (Lipofectamine 3000, Invitrogen) were conducted using cytomegalovirus plasmid vectors (pcDNA3.1, GenScript) that carried a 3X Flag tag to insert the _Opa1__WT_ and _Opa1__V291D_ genes. After transfection, the cells were lysed, and immunoprecipitation was performed to isolate the ubiquitinated OPA1 protein. MG132 (25 μM) was used to inhibit protease activity. The isolated proteins were analyzed to evaluate the ubiquitination status and stability of the OPA1 protein in the lysates obtained from HEK293 cells transfected with _Opa1__WT_ and _Opa1__V291D_ .
## Coimmunoprecipitation
For coimmunoprecipitation assays, HEK293 cells were transiently transfected with either an empty control vector or a Flag- tagged OPA1 expression construct using Lipofectamine 3000 (Thermo Fisher Scientific), according to the manufacturer’s protocol. After 48 hours, cells were harvested and lysed in ice- cold RIPA buffer supplemented with protease inhibitors. Clarified lysates were incubated overnight at 4°C with anti- Flag M2 agarose beads (Sigma- Aldrich, M8823). Bound proteins were washed, eluted, and subjected to immunoblot analysis. Experimental procedures were performed following previously published protocols ( _105_ ).
## Analysis of mitochondrial respiratory and hydrolytic function in retinas
To evaluate mitochondrial function in the _Opa1__V291D/+_ mouse model, we used the RIFS and HyFS assays on a Seahorse XF analyzer (Agilent Technologies, Cedar Creek, TX, USA), as described elsewhere ( _40_ – _42_ ). Retinal and heart tissues were harvested and immediately frozen at −80°C and then sent for analysis. Frozen tissues were placed in tubes containing four 3- mm zirconium beads and homogenized in mitochondrial assay solution [MAS buffer: 70 mM sucrose, 220 mM mannitol, 5 mM KH2PO4, 5 mM MgCl2, 1 mM EGTA, and 2 mM Hepes (pH 7.4)] using a bead homogenizer (Benchmark Scientific, Sayreville, NJ, USA) for 30 s at 6.5 m/s. The homogenates were then centrifuged at 1000 _g_ for 5 min at 4°C, and the supernatants were collected. Protein concentrations were determined, with retinal samples showing optimal responses to substrates at concentrations of 10 μg in the assays. This procedure yields a mixed mitochondrial homogenate containing disrupted mitochondria and submitochondrial particles with varying membrane orientations. Because the samples were previously frozen, exogenous NADH can access the matrix- facing NADH- binding site of Complex I. Thus, NADH was used directly as the substrate (1 mM) to assess Complex I–linked respiration, rather than pyruvate/malate.
Complex II–linked respiration was measured using 5 mM succinate in the presence of 2 μM rotenone to inhibit Complex I. To inhibit the ETC upstream of Complex IV, 4 μM antimycin A (a Complex III inhibitor) and 2 μM rotenone (a Complex I inhibitor) were used. Complex IV activity was assessed by supplying electrons through 0.5 mM _N_ , _N_ , _N_ ', _N_ '- tetramethyl- p- phenylenediamine (TMPD; maintained in a reduced state by 1 mM ascorbate), with 50 mM azide serving as a Complex IV inhibitor. Oxygen consumption rates were accurately measured and normalized to protein content and mitochondrial density using MTDR, to account for variations in sample processing or intrinsic mitochondrial differences. Because of limited retinal material, two complexes were typically measured per well. In the HyFS assay, the hydrolytic capacity of Complex V (ATP synthase) was assessed under uncoupled conditions. The assay was initiated with 5 mM succinate and 2 μM rotenone to measure respiratory capacity through Complex II. The ETC was then shut down with 2 μM antimycin A, and 1 μM carbonyl cyanide _p_ - trifluoromethoxyphenylhydrazone was added to ensure complete uncoupling. Subsequently, 20 mM ATP was injected to drive ATP synthase in the reverse (hydrolytic) direction, while 5 μM oligomycin was added to inhibit Complex V activity. Because the mitochondrial membranes are disrupted, ATP freely accesses the matrix- facing catalytic site of Complex V, allowing direct measurement of ATP hydrolysis–driven oxygen consumption independent of ADP/ATP translocase function. The output data from three technical replicates were averaged for analysis. The ATP hydrolytic capacity measurements were normalized to Complex V expression, as determined using immunoblotting for ATP5A1.
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## MALDI- TOF MS imaging
To investigate metabolomics changes in the mouse retina, MALDITOF MS imaging was performed at the MALDI MS Imaging Facility, Advanced Science Research Center, The City University of New York. Mouse eyeballs were harvested at 200 days of age, embedded in 4% CMC (no. 419273, Sigma- Aldrich) at −10°C, and snap frozen on dry ice. Cryosections (10- μm thickness) were prepared using a CryoStar NX70 (Thermo Fisher Scientific), mounted on indium tin oxide–coated slides (no. 8237001, Bruker Daltonics), and desiccated under vacuum for 30 min. Matrix deposition was performed with an HTX M5 sprayer (HTX Technologies) using 2,5- dihydroxybenzoic acid (DHB) (no. D2933, TCI Chemicals) 40 mg/ml in methanol/water, 70/30 at 85°C for 8 cycles or _N_ - (1- naphthyl) ethylenediamine dihydrochloride (NEDC, no. 222488, Sigma- Aldrich) 10 mg/ml in isopropanol/water, 70/30 at 80°C for 30 cycles. The same spray parameters were used for both matrices: velocity of 1300 mm/min; track spacing of 2 mm; N2 pressure of 10 psi (68.95 kPa); flow rate of 3 liters/min; and nozzle height of 40 mm. Initial spectra acquisition was conducted using a MALDI- TOF MS Autoflex (Bruker Daltonics) in positive ion (DHB) or negative ion (NEDC) mode, which was calibrated with red phosphorus (no. 343242, Sigma- Aldrich). The following settings were used for both ion modes: raster width of 25 μm, laser smartbeam of “minimum,” laser frequency of 500 Hz, 500 shots per position, and mass/charge ratio ( _m_ / _z_ ) range of 60 to 1200. Ion images were processed using FlexImaging (v3.0) and SCiLS Lab (v2015b), normalized via root mean square, and a bin width of ±0.10 to ±0.20 according to peak width at a certain _m_ / _z_ . The spectra were interpreted manually, and the analytes were assigned according to a method described previously ( _106_ ). To validate and extend metabolic coverage, high- resolution imaging was subsequently performed using a timsTOF fleX MALDI- 2 instrument (Bruker Daltonics) in both positive (DHB)
and negative (NEDC) ion modes. The instrument was operated with the following settings: raster width 20 μm, SmartBeam laser in “Single” mode, laser frequency 10,000 Hz, 200 shots per pixel (positive mode), 250 shots per pixel (negative mode), and an _m_ / _z_ acquisition range of 50 to 1000. Data were acquired using timsControl software and processed with SCiLS Lab using the same normalization strategy described above. Key metabolites were detected as follows: AMP at _m_ / _z_ 346.1 as [AMP- H]− , G6P at _m_ / _z_ 171.0 as [G6P- H]− , pyruvate at _m_ / _z_ 87.0 as [pyruvate- H]− , and ATP at _m_ / _z_ 508.0 as [ATP + H]+ . Quantification was performed within defined regions of interest in the tissue.
## Hematoxylin and eosin staining
Hematoxylin and eosin staining was performed on tissue sections after MALDI imaging, to access the histology of the MALDI images. The residual matrix was removed by rinsing slides with 95% ethanol, after which the sections were stained with Hematoxylin Gill No. 1 and Eosin Y (Sigma- Aldrich) according to the manufacturer’s instructions. The stained sections were imaged using a Leica Aperio CS2 slide scanner at ×20 magnification with a 0.75–numerical aperture Plan Apo objective. These images provided anatomical context for mass spectral data, allowing the establishment of precise correlations between molecular and histological features. Quantification was performed within defined regions of interest in the tissue.
## Immunostaining
To assess protein expression distribution in mouse retinal histology, immunofluorescence was performed on cryosections of mouse retinas at 360 days of age according to previously established protocols ( _100_ ). Briefly, slides were prepared using mouse retinas embedded in optimal cutting temperature compound (Tissue- Tek O.C.T. Compound, Sakura Finetek). The primary and secondary antibodies listed in table S4 were used for staining. Imaging was carried out using a Zeiss LSM 900 microscope equipped with an Airyscan super- resolution image scanning system (Carl Zeiss, Germany). Z- stack images spanning 5 μm with a step size of 0.3 μm were acquired from all retinal sections. Postacquisition processing and deconvolution were performed using the Airyscan Joint Deconvolution feature in the ZEN Blue software (v3.7). Images from matched mutant and _WT_ samples were captured during the same experimental session under identical imaging settings. The fluorescence intensity in each maximum projection image was manually segmented and quantitatively measured using the ImageJ software (https://imagej.net/ij/).
## ATP measurements from mouse retinas
ATP levels were measured in the retinas using a commercially available kit [ab83355, ATP Assay Kit (Colorimetric), Abcam] according to the manufacturer’s instructions. Fresh retinal tissue from both eyes of each mouse was carefully dissected and homogenized in the assay buffer. The homogenate was centrifuged at 13,000 _g_ for 5 min at 4°C, and the resulting supernatant was collected for protein quantification and subsequent analysis. To prevent enzyme interference in the assay, deproteinization was performed using a kit (ab204708, Deproteinizing Sample Preparation Kit, Abcam). After a 30- min incubation, the ATP assay was conducted, and optical density readings were taken at 570 nm using a microplate reader.
## NAD****+** **measurements from mouse retinas
To assess the levels of NAD+ and NADH in mice, we used a commercially available kit [ab65348, NAD+ /NADH Assay Kit (Colorimetric),
Abcam] following the manufacturer’s instructions. We collected retinas from each mouse, homogenized them, and centrifuged the mixture at 14,000 _g_ for 5 min at 4°C. Next, we transferred the supernatant to a 10- kDa spin column (ab93349, 10kD Spin Column, Abcam) and centrifuged it at 10,000 _g_ for 20 min at 4°C. The filtrate was collected for protein quantification and the NAD assay. Optical density readings were taken at 450 nm using a microplate reader at room temperature 1 hour after the procedure.
## GSH measurements from mouse retinas
Total GSH and reduced GSH levels were measured using a commercially available kit [ab239709, GSH+GSSG/GSH Assay Kit (Colorimetric), Abcam], following the manufacturer’s instructions. Retinal tissues were collected from both eyes of each mouse and homogenized in the buffer supplied with the kit. Protein quantification was conducted before adding 5% 5- sulfosalicylic acid to precipitate the proteins in the samples. Next, the reaction mix and substrate solution were added to the samples and incubated for 10 min. Optical density readings were taken at 415 nm using a microplate reader at room temperature 10 min after the procedure. The levels of GSH and GSSG were calculated on the basis of the optical density readings.
## SOD measurements from mouse retinas
SOD levels were measured using a commercial kit [ab65354, Superoxide Dismutase Activity Assay Kit (Colorimetric), Abcam] following the manufacturer’s instructions. Retinal samples were homogenized in ice- cold immunoprecipitation lysis buffer (no. 87787, Thermo Fisher Scientific) that contained 1 mM phenylmethylsulfonyl fluoride protease inhibitor (no. 36978, Thermo Fisher Scientific). The homogenates were then centrifuged at 14,000 _g_ for 5 min at 4°C, and the supernatants were collected for analysis. The SOD assay was performed by mixing the supernatant with the working solution provided in the kit, followed by incubation at 37°C for 20 min. Optical density readings were obtained at 450 nm using a microplate reader to quantify SOD activity.
## Lactate measurements in mouse retinas
The levels of lactate in the retinas were measured using a commercially available kit [ab65331, l- Lactate Assay Kit (Colorimetric), Abcam] according to the manufacturer’s instructions. Fresh retinal tissue from both eyes of each mouse was carefully dissected and homogenized. The homogenate was then centrifuged at 14,000 _g_ for 5 min at 4°C, and the resulting supernatant was collected. Deproteinization (ab204708, Deproteinizing Sample Preparation Kit, Abcam) was carried out to prevent lactate degradation by endogenous LDH. The deproteinized supernatant was then used for the assay. After a 30- min incubation at room temperature, the optical density was measured at 450 nm on a microplate reader.
## Single- nucleus RNA sequencing
To investigate the impact of this _Opa1_ variant on the retinal transcriptomes at the single- cell level, we performed snRNA- seq on pooled frozen retinal tissues. Nucleus extraction was performed using the Miltenyi Nuclei Extraction Buffer (Miltenyi Biotec) according to the manufacturer’s guidelines. Upon isolation, the nuclei were counted using trypan blue and a Countess III Automated Cell Counter (Thermo Fisher Scientific, Waltham, MA, USA). snRNA libraries were prepared using the Chromium Single Cell 3′ kit (10x Genomics) and sequenced on an Illumina platform using standard protocols. After obtaining the sequencing data, we used Cell Ranger
(v8.0) with default parameters to generate a filtered_feature_bc_ matrix.h5 file containing cell barcodes and transcript counts for each sample. The data were aggregated using the Cell Ranger aggr program. The integrated dataset was first imported into the Rosalind platform (www.rosalind.bio/) for dimension reduction and unsupervised clustering using Cell Ranger Graph Based Clustering (10x Genomics). The dataset was then loaded into R (v4.2) and the Seurat package (v5.0) ( _107_ ). Cell types were annotated using SC- type (v1.0) ( _108_ ) with cell markers for major retinal cells (table S3). A pathway enrichment analysis was performed using clusterProfiler (v4.10.1) ( _109_ ) with the REACTOME ( _110_ ) and WikiPathways ( _111_ ) databases. The results of differential gene expression analyses were visualized using heatmaps and dot plots wrapped in the Seurat package, and normalization was performed using log2 transformation. The snRNA- seq data have been deposited into the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus repository (GSE292269).
## High- resolution spatial transcriptomics of the mouse retinas
Mouse eyes from 280- day _WT_ and _Opa1__V291D/+_ mice were enucleated after euthanasia. Whole eyecups were fixed in 10% neutralbuffered formalin for 12 to 24 hours, dehydrated, and paraffin embedded using standard histological procedures. Retinal sections (10- μm thickness) were collected onto 10x Genomics Visium HD FFPE Spatial Gene Expression slides. Sections were deparaffinized, stained with hematoxylin and eosin, and imaged to document tissue morphology and orientation. Target retrieval, probe hybridization, and on- slide chemistry were performed according to the 10x Genomics Visium HD FFPE protocol, with minor optimizations for retinal tissue integrity. Spatial gene expression libraries were constructed per manufacturer instructions, sequenced on an Illumina platform, and processed using Space Ranger (10x Genomics) for alignment, segmentation, and feature quantification. Annotation of ganglion cell–enriched regions was done by using QuPath ( _112_ ). Downstream spot- level analysis and clustering were performed in Seurat package (v5.0) ( _107_ ).
## Generation of RGC- specific** **_MitoLbNOX_ overexpression in
## _Opa1_****_V291D/+_** **mice
In this study, we generated _Rosa26__LSL- MitoLbNOX_ ( _LoxP- Stop- Lox[LSL]MitoLbNOX_ ) mice using a method similar to that used for the _Rosa26__LSL- MitoTag_ line (JAX no. 032290, the Jackson Laboratory), which incorporates _3XHA- EGFP- OMP25_ ( _MitoTag_ cassette) into the _Rosa26_ locus for targeted mitochondrial _EGFP_ expression ( _113_ ). We constructed a targeting vector containing a _CAG_ promoter, a _loxP_ - flanked reversed neomycin cassette, an _SV40 poly- adenylation_ sequence, and cDNA encoding _MitoLbNOX_ from the pUC57- mito _Lb_ NOX plasmid (Addgene plasmid no. 74448), which was linearized and targeted to intron 1 of the mouse _Rosa26_ gene. To achieve conditional _mitoLbNOX_ overexpression in _Opa1__V291D/+_ mice, we crossed _LSL- MitoLbNOX_ mice with _Opa1__V291D/+_ mice, generating _Opa1__V291D/+_ _; Rosa26__LSL- MitoLbNOX/+_ offspring ( _V291D- MitoLbNOX_ ). For the RGC- specific mitochondrial reporter _Cre_ line, we created double homozygous _Vglut2__Cre_ _; Rosa26__LSL- MitoTag_ ( _VG2- MitoTag_ ) mice by crossing _Vglut2- Ires- Cre_ mice (JAX no. 28863, the Jackson Laboratory) with _MitoTag_ reporter mice (JAX no. 032290, the Jackson Laboratory) over two generations. Last, to compare mice with and without _mitoLbNOX_ overexpression in the RGC of _Opa1__V291D/+_ mice, we crossbred _V291D- MitoLbNOX_ mice with _VG2- MitoTag_ mice and selected _V291D- VG2- MitoTag_ and _V291DVG2- MitoTag- MitoLbNOX_ offspring for experiments (Fig. 8A).
## Statistical analysis
Study mice were matched for sex and age between the littermatecontrolled _WT_ and mutant groups. Statistical analyses were conducted using GraphPad Prism (v10.4), SPSS Statistics (v21), and R (v4.2). Unpaired independent _t_ tests or linear regression analyses were used to compare the continuous parameters between the two groups. One- way analysis of variance (ANOVA) was used for comparisons of the continuous parameters between three groups. Continuous variables are expressed as the means ± SEM in the plots. _P_ values derived from multiple testing were corrected using the BenjaminiHochberg method. A two- tailed _P_ value of <0.05 and a _q_ value of <0.1 indicated statistical significance.
## Supplementary Materials
**The PDF file includes:** Figs. S1 to S9 tables S1 to S4 legends for supplementary excel files
**Other Supplementary Material for this manuscript includes the following:**
Supplementary excel Files
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**Acknowledgments:** We would like to express our gratitude to t. c. Swayne and the confocal and Specialized Microscopy Shared Resource at the herbert irving comprehensive cancer center, columbia University, for technical assistance. We also thank n. nolan, J. Zhao, c. P.- Y. Su, and S. chang from the department of Ophthalmology at columbia University irving Medical center for support and A. h.- F. lin and B. Y.- l. chou from Raising Statistic consultant inc. for
assistance with the statistical analyses. the salary of S.h.t. was supported by the national eye institute (nei), national institutes of health, under awards U01eY034590, R24eY028758, P30eY019007, R01eY033770, R01eY018213, and R01eY024698, and by the Richard Jaffe Foundation, the nYee Foundation, the Rosenbaum Family Foundation, and unrestricted funds from Research to Prevent Blindness (RPB). **Funding:** this work was funded by chang Gung Memorial hospital, taiwan (cMRPG3n1001 and cMRPG3Q0451) (e.Y.- c.K.); national Science and technology council, taiwan (nStc 113- 2314- B- 182A- 150- MY3) (e.Y.- c.K.); chang Gung University, taiwan (UARPd1n0031 and UARPd1P0261) (e.Y.- c.K.); national eye institute of the national institutes of health grant R01eY033359 (G.t.); national eye institute of the national institutes of health grants R01eY031354 and R21eY037007 (n.- K.W.); Gerstner Philanthropies (n.- K.W.); the United Mitochondrial disease Foundation (n.- K.W.); Genetically Modified Mouse Model Shared Resource irving comprehensive cancer center at columbia University, national institutes of health nci cancer center Support Grant P30cA013696 (c.- S.l.); national institute of General Medical Sciences of the national institutes of health grant 1S10Od030401- 01A1 (t.- d.l.) and S10Od036268 (Y. h.); national eye institute of the national institutes of health Shared instrument grant S10Od028637 and national eye institute of the national institutes of health grants U01eY034590, R24eY028758, 5P30eY019007, R01eY033770, R01eY018213, and R01eY024698 (S.h.t.); the Richard Jaffe Foundation (S.h.t.); the nYee Foundation (S.h.t.); the Rosenbaum Family Foundation (S.h.t.); and an unrestricted grant to the department of Ophthalmology, columbia University, from Research to Prevent Blindness, new York, nY. **Author contributions:** conceptualization: c.- n.t., e.Y.- c.K., c.- c.l., c.- S.l., n.- K.W., S.h.t., Y.- J.t., and O.S. Methodology: t.- d.l., i.Y.- F.c., J.P., e.Y.- c.K., c.- c.l., c.K., G.t., h.- c.h., c.- S.l., n.- K.W., S.h.t., J.c., c.- Y.h., e.h.W., and Y.- J.t. investigation: t.- d.l., c.- n.t., J.P., e.Y.- c.K., P.- h.l., c.- c.l., K.P.M., c.- l.t., c.- S.l., n.- K.W., S.h.t., J.c., l.S., W.- h.P., e.h.W., and Y.- J.t. visualization: Y.- c.t., Y.h., i.Y.- F.c., c.- c.l., K.P.M., c.- S.l., n.- K.W., J.c., e.h.W., and Y.- J.t. validation: t.- d.l., c.- n.t., i.Y.- F.c., J.P., e.Y.- c.K., c.- c.l., c.- S.l., n.- K.W., S.h.t., J.c., W.- h.P., e.h.W., and Y.- J.t. data curation: Y.- c.t., c.- n.t., i.Y.- F.c., e.Y.- c.K., c.K., c.- S.l., n.- K.W., J.c., c.- Y.h., e.h.W., and Y.- J.t. Formal analysis: Y.- c.t., i.Y.- F.c., e.Y.- c.K., c.- l.t., c.K., c.- S.l., n.- K.W., S.h.t., J.c., W.- h.P., c.- Y.h., e.h.W., e.S., and Y.- J.t. Software: Y.- c.t., i.Y.- F.c., G.t., n.- K.W., c.- Y.h., and e.h.W. Resources: t.- d.l., c.- n.t., J.P., e.Y.- c.K., G.t., h.- c.h., c.- S.l., n.- K.W., and Y.- J.t. Funding acquisition: e.Y.- c.K., G.t., c.- S.l., and n.- K.W. Project administration: e.Y.- c.K., c.- S.l., n.- K.W., and S.h.t. Supervision: c.- n.t., i.Y.- F.c., e.Y.- c.K., c.- c.l., G.t., c.- S.l., n.- K.W., S.h.t., and O.S. Writing—original draft: Y.- c.t., e.Y.- c.K., c.- c.l., c.- S.l., n.- K.W., S.h.t., J.c., and e.h.W. Writing—review and editing: t.- d.l., c.- n.t., i.Y.- F.c., e.Y.- c.K., c.- c.l., G.t., c.- S.l., n.- K.W., S.h.t., J.c., l.S., c.- Y.h., e.h.W., Y.- J.t., and O.S. **Competing interests:** the authors declare that they have no competing interests. **Data, code, and materials availability:** snRnA- seq data have been deposited into the ncBi GeO repository (GSe292269, www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSe292269). All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. this study did not generate any new materials.
Submitted 27 March 2025 Accepted 13 January 2026 Published 18 February 2026 10.1126/sciadv.adx7815
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## 目录（TOC）
> 全文较长，已按章节分片至 `parts/`（每片 ≤ 80KB，可单独 Read 不截断）。
- `N E U R O S C I E N C E` — 行 63–63（part01）
- `Disrupted energy metabolism is associated with retinal ganglion cell degeneration in autosomal dominant optic atrophy` — 行 64–70（part01）
- `INTRODUCTION` — 行 71–89（part01）
- `RESULTS` — 行 90–90（part01）
- `Clinical and genetic profile of the patient with ADOA carrying an** **_OPA1_ missense variant` — 行 91–93（part01）
- `Generation and characterization of a patient- specific knock- in ADOA mouse model (** **_Opa1_****_V291D/+_** **)` — 行 94–96（part01）
- `The visual functional phenotype of the novel** **_Opa1_****_V291D/+_** **mice recapitulated the features of ADOA` — 行 97–103（part01）
- `Reduction in RNFL thickness and RGC count in the retinas of the** **_Opa1_****_V291D/+_** **mice` — 行 104–108（part01）
- `The optic nerves of** **_Opa1_****_V291D/+_** **mice showed alterations in axonal and mitochondrial structure` — 行 109–121（part01）
- `_Opa1_****_V291D/+_** **mice showed decreased stability of the OPA1 protein` — 行 122–130（part01）
- `Reduction of mitochondrial Complex I activity (NADH/ ubiquinone oxidoreductase) in the retinas of` — 行 131–131（part01）
- `_Opa1_****_V291D/+_** **mice` — 行 132–138（part01）
- `Reduced antioxidant capacity and increased oxidative stress in the retinas of** **_Opa1_****_V291D/+_** **mice` — 行 139–141（part01）
- `Decreased NAD****+** **/NADH redox ratio and ATP levels but increased glycolysis in the retinas of** **_Opa1_****_V291D/+_** **mice` — 行 142–144（part01）
- `Decreased ATP with accumulation of adenosine monophosphate in the inner retinas, while increased glycolytic metabolites in the outer retinas of` — 行 145–149（part01）
- `Reduced glycolytic activity in the ganglion cell layer contrasted with the photoreceptor layer` — 行 150–152（part01）
- `snRNA- seq and spatial transcriptomics revealed the down- regulation of energy metabolism–related genes in the RGCs of** **_Opa1_****_V291D/+_** **mice` — 行 153–163（part01）
- `Enhanced RGC function and survival in** **_Opa1_****_V291D/+_** **mice following** **_MitoLbNOX_ overexpression` — 行 164–168（part01）
- `DISCUSSION` — 行 169–199（part01）
- `MATERIALS AND METHODS` — 行 200–200（part01）
- `Study design` — 行 201–203（part01）
- `Patients with ADOA and mouse models` — 行 204–208（part01）
- `Pattern electroretinography` — 行 209–213（part01）
- `Confocal microscopy assessment of mitochondrial morphology in mouse optic nerves` — 行 214–216（part01）
- `Flash electroretinography` — 行 217–219（part01）
- `Spectral domain–optical coherence tomography` — 行 220–222（part01）
- `RGC counting in flat- mounted retinas` — 行 223–225（part01）
- `Transmission electron microscopy` — 行 226–228（part01）
- `Immunoblotting` — 行 229–231（part01）
- `Quantitative real- time PCR for** **_Opa1_` — 行 232–234（part01）
- `Cell culture for protein stability testing` — 行 235–237（part01）
- `Coimmunoprecipitation` — 行 238–240（part01）
- `Analysis of mitochondrial respiratory and hydrolytic function in retinas` — 行 241–245（part01）
- `MALDI- TOF MS imaging` — 行 246–250（part02）
- `Hematoxylin and eosin staining` — 行 251–253（part02）
- `Immunostaining` — 行 254–256（part02）
- `ATP measurements from mouse retinas` — 行 257–259（part02）
- `NAD****+** **measurements from mouse retinas` — 行 260–264（part02）
- `GSH measurements from mouse retinas` — 行 265–267（part02）
- `SOD measurements from mouse retinas` — 行 268–270（part02）
- `Lactate measurements in mouse retinas` — 行 271–273（part02）
- `Single- nucleus RNA sequencing` — 行 274–278（part02）
- `High- resolution spatial transcriptomics of the mouse retinas` — 行 279–281（part02）
- `Generation of RGC- specific** **_MitoLbNOX_ overexpression in` — 行 282–282（part02）
- `_Opa1_****_V291D/+_** **mice` — 行 283–285（part02）
- `Statistical analysis` — 行 286–288（part02）
- `Supplementary Materials` — 行 289–295（part02）
- `REFERENCES` — 行 296–533（part02）
---
### N E U R O S C I E N C E
# Disrupted energy metabolism is associated with retinal ganglion cell degeneration in autosomal dominant optic atrophy
**Eugene Yu- Chuan Kang****1,2,3,4** **, Yun- Ju Tseng****1** **, Wei- Hao Peng****5** **, Hui- Chuan Hung****6** **, Pei- Hsuan Lin****1,7** **, Katrina P. Montales****8** **, Emmet Sherman****9** **, John Peregrin****1** **, Ethan Hunghsi Wang****1,10** **, Chunya Kang****11** **, Yu- Chuan Teng****12** **, Chen- Yang Huang****4,12,13** **, Chia- Lung Tsai****12** **, Ian Yi- Feng Chang****12,14** **, Jiazhang Chen****15** **, Gülgün Tezel****1** **, Ye He****15,16,17** **, Tai- De Li****9,18** **, Linsey Stiles****8** **, Orian Shirihai****8** **, Stephen H. Tsang****1,6** **, Chi- Chun Lai****4,19** **, Chi- Neu Tsai****3,20** ***, Chyuan- Sheng Lin****6** ***, Nan- Kai Wang****1,2,4** *****
copyright © 2026 the Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. no claim to original U.S. Government Works. distributed under a creative commons Attribution noncommercial license 4.0 (cc BY- nc).
**Autosomal dominant optic atrophy (ADOA) is a hereditary optic neuropathy caused by** **_OPA1_ variants, leading to retinal ganglion cell (RGC) degeneration and vision loss. The mechanisms behind RGC vulnerability to mitochondrial dysfunction remain unclear. We developed a patient- specific** **_Opa1_****_V291D/_**_+_ **knock- in mouse model to investigate mitochondrial dysfunction and retinal metabolism in ADOA. We observed that** **_Opa1_****_V291D/_**_+_ **mice exhibited anatomical and functional RGC abnormalities recapitulating the ADOA phenotypes. Reduced optic atrophy 1 (OPA1) protein levels were noted in** **_Opa1_****_V291D/_**_+_ **mice, accompanied by decreased protein stability. Moreover, mitochondrial function was compromised, as indicated by reduced Complex I activity, increased oxidative stress, and diminished adenosine triphosphate production in the retinas of** **_Opa1_****_V291D/_**_+_ **mice. Spatial metabolomics revealed energy deficits in the inner retina and heightened glycolysis in the outer retina. Immunostaining showed decreased expression of glycolytic proteins in the ganglion cell layer. Single- nucleus RNA sequencing disclosed significant down- regulation of energy- production genes in RGCs, while other retinal cell types remained unaffected. These findings emphasize the specific vulnerability of RGCs to bioenergetic crises, connecting disrupted energy homeostasis to their degeneration. By increasing the nicotinamide adenine dinucleotide (NAD****+** **)/reduced form of NAD****+** **(NADH) redox ratio through the overexpression of mitochondrial- targeted** **_Lactobacillus brevis_ NADH oxidase (** **_MitoLbNOX_ ) in RGCs, we demonstrated improved RGC function and survival through enhanced energy metabolism and reduced oxidative stress. These findings confirm that disrupted energy metabolism leads to RGC degeneration and emphasize the enhancement of the NAD****+** **/NADH redox ratio as a promising treatment strategy to protect RGCs from degeneration in ADOA.**
### INTRODUCTION
Autosomal dominant optic atrophy (ADOA) is the most common inherited optic neuropathy, with incidence rates ranging from 1 in 12,000 to 50,000 individuals ( _1_ , _2_ ). It is a mitochondrial eye disease primarily characterized by the degeneration of retinal ganglion cells (RGCs) ( _3_ ). This degeneration leads to progressive vision loss and is associated with variants in the nuclear DNA–encoded OPA1 mitochondrial dynamin like GTPase ( _OPA1_ ) gene, which compromise mitochondrial function ( _4_ – _6_ ). Variants in the _OPA1_ gene alter the optic atrophy 1 (OPA1) protein, a key component of the inner mitochondrial membrane responsible for mitochondrial dynamics and fusion ( _7_ ). In mammalian mitochondria, the OPA1 protein plays a vital role not only in the regulation of the fusion of the inner mitochondrial
membrane but also in the shaping of mitochondrial cristae ( _7_ , _8_ ), which are essential for the regulation of mitochondrial respiration, the stabilization of the electron transport chain (ETC), and the maintenance of oxidative stress homeostasis ( _9_ , _10_ ). Understanding the impact of the _OPA1_ variant on RGC is crucial for clarifying the pathogenic mechanisms underlying ADOA.
The impact of _OPA1_ variants on cells has been studied in previous in vitro research. Those observations revealed that HeLa cells transfected with _OPA1_ variants exhibited fragmented mitochondria and impaired oxidative phosphorylation (OXPHOS) ( _11_ , _12_ ). However, a separate study found that there was no decrease in mitochondrial adenosine triphosphate (ATP) production in ADOA human fibroblasts carrying different _OPA1_ variants ( _13_ ), indicating a disparity
> 1department of Ophthalmology, vagelos college of Physicians and Surgeons, columbia University irving Medical center, new York, nY 10032, USA. 2department of Ophthalmology, chang Gung Memorial hospital, linkou Medical center, taoyuan 333, taiwan.3 Graduate institute of clinical Medical Sciences, college of Medicine, chang Gung University, taoyuan 333, taiwan.4 School of Medicine, chang Gung University, taoyuan 333, taiwan.5 School of Medicine, national tsing hua University, hsinchu 300, taiwan. 6department of Pathology and cell Biology, herbert irving comprehensive cancer center, columbia University Medical center, new York, nY 10032, USA. 7department of Ophthalmology, national taiwan University Yunlin Branch, Yunlin 640, taiwan.8 division of endocrinology, department of Medicine, david Geffen School of Medicine, University of california, los Angeles, los Angeles, cA 90095, USA.9 nanoscience initiative at Advanced Science Research center, Graduate center of the city University of new York, new York, nY 10031, USA.10 college of Arts and Sciences, University of Miami, coral Gables, Fl 33146, USA.11 department of education, Keelung chang Gung Memorial hospital, Keelung 204, taiwan.12 Genomic Medicine core laboratory, chang Gung Memorial hospital, taoyuan 333, taiwan.13 department of Medical Oncology, chang Gung Memorial hospital, linkou Medical center, taoyuan 333, taiwan.14 Molecular Medicine Research center, chang Gung University, taoyuan 333, taiwan.15 Advanced Science Research center (ASRc), Graduate center of the city University of new York, new York, nY 10031, USA.16 Ph.d. Program in Biology, Graduate center of the city University of new York, new York, nY 10031, USA.17 department of Biology, city college of new York, city University of new York, new York, nY 10031, USA.18 department of Physics, city college of new York, city University of new York, new York, nY 10031, USA.19 department of Ophthalmology, new taipei Municipal tucheng hospital, new taipei city 23652, taiwan.20 department of Surgery, new taipei Municipal tucheng hospital, new taipei city 23652, taiwan. *corresponding author. email: pink7@ mail. cgu. edu. tw (c.- n.t.); csl5@ cumc. columbia. edu (c.- S.l.); wang. nankai@ gmail. com (n.- K.W.)
between these observations and the in vitro findings collected from non- RGC cells. Recent studies demonstrated that introducing the _Opa1__K301A_ and _Opa1__R905*_ variants into mouse RGC cultures resulted in the autophagic degradation of mitochondria and a subsequent decrease in mitochondrial activity content ( _14_ ). Despite these findings connecting the _OPA1_ variant to mitochondrial dysfunction, it is still unclear whether the degeneration of RGCs in ADOA is primarily due to a bioenergetic crisis, decreased antioxidant capacity, or a combination of both factors ( _15_ , _16_ ).
In vivo models provide advantages over in vitro models regarding the evaluation of the impact of these variants on visual function. Three _Opa1_ gene–modified ADOA mouse models have been reported. These include mice carrying a nonsense variant ( _Opa1__Q285STOP_ ) ( _17_ – _20_ ), a 4– base pair deletion causing a frameshift ( _Opa1__c.2708_2711delTTAG_ ) ( _21_ , _22_ ), and a splice- site variant ( _Opa1__c.1065+5G>A_ ) ( _23_ ). It is important to emphasize that all three mouse models express a truncated OPA1 protein. Currently, there are no reports of _Opa1_ mouse models containing missense variants, which are the most common protein- coding mutations identified in patients with ADOA, according to ClinVar and the Leiden Open Variation Database ( _24_ , _25_ ). Furthermore, no studies have examined transcriptomes at the single- cell level in the _Opa1_ mouse model to understand why RGCs are more vulnerable than other retinal cells, especially since this nuclear- encoded protein is expressed universally in all cells. In addition, while photoreceptors have the highest density of mitochondria in the retina ( _26_ ), the mitochondrial dysfunction associated with ADOA primarily affects RGCs, leaving photoreceptors largely unaffected. This disparity underscores the urgent need to investigate the effects of _Opa1_ variants on different retinal cells. Comprehensive studies are crucial to uncover the underlying disease mechanisms, identify factors contributing to RGC vulnerability, and develop targeted therapeutic interventions for RGC degeneration.
Nicotinamide has garnered attention in RGC degeneration as it is depleted in the plasma signatures of patients with ADOA and glaucoma ( _27_ – _30_ ). While nicotinamide adenine dinucleotide (NAD+ ) itself plays a crucial role in glycolysis, the tricarboxylic acid (TCA) cycle, and OXPHOS, the NAD+ /reduced form of NAD+ (NADH) redox ratio serves as a key regulator of cellular energy metabolism and an indicator of cellular stress levels ( _30_ – _32_ ). Previous studies have demonstrated that oral administration of the NAD+ precursor nicotinamide and gene therapy promoting _Nmant1_ expression, a key NAD+ - producing enzyme, halted RGC degeneration in the DBA/2J mouse model of glaucoma ( _33_ – _35_ ). Although these promising results highlight the antioxidant properties of vitamin B3 and its role in NAD+ synthesis, it remains uncertain whether similar strategies to increase NAD+ levels could enhance energy metabolism and support RGC survival in the _Opa1_ mouse model. Moreover, it is unclear whether directly converting NADH to NAD+ to boost the NAD+ /NADH redox ratio would be an effective and efficient strategy for protecting RGCs.
In this study, we developed a novel mouse model by introducing a patient- derived missense variant of the _OPA1_ gene to investigate the pathophysiology of ADOA. We evaluated whether the model accurately replicated the clinical phenotypes of ADOA, focusing on functional deficits, anatomical alterations, OPA1 protein characteristics, and mitochondrial phenotypes. To evaluate the effect of the _Opa1_ variant on mitochondrial function, we analyzed energy metabolism and oxidative stress throughout the retina. Immunostaining and spatial metabolomics were used to assess histological changes and metabolic adaptations, particularly in the ganglion cell layer where RGCs reside. We used single- nucleus RNA sequencing (snRNA- seq)
to uncover transcriptomic changes linked to ADOA at the single- cell level, aiming to identify the mechanisms contributing to the selective vulnerability of RGCs in ADOA. In addition, we examined the impact of increasing the NAD+ /NADH redox ratio in RGCs on their survival in our ADOA mouse model.
### RESULTS
## Clinical and genetic profile of the patient with ADOA carrying an** **_OPA1_ missense variant
A 32- year- old woman visited our institution with a history of gradually declining vision. The results of an eye examination performed on this patient are displayed in Fig. 1. Fundus imaging indicated temporal pallor of the optic disc (Fig. 1A). Optical coherence tomography (OCT) of the optic disc revealed a reduction in retinal nerve fiber layer (RNFL) thickness (Fig. 1B). Electrophysiological testing showed normal rod and cone responses on full- field electroretinography (ERG), albeit with reduced pattern ERG (PERG) responses (Fig. 1C). Genetic testing confirmed the presence of a heterozygous variant in the _OPA1_ gene, i.e., c.1037T>A, p.V346D (NM_130837.3), thereby confirming the diagnosis of ADOA. This variant is classified as a missense variant, which is the most common type of mutation in ADOA, according to reports in ClinVar and the Leiden Open Variation Database ( _24_ , _25_ ). This _OPA1_ missense variant was recently submitted by a reporter to the ClinVar database (ID: 447907). The _OPA1__V346D_ variant is classified as likely pathogenic, on the basis of aggregated data from public databases, following American College of Medical Genetics and Genomics guidelines (table S1).
## Generation and characterization of a patient- specific knock- in ADOA mouse model (** **_Opa1_****_V291D/+_** **)
Because of the absence of _OPA1_ mouse models carrying missense variants, we developed a patient- specific knock- in mouse model ( _Opa1__V291D/+_ ) carrying a V291D variant equivalent to the V346D variant found in our patient with ADOA (Fig. 1D). The resulting knock- in mouse harbored the _Opa1_ c.871T>A variant, which changes the 291st amino acid of OPA1 from valine to aspartic acid. Mice that were homozygous for this variant exhibited embryonic lethality, consistent with observations in other ADOA mouse models. We verified the presence of the V291D variant through the polymerase chain reaction (PCR) amplification of exon 9 using forward and reverse primers (table S2), which confirmed variant heterozygosity in the mutant mice (Fig. 1E); this was further validated using Sanger sequencing (Fig. 1F). The mutant mice had lower body weights than their _WT_ littermate controls after 180 days (Fig. 1G). Moreover, the mutant mice exhibited a hunched- back posture (fig. S1), which was suggestive of an illness or aging condition associated with the specific variant.
## The visual functional phenotype of the novel** **_Opa1_****_V291D/+_** **mice recapitulated the features of ADOA
To analyze the visual functional presentation of the _Opa1__V291D/+_ mouse model, we performed several electrophysiological tests, including PERG, photopic negative responses (PhNRs), scotopic threshold response (STR), and serial- intensity scotopic and photopic flash ERGs. At 180 days, the PERG revealed a significant decrease in amplitude between P1 and N2, which continued to decline up to 630 days, indicating the presence of a degenerative process in this mouse model (Fig. 2A). _Opa1__V291D/+_ mice exhibited a significantly reduced PhNR amplitude at
**Fig. 1. Optic atrophy and visual function impairment in a patient with ADOA and the generation of an** **_Opa1_****_V291D/+_** **mouse model.** ( **A** ) Fundus photography showing temporal disc pallor in the left eye, representative of both eyes. ( **B** ) Oct demonstrating decreased RnFl thickness, averaging 69.8 μm in the left eye. ( **C** ) Full- field eRG indicating normal rod and cone responses, with decreased PeRG responses. ( **D** ) targeting strategy used for generating the _Opa1__V291D/+_ knock- in mouse, with primers (F1 and R1) designed to detect exon 9 of _Opa1_ . ( **E** ) Genotyping results for _Opa1_+/+ and _Opa1__V291D/+_ tissues using the indicated primers. the knock- in allele includes an additional 83 nucleotides compared with the _wildtype_ ( _WT_ ) allele, incorporating the _LoxP_ site and adjacent sequences. ( **F** ) Sequencing of the region between the indicated primers confirming the heterozygous t- to- A variant. ( **G** ) Body weight measurements of mice at different ages (total _n_ = 306; independent _t_ tests _P_ = 0.8096, 0.3582, 0.2501, 0.0191, 0.0011, <0.0001, and < 0.0001 at P30, P90, P120, P180, P270, P360, and P450, respectively). data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, **** _P_ < 0.0001. n.s., not significant; ex, exon; neoR, neomycin resistance.
180 days (Fig. 2B). In terms of STR, a significant reduction in the negative STR was observed at all three intensities (−5.6, −5.3, and −5.0 log cd·s/m2 ) at 180 days (Fig. 2C). The scotopic and photopic ERGs did not display significant differences in both a- and b- wave amplitudes across all intensities between the _Opa1__V291D/+_ and their littermate- control _WT_ mice at 360 days (Fig. 2D). Our _Opa1__V291D/+_ mice exhibited abnormal results in electrophysiological tests specific to RGC, whereas the function of photoreceptors remained unaffected. These findings were consistent with those observed in human patients with ADOA.
## Reduction in RNFL thickness and RGC count in the retinas of the** **_Opa1_****_V291D/+_** **mice
In addition to assessing their functional phenotype, we used in vivo spectral domain (SD)–OCT and immunostaining to elucidate the anatomical phenotype in the retinas of the _Opa1__V291D/+_ mice. The SD- OCT examination revealed a decreased RNFL thickness in _Opa1__V291D/+_ mice compared with their littermate _WT_ controls. This was observed in both female and male mice at 90 days (Fig. 3A). The number of RGCs was examined by immunostaining of wholemounted retinas using an anti- BRN3A antibody, as shown in Fig. 3B. This analysis revealed a reduction in RGC numbers in _Opa1__V291D/+_ mice compared with their littermate _WT_ controls, which was correlated with the decrease in RNFL thickness. Notably, the RGC counts
in mutant mice were significantly decreased after 180 days and continued to decline up to 420 days.
## The optic nerves of** **_Opa1_****_V291D/+_** **mice showed alterations in axonal and mitochondrial structure
To examine in greater detail the anatomical features of the myelinated sheath and mitochondrial morphology in the optic nerve, which contains the axons of RGCs, super- resolution imaging, including spinning disk confocal microscopy (SDCM) with super- resolution radial fluctuations (SRRFs) and transmission electron microscopy (TEM), was applied to mouse optic nerves. Our SDCM with SRRF imaging analysis detected the presence of altered mitochondrial shapes in _Opa1__V291D/+_ mice at 360 days, which exhibited a greater number of spherical and less variable mitochondria compared with control mice (Fig. 3C), indicating the presence of mitochondrial fragmentation caused by impaired mitochondrial fusion. To further delineate regional differences in mitochondrial dynamics, we performed additional imaging to assess mitochondria across the prelaminar region, the unmyelinated optic nerve head, and the myelinated optic nerve ( _36_ ). Increased mitochondrial sphericity was consistently observed in mutant mice across all three regions (fig. S2A). TEM analysis revealed a loosened myelinated sheath and a significantly reduced number of myelinated axons in mutant mice at ages 50 and 360 days (Fig. 3D), reflecting chronic RGC
**Fig. 2.** **_Opa1_****_V291D/+_** **variant in mice recapitulates the RGC- specific visual function deficits of patients with ADOA.** ( **A** ) Representative PeRG recordings showing the amplitude measured from n2 to P1 (total _n_ = 161; independent _t_ tests _P_ = 0.8478, 0.0097, 0.0021, <0.0001, 0.0265, and 0.0462 at P90, P180, P270, P460, P450, and P630, respectively). ( **B** ) Representative PhnR recordings showing the amplitude measured from the baseline to the trough (total _n_ = 15; independent _t_ tests _P_ = 0.0026). ( **C** ) Representative StR recordings showing the amplitude measured from the baseline to the positive StR (pStR) and negative StR (nStR) (total _n_ = 51; independent _t_ tests _P_ = 0.0021, <0.0001, and 0.0006 at nStR –5.6, −5.3, and −5.0 log cd·s/m2 , respectively; _P_ = 0.6499, 0.9336, and 0.9042 at pStR –5.6, −5.3, and −5.0 log cd·s/m2 , respectively). ( **D** ) Representative serial scotopic and photopic eRG recordings at different intensities at 360 days (total _n_ = 9; linear regression model _P_ for interaction = 0.420, 0.887, 0.201, and 0.117 in scotopic a- wave, photopic a- wave, scotopic b- wave, and photopic b- wave, respectively). data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, *** _P_ < 0.001, **** _P_ < 0.0001.
degeneration ( _37_ ). Moreover, TEM imaging of the optic nerve revealed changes in mitochondrial morphology in the _Opa1__V291D/+_ mice, including the separation of the inner mitochondrial membranes, the loss of cristae, and mitochondrial vacuolation (Fig. 3E). Examination of RGC somata in the ganglion cell layer also revealed disrupted mitochondria and the accumulation of mitophagosomes (fig. S2B). To determine whether mitochondrial genomic alterations accompanied these structural abnormalities, we analyzed mitochondrial DNA (mtDNA) copy number and integrity in retinal tissues. Quantitative PCR (qPCR) revealed a significant increase in mtDNA copy
number in _Opa1__V291D/+_ retinas compared with _WT_ controls, possibly reflecting impaired fusion, accumulation of mitophagosome, and compensatory mitochondrial turnover (fig. S3A). In contrast, qPCR- based mtDNA damage assays and long- extension PCR showed no detectable differences in mtDNA deletions or damage between _Opa1__V291D/+_ and _WT_ mice (fig. S3B). These results indicate that, although mtDNA copy number is elevated, the overall integrity of the mitochondrial genome remains intact, suggesting that the observed mitochondrial defects are primarily functional and structural rather than due to mtDNA instability. Together, these findings indicate that
**Fig. 3. The** **_Opa1_****_V291D/+_** **variant leads to RGC loss and mitochondrial ultrastructure alterations in the retina and optic nerve.** ( **A** ) Sd- Oct at 90 days (total _n_ = 32; independent _t_ tests _P_ = 0.0360, 0.0197, and 0.0228 in male, female, and total groups, respectively). ( **B** ) Representative images showing BRn3A- positive RGc counts in 20 squares from three different zones of a whole- mounted retina. Analysis of the RGc counts per 20 squares at 180, 360, and 420 days ( _n_ = 4, 6, and 3 mice per group at P180, P360, and P420, respectively; independent _t_ tests _P_ = 0.0105, 0.0031, and 0.0028 at P180, P360, and P420, respectively). ( **C** ) Representative image showing confocal microscopy with super- resolution imaging of the optic nerves. violin plot of the mitochondrial sphericity in the optic nerves at 360 days ( _n_ = 4 mice in each group; independent _t_ tests _P_ = 0.0356). ( **D** ) Representative images showing optic nerve ultrastructure in teM. Analysis of the number of myelinated axons in optic nerves at 50 days ( _n_ = 3 mice in each group; independent _t_ tests _P_ = 0.0009) and 360 days ( _n_ = 4 mice in each group; independent _t_ tests _P_ < 0.0001). ( **E** ) Separation of the inner mitochondrial membranes, loss of cristae, and mitochondrial vacuolation were also observed in _Opa1__V291D/+_ mice. data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, *** _P_ < 0.001, **** _P_ < 0.0001.
_Opa1__V291D/+_ mice exhibit reduced RGC numbers in the retina, thinner RNFL on SD- OCT, fewer myelinated axons in the optic nerve, and mitochondrial structural abnormalities accompanied by increased mtDNA copy number but preserved mtDNA integrity. These combined changes demonstrate RGC degeneration associated with impaired mitochondrial fusion and respiratory dysfunction.
## _Opa1_****_V291D/+_** **mice showed decreased stability of the OPA1 protein
To investigate whether the V291D variant affects the expression of the OPA1 protein in the retina, we performed Western blot analyses, which revealed a significantly reduced level of the OPA1 protein in the retinas of _Opa1__V291D/+_ mice (Fig. 4A). In contrast, qPCR analyses
**Fig. 4. Decreased OPA1 protein levels in the** **_Opa1_****_V291D/+_** **mouse retinas and reduced OPA1 protein stability in** **_Opa1_****_V291D_** **_-_ transfected cells.** ( **A** ) Western blot (WB) showing the OPA1 protein expression in retinas ( _n_ = 6 in each group, independent _t_ tests _P_ < 0.0001). ( **B** ) qPcR of the _Opa1_ mRnA expression in retinas ( _n_ = 6 in each group, independent _t_ tests _P_ = 0.7744). ( **C** ) lysates from heK293 cells transfected with _Opa1__WT_ and _Opa1__V291D_ were treated with MG132. immunoprecipitation (iP) revealed the presence of polyubiquitinated OPA1 in the _Opa1__V291D_ - transfected cells. ( **D** ) levels of the OPA1 protein after treatment with MG132 (25 μM) at baseline, 4 hours, and 6 hours in the _Opa1__V291D_ - transfected heK293 cells ( _n_ = 3 in each group; one- way analysis of variance (AnOvA) with tukey’s test _P_ = 0.9431 and 0.0241 in 0 versus 4 hours and 0 versus 6 hours). data are presented as means ± SeM. * _P_ < 0.05, **** _P_ < 0.0001.
revealed no differences in _Opa1_ mRNA expression in retinal cells between the mutant and littermate- control _WT_ mice, indicating that the down- regulation of OPA1 protein levels was not due to a decrease in the transcription of the corresponding gene (Fig. 4B). Because reduced protein levels are often linked to protein destabilization and degradation via the ubiquitin- proteasome system ( _38_ ), we further examined OPA1 protein expression and ubiquitination in _Opa1__WT_ _-_ and _Opa1__V291D_ _-_ transfected human embryonic kidney (HEK) 293 cells. In the presence of _N_ - carbobenzyloxy- l- leucyl- l- leucyl- l- leucinal (MG132) (25 μM), which is a proteasome inhibitor, we observed polyubiquitinated OPA1 substrates in the lysates of _Opa1__V291D_ - transfected cells (Fig. 4C), suggesting that increased degradation through the ubiquitin- proteasome pathway contributed to the down- regulation of the OPA1 protein. Moreover, treating HEK293 cells with MG132 (25 μM) for 4 and 6 hours resulted in a mild restoration of OPA1 accumulation after 6 hours, especially the short form of the protein, in _Opa1__V291D_ _-_ transfected cells (Fig. 4D), indicating that the ubiquitin- proteasome pathway contributes to, but does not fully account for, OPA1 depletion. To further assess the impact of the V291D variant on OPA1 isoform processing, we analyzed the ratio of long OPA1 (l- OPA1) and short OPA1 (s- OPA1) in retinal lysates. Both isoforms were significantly reduced in _Opa1__V291D/+_ mice compared with _WT_ controls, with a disproportionately greater decrease in the short (soluble) form (fig. S4). This pattern suggested that the V291D variant caused reduced overall OPA1 protein stability
and impaired proteolytic processing, leading to selective depletion of s- OPA1. Because s- OPA1 acts together with l- OPA1 in crista remodeling, mitochondrial fusion, and restoration of energy efficiency ( _39_ ), its preferential loss likely aggravates crista disorganization and compromises OXPHOS efficiency. Together, these results demonstrate that the _Opa1__V291D_ variant leads to decreased protein stability, enhanced proteasomal degradation, and impaired isoform processing, resulting in reduced OPA1 function. This combination of effects provides a mechanistic link between the mutation, disrupted mitochondrial structure, and the OXPHOS dysfunction underlying RGC degeneration in ADOA.
## Reduction of mitochondrial Complex I activity (NADH/ ubiquinone oxidoreductase) in the retinas of
## _Opa1_****_V291D/+_** **mice
On the basis of our examination of the changes in the shape and structure of mitochondria in _Opa1__V291D/+_ mice, we investigated how these alterations affect mitochondrial function in the retinas of these mice. Specifically, we performed tests to measure mitochondrial respiration and ATP hydrolysis in the retinas using frozen tissue samples [referred to as the respirometry in frozen sample (RIFS) and hydrolysis in frozen sample (HyFS) assays, respectively (Fig. 5A) ( _40_ – _42_ ). The results of these assays revealed a significant decrease in the activity of Complex I in terms of both the protein- normalized and the MitoTracker Deep Red (MTDR)–normalized oxygen consumption rates in _Opa1__V291D/+_
**Fig. 5. Mitochondrial dysfunction, oxidative stress, reduced energy production, and glycolytic shift in** **_Opa1_****_V291D/+_** **mouse retinas.** ( **A** ) Representative bioenergetic profile, as determined using the RiFS protocol in frozen retinas. ( **B** ) Optimized RiFS analysis of mitochondrial complex i, ii, and iv activities normalized to total protein and mitochondrial content (MtdR; _n_ = 6 per group). ( **C** ) Ratios of complex i/iv, ii/iv, and i/ii activities. Optimized RiFS results normalized to total protein and mitochondrial content using MtdR ( _n_ = 6 per group). ( **D** ) AtP hydrolytic capacity assessed by hyFS ( _n_ = 6 per group) ( **E** ) the GSh/GSSG ratio and total GSh level in retinal lysate ( _n_ = 7 per group. ( **F** ) the SOd activity in mouse retinas ( _n_ = 7 per group). ( **G** ) Representative immunostaining of 4- hne in retinal sections showing increased fluorescence intensity in the _Opa1__V291D/+_ mouse retina, particularly in the ganglion cell layer. Bar chart of the 4- hne fluorescence intensity in retinal immunostaining ( _n_ = 5 per group). ( **H** ) the nAd+ /nAdh ratio, the quantity (picomol) of nAd+ per amount (milligram), and the quantity (picomol) of nAdh per amount (milligram) of protein in mouse retinas ( _n_ = 6 per group). ( **I** ) the quantity (nmol) of AtP per amount (milligram) of protein in mouse retinas ( _n_ = 6 per group). ( **J** ) the level of lactate per amount (milligram) of protein in mouse retinas ( _n_ = 5 per group). ( **K** ) Western blot of the phospho- PFKFB3, phospho- GlUt1, hK1, and hK2 in mouse retinas lysates with quantification ( _n_ = 6 to 8 per group). data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, *** _P_ < 0.001. AA, antimycin A; Rot, rotenone; Asc, ascorbate.
mice (Fig. 5B). This normalization helped account for potential variations in mitochondrial content between samples, thereby ensuring that the observed differences reflect true functional deficits rather than changes in mitochondrial abundance. In addition to Complex I dysfunction, we also observed a decrease in Complex IV activity, as evidenced by the increased Complex II/IV activity ratio without significant change in Complex II activity in _Opa1__V291D/+_ mice (Fig. 5C). This suggests that both Complex I and IV activities are diminished in _Opa1__V291D/+_ retinas, consistent with the role of OPA1 in maintaining mitochondrial crista integrity, which is essential for the stability and function of respiratory complexes. The altered ratio further indicates a compensatory adjustment in the respiratory chain to preserve energy production despite dual impairment. In the HyFS assay, a trend toward reduced protein- normalized ATP hydrolytic capacity was observed in _Opa1__V291D/+_ mice (Fig. 5D), although this result did not reach statistical significance. These findings underscore the presence of ETC defects with Complex I dysfunction in the retina of _Opa1__V291D/+_ mice.
## Reduced antioxidant capacity and increased oxidative stress in the retinas of** **_Opa1_****_V291D/+_** **mice
Because of the potential for the induction of oxidative stress by Complex I impairment ( _43_ ), we also examined the antioxidant and oxidative stress profiles in mouse retinas. The glutathione (GSH) levels and the ratio of GSH to its oxidized form (GSSG) were significantly lower in _Opa1__V291D/+_ retinas (Fig. 5E), indicating a disrupted redox balance and heightened oxidative stress. In addition, superoxide dismutase (SOD) activity was significantly lower in the _Opa1__V291D/+_ retinas (Fig. 5F), suggesting a diminished antioxidant capacity that may worsen oxidative stress by facilitating the accumulation of superoxide radicals. Immunostaining of retinal tissues for 4- hydroxynonenal (4- HNE), which is a crucial marker of oxidative stress ( _44_ ), showed significantly elevated 4- HNE levels in _Opa1__V291D/+_ mice, particularly in the inner retina (Fig. 5G), further confirming the accumulation of oxidative stress. These findings link oxidative stress with the defective ETC and impaired Complex I activity observed in _Opa1__V291D/+_ retinas.
## Decreased NAD****+** **/NADH redox ratio and ATP levels but increased glycolysis in the retinas of** **_Opa1_****_V291D/+_** **mice
Given the defects in ETC observed in _Opa1__V291D/+_ mouse retinas, we next examined retinal energy metabolism by measuring NAD+ and its reduced form (NADH) and ATP levels using assay kits. The results indicated that the NAD+ /NADH redox ratio and NAD+ levels were reduced in _Opa1__V291D/+_ mouse retinas, whereas NADH levels remained comparable between the mutant and control groups (Fig. 5H), which was consistent with the impairment in Complex I activity noted in _Opa1__V291D/+_ mice. The ATP levels were significantly decreased in _Opa1__V291D/+_ mouse retinas (Fig. 5I), thus corroborating ETC dysfunction and the resulting bioenergetic crisis within the retina. Since glycolysis serves as an alternative energy source when OXPHOS is impaired in the retina ( _45_ , _46_ ), we further assessed lactate levels in mouse retinas. Lactate assays revealed increased lactate production in _Opa1__V291D/+_ retinas (Fig. 5J), indicating an adaptive metabolic response. In addition, immunoblot analysis demonstrated significant up- regulation of phospho–6- phosphofructo- 2- kinase/fructose- 2,6bisphosphatase 3 (PFKFB3), phospho–glucose transporter 1 (GLUT1), hexokinase 1 (HK1), and HK2 proteins in _Opa1__V291D/+_ retinas (Fig. 5K), suggesting a metabolic shift toward glycolysis to compensate for impaired ETC function.
## Decreased ATP with accumulation of adenosine monophosphate in the inner retinas, while increased glycolytic metabolites in the outer retinas of
**_Opa1_****_V291D/+_** **mice**
To further characterize metabolic alterations in _Opa1__V291D/+_ mouse retinas, we conducted matrix- assisted laser desorption/ionization time- of- flight (MALDI- TOF) mass spectrometry (MS) analysis, which revealed substantial ATP depletion and adenosine monophosphate (AMP) accumulation, particularly in the inner retinal layers where RGCs reside, indicating a severe energy crisis in these regions (Fig. 6A). In contrast, MALDI results demonstrated significantly elevated signal intensities of glycolysis metabolites, including glucose- 6- phosphate (G6P) and pyruvate, predominantly in the outer retinal layers, where photoreceptors are located (Fig. 6B). These findings suggest a metabolic shift toward glycolysis as a compensatory mechanism in response to energy deficits in _Opa1__V291D/+_ retinas, particularly in the photoreceptor- rich outer retina, while the inner retinal layers, including RGCs, do not exhibit this change.
## Reduced glycolytic activity in the ganglion cell layer contrasted with the photoreceptor layer
To assess cellular responses to the bioenergetic crisis at the histological level, we analyzed phospho–AMP- activated protein kinase α (AMPKα) expression using immunostaining. We observed increased phosphoAMPKα fluorescence intensity in both the ganglion cell and photoreceptor layers of _Opa1__V291D/+_ retinas, indicating AMPK pathway activation under metabolic stress (Fig. 6C). Previous studies have demonstrated distinct preferences regarding the energy metabolism between the retinal layers, with outer retinal layers relying on glycolysis to compensate for ATP deficiencies ( _45_ – _48_ ), whereas inner retinal cells, including RGCs, primarily depend on mitochondrial ETC and OXPHOS, exhibiting lower glycolytic activity ( _45_ , _46_ ). Given these differences and building on the results of our immunoblot analysis, which indicate a glycolytic shift in retinal metabolism in response to a bioenergetic crisis, we further investigated the expression of glycolytic enzymes at the histological level to evaluate metabolic changes across different retinal layers. Immunostaining revealed a significantly reduced fluorescence intensity for phospho- PFKFB3, HK1, lactate dehydrogenase B (LDHB), and isocitrate dehydrogenase 3 (IDH3) in the ganglion cell layer of _Opa1__V291D/+_ mice. In contrast, the fluorescence intensities of phospho- PFKFB3, phospho- GLUT1, and HK1 were significantly elevated in the photoreceptor inner and outer segment layers (Fig. 6C). These findings suggest that, in response to the bioenergetic crisis caused by defective ETC, the compensatory energy metabolism via glycolysis and TCA cycle was impaired in the ganglion cell layer, where RGCs reside. This disruption of energy homeostasis in the ganglion cell layer may suggest the selective vulnerability of RGCs in _Opa1__V291D/+_ mice.
## snRNA- seq and spatial transcriptomics revealed the down- regulation of energy metabolism–related genes in the RGCs of** **_Opa1_****_V291D/+_** **mice
To understand further the molecular mechanisms underlying ADOA at the single- cell resolution, we used snRNA- seq to analyze transcriptomic changes in RGCs and other retinal cell types between _Opa1__V291D/+_ and _WT_ mouse retinas at 360 days. In a total of 19,315 nuclei, the snRNA- seq and unsupervised clustering analysis identified 10 clusters corresponding to nine retinal cell types, as assessed on the basis of the expression of specific cell markers (table S3), together with an additional cluster comprising other cells, as shown in Fig. 7A. Two
**Fig. 6. Reduced energy production and metabolic shift toward glycolysis in** **_Opa1_****_V291D/+_** **mouse retinas, with decreased glycolytic activity in the ganglion cell layer.** ( **A** ) Representative hematoxylin and eosin (h&e)–stained retinal sections, corresponding MAldi MS images, and manual image segmentation from _WT_ and _Opa1__V291D/+_ mice at 180 days. Bar charts of AtP signal intensity in positive ion mode ( _n_ = 3 per group; independent _t_ test _P_ = 0.0423, 0.0496, and 0.0649 in whole retina, inner retinal layer, and outer retinal layer) and AMP signal intensity in negative ion mode ( _P_ = 0.0451, 0.0280, and 0.0697). ( **B** ) Representative MAldi MS images of G6P and pyruvate in _WT_ and _Opa1__V291D/+_ mouse retinas at 180 days. Bar chart of G6P signal intensities in negative ion mode ( _P_ = 0.0188, 0.0744, and 0.0260 in whole retina, inner retinal layer, and outer retinal layer) and pyruvate signal intensities in negative ion mode ( _P_ = 0.0335, 0.0502, and 0.0367). ( **C** ) Representative immunostaining of phosphoAMPKα, phospho- PFKFB3, phospho- GlUt1, hK1, ldhB, and idh3 in retinal sections from _WT_ and _Opa1__V291D/+_ mice. Bar charts of the fluorescence intensity of phosphoAMPKα ( _n_ = 5 per group; independent _t_ test _P_ = 0.0013 and 0.0396 in ganglion cell layer and photoreceptor layer, respectively), phospho- PFKFB3 ( _P_ = 0.0454 and 0.0029), phospho- GlUt1 ( _P_ = 0.0595 and 0.0029), hK1 ( _P_ = 0.0018 and 0.0394), ldhB ( _P_ = 0.0038 and 0.8227), and idh3 ( _P_ = 0.0006 and 0.3595) in mouse retinas. data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, *** _P_ < 0.001.
**Fig. 7. Down- regulation of genes involved in the ETC, complex I biogenesis, and glycolysis in RGCs of** **_Opa1_****_V291D/+_** **mice.** ( **A** ) A cluster analysis of the results from snRnA- seq of retinal cells from _WT_ and _Opa1__V291D/+_ mice at 360 days identified 10 retinal cell types, including two distinct RGc clusters, RGc- 1 and RGc- 2, via unsupervised clustering. ( **B** ) A heatmap of the RGc markers in the RGc- 1 and RGc- 2 clusters in _WT_ and _Opa1__V291D/+_ mice. Both clusters expressed pan- RGc markers, with no statistically significant differences in _Pou4_ markers between the clusters. ( **C** ) heatmaps of pathway analyses highlighting multiple down- regulated genes in RGc- 2 from _Opa1__V291D/+_ mice compared with _WT_ controls, particularly in pathways related to etc ( _n_ = 5 mice per group; adjusted _P_ < 0.0001, _q_ < 0.0001, WikiPathways database), complex i biogenesis (adjusted _P_ < 0.0001, _q_ < 0.0001, ReActOMe database), and glycolysis (adjusted _P_ = 0.0081, _q_ = 0.0064, ReActOMe database). ( **D** ) A dot plot illustrating differential gene expression in the etc and glycolysis pathways across various retinal cell types. RGc- 2 displayed more significant differences in gene expression between _Opa1__V291D/+_ and _WT_ mice compared with other retinal cell clusters. ( **E** ) Representative image of high- resolution spatial transcriptomics analyzed using QuPath cell segmentation. cells from the ganglion cell layer (Gcl) were selected and clustering distinguished Gcl- derived cell populations in _WT_ and _Opa1__V291D/+_ retinas at 280 days. ( **F** ) heatmaps from spatial transcriptomic pathway analysis showing decreased expression of etc genes (adjusted _P_ = 0.0079, _q_ = 0.1215; WikiPathways database) and glycolysis genes (adjusted _P_ < 0.0001, _q_ < 0.0001; WikiPathways database) in RGc- rich regions of _Opa1__V291D/+_ retinas.
distinct RGC clusters, RGC- 1 and RGC- 2, were identified. A comparative analysis revealed that both clusters expressed pan- RGC markers, including _Rbpms_ , _Slc17a6_ , and _Thy1_ ( _49_ , _50_ ). There were no statistically significant differences between RGC- 1 and RGC- 2 in the expression of _Pou4_ genes, despite minor variations in relative expression levels observed in the plots (Fig. 7B). The differential gene expression analysis of these 10 clusters showed significant down- regulation of genes linked to the ETC, Complex I biogenesis, and glycolysis, particularly in the RGC- 2 cluster of _Opa1__V291D/+_ mice compared with the littermate _WT_ mice (Fig. 7C). In contrast, no significant changes in the expression of genes associated with energy- production pathways were detected in other retinal cell types, such as rods and cones, between _Opa1__V291D/+_ and _WT_ mice. A dot plot analysis displayed the log expression and percent expression of genes related to ETC and glycolysis across different cell types in _Opa1__V291D/+_ and _WT_ mice (Fig. 7D). It revealed that RGC- 2 had high energy demands in _WT_ mice and showed more pronounced differences between _Opa1__V291D/+_ and _WT_ compared with the other cell clusters. These snRNA- seq findings aligned with the results of the spatial metabolomics and immunostaining reported above, which indicated energy depletion and impaired
glycolysis predominantly in the ganglion cell layer. In addition, the snRNA- seq revealed decreased expression of genes related to mitophagy and autophagy pathways specifically in the RGC- 2 cluster (fig. S5A), whereas these pathways were preserved in photoreceptors and other retinal cells. To spatially validate these observations, we performed high- resolution spatial transcriptomics on mouse retinal sections. Consistent with the snRNA- seq data, spatial transcriptomic analysis revealed markedly reduced expression of ETC- and glycolysis- related genes in cells within the ganglion cell layer of _Opa1__V291D/+_ retinas compared with _WT_ (Fig. 7, E and F). Spatial transcriptomics also confirmed decreased expression of autophagy- related genes specifically in RGCrich regions, whereas mitophagy- related transcripts showed a downward trend but did not reach statistical significance (fig. S5B). These pathway- specific deficits were not observed in photoreceptors. Together, these integrated transcriptomic datasets demonstrate that RGCs exhibit coordinated down- regulation of ETC, glycolysis, and mitochondrial turnover pathways. This cell type–specific impairment in metabolic and mitochondrial quality- control responses likely contributes to the selective vulnerability of RGCs in _Opa1__V291D/+_ mice and underlies their progressive degeneration in ADOA.
## Enhanced RGC function and survival in** **_Opa1_****_V291D/+_** **mice following** **_MitoLbNOX_ overexpression
Considering that our _Opa1__V291D/+_ mouse model displayed Complex I dysfunction, a reduced NAD+ /NADH redox ratio, elevated oxidative stress, and decreased ATP production, we investigated whether increasing the NAD+ /NADH redox ratio could promote RGC survival in our _Opa1__V291D/+_ mice. To increase the NAD+ /NADH ratio, our approach was to use _Lactobacillus brevis_ ( _Lb_ ) NOX ( _51_ ), a bacterial water- forming NADH oxidase, to directly increase NAD+ by oxidization of NADH to NAD+ . Both _LbNOX_ and _MitoLbNOX_ , the latter containing the mitochondrial targeting sequence, have been shown to lower cytosolic NADH levels in HeLa cells, as demonstrated by the SoNar sensor and lactate/pyruvate ratios ( _51_ ). However, _MitoLbNOX_ not only had a more significant effect on the mitochondrial NAD+ /NADH ratio but also doubled the total cellular NAD+ /NADH ratio, while _LbNOX_ does not significantly affect the total cellular NAD+ /NADH ratio because most of the NADH within the cell is located in the mitochondria, and _MitoLbNOX_ directly acts in this compartment ( _51_ , _52_ ). Therefore, we used _MitoLbNOX_ to effectively boost the NAD+ /NADH redox ratio in RGC mitochondria. We generated _Opa1__V291D/+_ _; Rosa26__LSL- MitoLbNOX/+_ (hereafter, _V291D- MitoLbNOX_ ) mice that could conditionally overexpress _MitoLbNOX_ when crossed with an RGC- specific _Cre_ reporter line ( _Vglut2__Cre_ _;Rosa26__LSL- MitoTag_ ; hereafter _VG2- MitoTag_ ) (Fig. 8A). To verify the specificity of Cre- loxP–mediated conditional overexpression, we examined green fluorescent protein (GFP) expression within the _MitoTag_ cassette in _VG2- MitoTag_ mice, confirming localized GFP expression in RGCs (Fig. 8B). GFP expression remained stable in both _Opa1__V291D/+_ _;Vglut2__Cre/+_ _;Rosa26__LSL- MitoTag/LSL- MitoLbNOX_ ( _V291D- VG2MitoTag- MitoLbNOX_ ) and _Opa1__V291D/+_ _;Vglut2__Cre/+_ _;Rosa26__LSL- MitoTag/+_ ( _V291D- VG2- MitoTag_ ) mouse retinas (Fig. 8B). Next, we analyzed the functional outcomes and survival of RGCs from the _V291D- VG2MitoTag- MitoLbNOX_ and _V291D- VG2- MitoTag_ mice. PERG recordings at 180 days demonstrated significantly larger amplitudes in the _V291D- VG2- MitoTag- MitoLbNOX_ mice compared with their littermate control _V291D- VG2- MitoTag_ mice, indicating improved RGC function (Fig. 8C). In addition, whole- mounted retina immunostaining revealed a greater count of RGCs per peripheral square in _V291D- VG2MitoTag- MitoLbNOX_ mice (Fig. 8D), suggesting that _MitoLbNOX_ overexpression enhances RGC survival. On the basis of prior studies, boosting the NAD+ /NADH redox ratio through the overexpression of _MitoLbNOX_ could improve energy metabolism via the TCA cycle while also playing a crucial role in oxidative stress regulation ( _31_ , _53_ ). Thus, we evaluated TCA cycle activity and oxidative stress levels at the histological level. Immunostaining of the retinal section revealed elevated pyruvate dehydrogenase E1 component (PDHE1) and IDH3 expression in the ganglion cell layer of _V291D- VG2- MitoTag- MitoLbNOX_ mice, indicating heightened TCA cycle activity. In addition, the fluorescence intensity of 4- HNE in the ganglion cell layer significantly decreased in _V291D- VG2- MitoTag- MitoLbNOX_ mice, indicating a reduction in oxidative stress following _MitoLbNOX_ overexpression (Fig. 8E). To further explore whether the integrated stress response (ISR) contributes to the pathological phenotype, we performed additional immunofluorescence staining for eukaryotic translation initiation factor 2A (eIF2α), phosphorylated eIF2α (p- eIF2α), and activating transcription factor 4 (ATF4). No significant differences in either marker were detected among _WT_ , _V291D- VG2- MitoTag_ , and _V291D- VG2MitoTag- MitoLbNOX_ retinas, suggesting that the canonical ISR pathway is not prominently activated under these conditions. In contrast, nuclear factor erythroid 2- related factor 2 (NRF2) expression was markedly
reduced in the ganglion cell layer of _V291D- VG2- MitoTag_ retinas and restored to near- normal levels following _MitoLbNOX_ overexpression (fig. S6). This NRF2 restoration aligns with the 4- HNE findings and indicates that _MitoLbNOX_ mitigates oxidative stress by normalizing redox signaling rather than suppressing the ISR. To further assess the metabolic impact of _MitoLbNOX_ overexpression, we performed MALDI analysis on _V291D- VG2- MitoTag_ and _V291D- VG2- MitoTagMitoLbNOX_ retinas (fig. S7). These analyses revealed a trend toward increased ATP abundance in the inner retinal layer of _V291D- VG2MitoTag- MitoLbNOX_ mice, consistent with improved mitochondrial energy output. Together, these findings demonstrate that _MitoLbNOX_ overexpression restores mitochondrial redox balance, enhances metabolic capacity, reduces oxidative stress, and ultimately protects RGCs from degeneration in _Opa1__V291D/+_ mice.
### DISCUSSION
In this study, we developed a novel patient- specific _Opa1__V291D/+_ knock- in mouse model to replicate the most common type of mutation, the missense mutation, found in human patients with ADOA. This model accurately recapitulated the anatomical and functional phenotypes of ADOA, reflecting those observed in patients. Our findings showed that the V291D variant affected mitochondrial structure, disrupted OXPHOS complexes and redox state, and increased oxidative stress in _Opa1__V291D/+_ mice. Furthermore, our study revealed that the RGCs in the _Opa1__V291D/+_ mouse model did not shift their energy metabolism to glycolysis, unlike other retinal cells, which adapted to compensate for the bioenergetic crisis caused by the defective ETC function. These findings provide a potential explanation for the selective vulnerability of RGCs observed in ADOA. To explore potential therapeutic strategies, we overexpressed _MitoLbNOX_ in RGC mitochondria and observed enhanced TCA cycle activity, reduced oxidative stress, and restored RGC function and survival in _Opa1__V291D/+_ mice. These findings highlight the critical role of bioenergetic crisis and oxidative stress in RGC degeneration and suggest that targeting NAD+ /NADH homeostasis with _MitoLbNOX_ overexpression could serve as a promising therapeutic strategy for ADOA.
The genetics of _OPA1_ - related ADOA are more complex and diverse than initially recognized. Many of these variants lead to the premature truncation of the open reading frame, pinpointing haploinsufficiency as the primary disease mechanism. In contrast, missense variants, which are primarily clustered in the guanosine triphosphatase (GTPase) domain, are believed to exert a dominant- negative effect and are strongly associated with an increased risk of developing the more severe ADOA “plus” phenotype ( _14_ , _54_ – _56_ ). In our study, the V346D variant identified in our patient with ADOA and the V291D variant from our novel mouse model are located within the leading portion of the GTPase domain ( _57_ ). Our _Opa1__V291D/+_ mouse model exhibited significantly reduced OPA1 protein levels. Similarly, cultured cells transfected with the V291D variant showed diminished levels and stability of the OPA1 protein. In turn, treatment with MG132 only partially restored the OPA1 levels, indicating that its degradation is not fully reliant on the ubiquitin- proteasome system and suggesting the involvement of additional regulatory mechanisms that contribute to the instability of the OPA1 protein. Our findings indicate that the OPA1 protein is highly unstable in the presence of this variant, supporting the hypothesis that the V291D missense variant causes haploinsufficiency. Similarly, patient- derived fibroblasts
**Fig. 8.** **_MitoLbNOX_ overexpression enhanced RGC function, survival, TCA cycle, and reduced oxidative stress in** **_V291D- VG2- MitoTag_ -** **_MitoLbNOX_ mice.** ( **A** ) Schematic diagram of the strategy used to generate _V291D- VG2- MitoTag_ and _V291D- VG2- MitoTag_ - _MitoLbNOX_ mice. ( **B** ) immunostaining of retinal sections from the _VG2MitoTag_ , _V291D- VG2- MitoTag_ , and _V291D- VG2- MitoTag- MitoLbNOX_ mouse models, showing GFP fluorescence colocalized with RBPMS+ RGc. ( **C** ) Analysis of PeRG recordings at 180 days ( _n_ = 13 mice per group; one- way AnOvA with tukey’s test _P =_ 0.0023, 0.6653, and 0.0227 for _Opa1__+/+_ ( _WT_ ) compared to _V291D- VG2- MitoTag_ , _WT_ compared to _V291D- VG2- MitoTag_ - _MitoLbNOX_ , and _V291D- VG2- MitoTag_ compared to _V291D- VG2- MitoTag_ - _MitoLbNOX_ , respectively). ( **D** ) Quantification of RGcs in the peripheral zone of whole- mounted retinas at 180 days ( _n_ = 5 mice per group; one- way AnOvA with tukey’s test _P_ = 0.0032, 0.6147, and 0.0175 for _WT_ compared to _V291D- VG2- MitoTag_ , _WT_ compared to _V291D- VG2- MitoTag_ - _MitoLbNOX_ , and _V291D- VG2- MitoTag_ compared to _V291D- VG2- MitoTag_ - _MitoLbNOX_ , respectively). ( **E** ) Representative immunostaining images of Pdhe1, idh3, and 4- hne in retinal sections from _WT_ , _V291D- VG2- MitoTag_ , and _V291D- VG2- MitoTag_ - _MitoLbNOX_ mice. Analysis of the fluorescence intensity of Pdhe1 ( _n_ = 5 mice per group; one- way AnOvA with tukey’s test _P_ = 0.0027, 0.5287, and 0.0192, for _WT_ compared to _V291D- VG2- MitoTag_ , _WT_ compared to _V291D- VG2MitoTag_ - _MitoLbNOX_ , and _V291D- VG2- MitoTag_ compared to _V291D- VG2- MitoTag_ - _MitoLbNOX_ , respectively), idh3 ( _P_ = 0.0028, 0.6565, and 0.0006), and 4- hne ( _P_ = 0.0135, 0.7149, and 0.0033) immunostaining in the ganglion cell layer. data are presented as means ± SeM. * _P_ < 0.05, ** _P_ < 0.01, *** _P_ < 0.001.
carrying missense variants in the GTPase domain of OPA1 display haploinsufficiency characterized by decreased OPA1 protein expression and a shortened protein half- life ( _58_ , _59_ ). Therefore, missense variants in the GTPase domain could lead to haploinsufficiency or a dominant- negative effect. Further experiments, including an active GTPase pull- down assay, are necessary to confirm this hypothesis.
Changes in OPA1 protein levels can disrupt the communication between mitochondria and the cell nucleus, resulting in significant transcriptional changes in neurons ( _60_ ). These alterations in mitochondrial dynamics can lead to a loss of coordination between mitochondrial and nuclear gene expression, particularly in the context of pathways that are involved in energy metabolism ( _61_ ). The cellular environment and energy- usage status can also affect the expression of both mitochondrial and nuclear- encoded energy metabolism transcripts, thereby highlighting the importance of a synchronized modulation between the nucleus and mitochondria in response to energy deficits and nutrient shifts ( _62_ , _63_ ). Furthermore, when mitochondrial dynamics are disturbed by OPA1 protein mutations, mitochondrial dysfunction can trigger retrograde signaling, in which stress signals are transmitted from the mitochondria to the nucleus ( _64_ , _65_ ). This signaling cascade can lead to changes in the expression of nuclear- encoded mitochondrial genes. A similar phenomenon is observed during the development of neurodegenerative disorders, such as Parkinson’s, Alzheimer’s, and Huntington’s diseases, in which mitochondrial abnormalities are closely associated with a significant down- regulation of the nuclear- encoded ETC and OXPHOS proteins, thus contributing to cellular aging in neural tissues ( _66_ – _68_ ). Increased oxidative stress, which can damage nucleic acids, likely plays a role in this premature aging ( _68_ , _69_ ). Furthermore, cells may activate apoptosis in response to oxidative stress, which can affect the expression of genes related to mitochondrial biogenesis ( _70_ ). The coordinated down- regulation of ETC and OXPHOS regulation in both mitochondria and the nucleus results in impaired mitochondrial metabolism, diminished energy production, and heightened oxidative stress, thereby creating a detrimental cycle that further promotes apoptosis ( _71_ ). This cascade aligns with the downregulation of ETC- related genes indicated by our snRNA- seq results, thus highlighting a potential mechanism for RGC- specific vulnerability in ADOA.
It is widely acknowledged but not well understood that RGCs are more susceptible than other retinal cells to mitochondrial dysfunction, although photoreceptors have the highest density of mitochondria in the retina. In addition, it remains unclear whether this vulnerability is primarily caused by a bioenergetic crisis, oxidative stress, or a combination of both ( _1_ , _15_ , _72_ ). A previous study introduced a mouse model of Leber hereditary optic neuropathy (LHON), which is a mitochondrial optic neuropathy caused by a variant in the _ND6_ gene, a key subunit of Complex I, and found that increased oxidative stress is likely a primary pathogenic factor in this disease, whereas ATP production was not affected ( _73_ ). Consequently, the accumulation of oxidative stress from impaired mitochondria is one of the major causes of RGC degeneration in LHON; thus, many studies have focused on antioxidants as potential treatments ( _16_ , _33_ ). Previous research has shown that idebenone, which bypasses defective Complex I and acts as an antioxidant, is a promising candidate that is currently an approved therapy for LHON ( _74_ , _75_ ). However, about half of the patients did not respond to this treatment, suggesting that oxidative stress alone is not the sole issue in LHON ( _76_ ). Furthermore, another report showed that multiple therapeutic targets affect
mitochondria and demonstrated that pathways beyond oxidative stress, including energy metabolism, mitochondrial biogenesis, and mitophagy, also play significant roles in fibroblasts derived from patients with LHON ( _77_ ). Here, we performed several experiments to assess the energetic and oxidative stress profiles of the _Opa1__V291D/+_ mouse retina. Our findings revealed an increase in oxidative stress and a reduction in ATP levels in _Opa1__V291D/+_ mouse retinas. These results suggest that both a bioenergetic crisis and oxidative stress contribute to the development of RGC degeneration in ADOA. Although both LHON and ADOA lead to RGC degeneration due to mitochondrial dysfunction, their underlying mechanisms may vary.
Furthermore, our results suggest that the increased oxidative stress and reduced ATP production observed in the _Opa1__V291D/+_ retina are likely attributable to compromised Complex I activity. Complex I not only plays a significant role in maintaining the balance of oxidative stress but also functions as the entry point for electrons in the ETC and as a proton pump to create a proton gradient ( _43_ , _78_ ). Although electrons can still enter the ETC through Complex II via the reduced form of flavin adenine dinucleotide (FADH2) if there is damage to Complex I, this affects the efficiency of OXPHOS and ATP production because Complex II does not contribute to proton translocation ( _79_ ). The decrease in Complex I activity observed in the _Opa1__V291D/+_ retina may be partly attributed to abnormalities in the inner mitochondrial membrane. The OPA1 protein, which is primarily responsible for inner mitochondrial membrane fusion, plays a critical role in maintaining the structure of mitochondrial cristae. Defective OPA1 protein can disrupt crista remodeling, destabilize respiratory complexes, and ultimately impair Complex I function ( _80_ ). Consistent with this mechanism, our blue- native polyacrylamide gel electrophoresis revealed a trend toward reduced levels of Complex I–containing supercomplexes in _Opa1__V291D/+_ retinas (fig. S8), suggesting subtle alterations in supercomplex stability. Although these changes did not reach statistical significance, they align with prior in vitro evidence that _OPA1_ variants can affect the structural organization of Complex I ( _81_ , _82_ ). Therefore, the relationship among _OPA1_ variants, mitochondrial structural changes, and Complex I dysfunction is closely interconnected, with each factor influencing the others in the pathogenesis of ADOA.
Although the V291D variant impaired energy production and increased oxidative stress throughout the retina, the functional impairments in _Opa1__V291D/+_ mice were limited to RGCs. This selective degeneration may be attributed to their heightened vulnerability to energy deficits, which are driven by their high energy demands, long axons, and lack of a myelinated sheath before the lamina cribrosa ( _83_ , _84_ ). In contrast, photoreceptors, which have the highest density of mitochondria in the retina, prefer glycolysis for energy production and can use lipids to compensate for ATP deficiencies ( _45_ , _47_ , _48_ , _85_ , _86_ ), whereas inner retinal cells, including RGCs, rely heavily on mitochondrial ETC and OXPHOS and exhibit a lower glycolytic activity ( _45_ , _46_ ). This greater reliance on ETC and OXPHOS renders RGCs particularly sensitive to mitochondrial dysfunction, explaining their susceptibility to degeneration in ADOA ( _83_ , _84_ , _87_ ). Although ATP production was generally decreased in the retina of the _Opa1__V291D/+_ mouse model, a metabolic shift toward glycolysis was observed, particularly in the outer retinal layers, suggesting that photoreceptors compensate for ATP deficiency by up- regulating glycolysis, consistent with previous findings ( _48_ , _88_ , _89_ ). This highlights the relationship between altered energy metabolism and the metabolic flexibility of retinal cell types ( _90_ ). The inability of RGCs
to adapt to defective ETC function, in contrast to the metabolic flexibility of photoreceptors, underscores the significant role of compromised energy metabolism in RGC degeneration associated with ADOA. This deficiency in energy production further elevates oxidative stress, creating a harmful cycle that worsens neuronal degeneration ( _91_ ).
Our _Opa1__V291D/+_ missense variant mouse model showed RGC abnormalities, both anatomically and functionally, closely matching the clinical presentation of human patients with ADOA. Although previous mouse models with truncated OPA1 proteins revealed changes in the shape and structure of mitochondria in the whole mouse retina and optic nerve ( _17_ , _21_ , _23_ ), transcriptomic changes in the retina at single- cell resolution remain unexplored. Moreover, the selective vulnerability of RGCs, with photoreceptors remaining largely unaffected, has yet to be fully understood. Our study revealed a significant downregulation of glycolytic proteins in the ganglion cell layer of the _Opa1__V291D/+_ retinas, as assessed using immunostaining; furthermore, our snRNA- seq analysis identified down- regulated energy production–related genes, including those involved in ETC and glycolysis, specifically in the RGC cluster. However, we did not detect significant changes in these genes related to energy- production pathways in other retinal cell types between _WT_ and _Opa1__V291D/+_ mice, including cones and rods, thus providing a potential explanation for the lack of significant photoreceptor dysfunction in our patient and mouse model. This impaired metabolic adaptation in RGCs likely exacerbates the bioenergetic crisis, ultimately contributing to their selective degeneration.
Although the cause- and- effect relationship between oxidative and metabolic stress is not fully understood in the pathogenesis of ADOA, we believe that both factors contribute to RGC degeneration in ADOA and that interrupting this vicious cycle could serve as a potential therapeutic target for the condition. In our study, we demonstrated that increasing the NAD+ /NADH redox ratio by _MitoLbNOX_ overexpression could improve energy metabolism via the TCA cycle and reduce oxidative stress in the _Opa1__V291D/+_ mouse model. This, in turn, promoted neuronal survival and successfully mitigated the detrimental effects of the _Opa1_ variant, restoring both functional integrity and survival in RGCs of _Opa1__V291D/+_ mice. In mitochondria, NAD+ serves as a coenzyme for three rate- limiting enzymes in the TCA cycle, where it is reduced to NADH, generating ATP for direct energy supply and producing FADH2 as an alternative electron donor for Complex II in the ETC ( _92_ ). Beyond our findings, a previous showed that _MitoLbNOX_ overexpression could activate the TCA cycle by increasing the NAD+ /NADH redox ratio in m.3243A>G fibroblasts ( _53_ ). Moreover, evidence from other disease models has shown that replenishing NAD+ levels can increase energy metabolism, reduce oxidative stress, and prolong survival across various cell types, including those in the heart, liver, and inflammatory cells ( _93_ – _96_ ). Last, our findings following _MitoLbNOX_ overexpression reaffirmed the critical role of bioenergetic crisis and oxidative stress, driven by Complex I dysfunction, in RGC degeneration, highlighting NAD+ /NADH homeostasis as a promising therapeutic target for preventing RGC loss in ADOA.
Despite evidence that _Opa1__V291D/+_ RGCs exhibit impaired metabolic compensation and heightened vulnerability to mitochondrial dysfunction, the precise mechanisms underlying this cell type–specific susceptibility remain incompletely understood. Although our data show that Complex I–driven NAD+ /NADH imbalance selectively disrupts glycolytic and TCA cycle rewiring in RGCs, the reason this effect is confined to inner retinal neurons rather than photoreceptors remains unresolved. A previous publication highlighted that
mitochondria display distinct “mitotypes” across cell types, reflecting specialized structural and functional adaptations to unique energetic demands ( _78_ ). In this context, RGCs may depend more heavily on Complex I–linked redox balance, whereas photoreceptors may have greater metabolic flexibility or alternative substrate usage that buffers against OXPHOS perturbations. Nevertheless, the molecular determinants of this selective vulnerability remain to be fully elucidated.
In conclusion, we developed the _Opa1__V291D/+_ missense mouse model, which recapitulated ADOA phenotypes. The V291D variant reduced _OPA1_ protein stability and expression, supporting a haploinsufficiency mechanism. It impaired mitochondrial morphology and Complex I function, leading to oxidative stress, ATP depletion, and an energetic crisis. As a compensatory response, the retina exhibited a metabolic shift toward glycolysis, but RGCs failed to upregulate glycolytic proteins. Spatial metabolomics, immunostaining, and snRNA- seq revealed pronounced bioenergetic crisis and downregulated energy- production genes in RGCs, highlighting their selective vulnerability in ADOA. Notably, increasing mitochondrial NAD+ /NADH redox ratio by _MitoLbNOX_ overexpression in RGC could improve energy metabolism, reduce oxidative stress, and enhance RGC survival, underscoring the therapeutic potential of targeting mitochondrial metabolism in ADOA.
### MATERIALS AND METHODS
## Study design
The objective of this study was to investigate the impact of a patientderived _Opa1_ missense variant on RGC degeneration, as well as to determine why RGCs are particularly vulnerable to mitochondrial dysfunction in ADOA. To achieve this, we generated a novel patientspecific knock- in _Opa1__V291D/+_ mouse model and conducted survival experiments to analyze functional phenotypes, as well as nonsurvival experiments for anatomical phenotyping and molecular assessments. Immunostaining, spatial metabolomics, and snRNA- seq were performed to examine the impact of the _Opa1_ variant at both the tissue and cellular levels. In addition, we examined how increasing the NAD+ /NADH redox ratio in RGCs affects their survival in our ADOA mouse model. This study was approved by the Institutional Review Board of Columbia University (no. AAAV3523) and adhered to the principles of the Declaration of Helsinki. Because of the retrospective nature of the study and the use of deidentified historical data, the Institutional Review Board granted a waiver of informed consent. All animal experiments were approved by the Institutional Animal Care and Use Committee of Columbia University (no. AC- AABQ7582).
## Patients with ADOA and mouse models
Patients with clinically diagnosed ADOA were reviewed, and their genetic testing reports were assessed at the Columbia University Irving Medical Center. An _OPA1_ missense variant was identified in one patient and was used to generate a knock- in mouse model. The patientspecific _Opa1__V291D/+_ mouse model was created by C.- S.L. The V291D point variant was introduced using the GalK pop- in- pop- out method into a bacterial artificial chromosome (BAC) clone (RP23- 229C8) from the BACPAC Resources Center (https://bacpacresources.org). A gene- targeting vector was prepared using the BAC recombineering method and electroporated into KV1 (129S6 hybrid) embryonic stem (ES) cells, to generate targeted ES clones via homology recombination;
the method showed an absence of aberrant splicing donor or acceptor activity. This knock- in mouse harbored a T- to- A missense variant, which converted the 291st amino acid of OPA1 from valine to aspartic acid. These mice were backcrossed to the _C57BL/6J_ strain (JAX no. 000664, the Jackson Laboratory) for five generations and then genotyped, which confirmed the absence of the _rd8_ variant ( _97_ ). All mice analyzed in this study were heterozygous _Opa1__V291D/+_ mice exhibiting normal longevity and fertility. In subsequent experiments, littermatecontrol _WT_ mice ( _Opa1__+/+_ ) were used for comparisons with _Opa1__V291D/+_ mice. To label mitochondria and assess their morphological features in these mice, we crossed the _Opa1__V291D/+_ mice with _mito::mKate2_ reporter mice (JAX no. 032188, the Jackson Laboratory), to express the fluorescent mKATE2 protein specifically in mitochondria. Housing for these animals was provided by the animal care facility of the Institute of Comparative Medicine at Columbia University.
## Pattern electroretinography
The PERG was conducted as described in prior publications ( _98_ , _99_ ). In brief, we used the PERG Animal System (Jorvec Corp, Miami, FL) for our recordings. The PERG signals from each eye were desynchronized using a phase- locking averaging method with two noncorrelated frequencies (right eye, every 492 ms; left eye, every 496 ms) and then averaged over three consecutive session blocks ( _98_ ). To assess the RGC- specific function, we measured the P1N2 amplitude from the peak positive waves (P1) to the lowest negative waves (N2) recorded in the grand- average PERG waveforms.
retinas were fixed in cold 4% paraformaldehyde in phosphate- buffered saline for 1 hour. To identify RGCs, a mouse anti- BRN3A antibody (1:50, MAB1585, Millipore) was used, followed by incubation with a secondary donkey anti- mouse antibody (1:200, 715- 225- 151; Jackson ImmunoResearch). RGCs were quantified using flat- mounted retinas, as described previously ( _102_ , _104_ ). We obtained 4, 4, and 12 squares with a size of 300 μm by 300 μm from each central, midperipheral, and peripheral retinal zone, respectively. The RGC counts from all squares were then totaled and analyzed. All images were acquired using a Nikon Ti Eclipse inverted confocal microscope. BRN3A+ cells were counted semiautomatically and quantitatively using the ImageJ software (https://imagej.net/ij/).
## Confocal microscopy assessment of mitochondrial morphology in mouse optic nerves
To analyze mitochondrial characteristics, we used SDCM with SRRFs in both _WT_ and _Opa1__V291D/+_ mice. Cryosections of optic nerves were prepared from both groups, and mitochondria were visualized through mKate2 expression, which enabled red fluorescence excitation (561 nm/594 nm) using an SDCM system (Dragonfly 600, Oxford Instruments Andor) with an iXon 888 Life EMCCD camera. Superresolution images were captured using a 100× oil objective and the Andor FUSION software (Oxford Instruments Andor), which operates the SRRF function. After acquiring the images, we applied deconvolution techniques and analyzed the data using the Surface Rendering Model provided in the iMaris software (v10.2) to thoroughly compare mitochondrial characteristics between the mouse models.
## Flash electroretinography
Flash ERG assessments were conducted according to previous publications ( _100_ ) using an Espion system coupled with a Ganzfeld stimulator (Colordome, Diagnosys LLC, Lowell, MA), to measure scotopic and photopic serial intensities. To assess the STR, the light intensities of the stimuli that were used for scotopic serial- intensity ERG were −5.6, −5.3, and −5.0 log cd·s/m2 in sequence. After a 10- min period of light adaptation, PhNRs were elicited using three different stimulus intensities, i.e., 0, 1, and 2 log cd·s/m2 , against a 10- cd·s/m2 rod- saturating green background. For each intensity level, an average of 25 flashes was calculated, with an interstimulus interval of 3000 ms. The positive and negative STRs were measured at 100 and 233 ms, respectively. To specifically evaluate the RGC function, PhNR amplitudes were measured from the baseline to the PhNR trough.
## Spectral domain–optical coherence tomography
We performed live imaging to measure the thickness of the RNFL using an SD- OCT imaging device (Envisu UHR2210, Bioptigen, Durham, NC, USA), which provides an axial resolution of 1.75 μm in tissue, according to previously established protocols ( _101_ ). A rectangular scan of 1.8 mm in length and width was performed, with 0° angle adjustments and no horizontal or vertical offsets. The scan settings included 1000 A- scans per B- scan, 100 B- scans, and 10 frames per B- scan, with 80 inactive A- scan lines per B- scan and one volume captured (fig. S9). The resulting 10- frame OCT images were averaged using the Bioptigen InVivoVue (v2.4) software and then further processed with the Bioptigen Diver (v.3.4.4) software, to obtain measurements of RNFL thickness.
## RGC counting in flat- mounted retinas
Immunolabeling and fluorescent staining of flat- mounted retinas were performed as previously described ( _102_ , _103_ ). Eyecups for flat- mounted
## Transmission electron microscopy
We used TEM to examine the morphology of mitochondria and the myelination of axons. Ultrathin cross sections were obtained from the optic nerve and stained with uranyl acetate and lead citrate for contrast enhancement. These sections were imaged using a Hitachi 7100 transmission electron microscope (TEM instrument; Hitachi, Tokyo, Japan) equipped with an advanced digital camera system for microscopy techniques.
## Immunoblotting
Mouse retinas were dissected at 180 days of age and homogenized with radioimmunoprecipitation assay (RIPA) lysis and extraction buffer (89900, Thermo Fisher Scientific), supplemented with protease and phosphatase inhibitor cocktails (P0044 and P8340, MilliporeSigma). This process was followed by sonication using an SLPe Digital Sonifier (Branson Ultrasonics, Brookfield, CT). The resulting supernatant was collected for protein quantification and subsequent Western blot analysis of total retinal proteins. Protein concentrations were determined with a Pierce BCA assay kit (23225, Thermo Fisher Scientific). For electrophoresis, proteins were denatured and separated using a Mini Blot system (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). The separated proteins were transferred onto polyvinylidene fluoride membranes (PB5240, Invitrogen) with a Power Blotter system (PB0012, Invitrogen). The membranes were incubated in blocking buffer for 30 min, followed by applying primary antibodies and incubating at 4°C overnight. Secondary antibodies were applied at room temperature for 2 hours. Details of the primary and secondary antibodies, as well as other materials, are provided in table S4. Signals were visualized using an iBright 1500 Imaging System (Invitrogen, Thermo Fisher Scientific), and data were analyzed with the iBright Analysis Software (v5.2.1).
## Quantitative real- time PCR for** **_Opa1_
To assess the gene expression levels of _Opa1_ between mutant and control mice, total RNA was extracted from mouse retinas using RNeasy kits (QIAGEN), following the manufacturer’s guidelines. cDNA was synthesized using the SuperScript VILO cDNA Synthesis Kit (Invitrogen), following the provided instructions. qPCR was then conducted using dye- based techniques with specifically designed primers (table S2), and samples were run in technical triplicate. The qPCR mixtures were prepared using the PowerTrack SYBR Green Master Mix (Thermo Fisher Scientific). A CFX Connect Realtime PCR Detection System (Bio- Rad Laboratories, Hercules, CA, USA) was used to monitor and analyze gene expression.
## Cell culture for protein stability testing
HEK293 cells (BCRC 60019; Bioresource Collection and Research Center, Hsinchu, Taiwan) were cultured to form a monolayer in a medium supplemented with 10% fetal bovine serum. Lipid- based transfections (Lipofectamine 3000, Invitrogen) were conducted using cytomegalovirus plasmid vectors (pcDNA3.1, GenScript) that carried a 3X Flag tag to insert the _Opa1__WT_ and _Opa1__V291D_ genes. After transfection, the cells were lysed, and immunoprecipitation was performed to isolate the ubiquitinated OPA1 protein. MG132 (25 μM) was used to inhibit protease activity. The isolated proteins were analyzed to evaluate the ubiquitination status and stability of the OPA1 protein in the lysates obtained from HEK293 cells transfected with _Opa1__WT_ and _Opa1__V291D_ .
## Coimmunoprecipitation
For coimmunoprecipitation assays, HEK293 cells were transiently transfected with either an empty control vector or a Flag- tagged OPA1 expression construct using Lipofectamine 3000 (Thermo Fisher Scientific), according to the manufacturer’s protocol. After 48 hours, cells were harvested and lysed in ice- cold RIPA buffer supplemented with protease inhibitors. Clarified lysates were incubated overnight at 4°C with anti- Flag M2 agarose beads (Sigma- Aldrich, M8823). Bound proteins were washed, eluted, and subjected to immunoblot analysis. Experimental procedures were performed following previously published protocols ( _105_ ).
## Analysis of mitochondrial respiratory and hydrolytic function in retinas
To evaluate mitochondrial function in the _Opa1__V291D/+_ mouse model, we used the RIFS and HyFS assays on a Seahorse XF analyzer (Agilent Technologies, Cedar Creek, TX, USA), as described elsewhere ( _40_ – _42_ ). Retinal and heart tissues were harvested and immediately frozen at −80°C and then sent for analysis. Frozen tissues were placed in tubes containing four 3- mm zirconium beads and homogenized in mitochondrial assay solution [MAS buffer: 70 mM sucrose, 220 mM mannitol, 5 mM KH2PO4, 5 mM MgCl2, 1 mM EGTA, and 2 mM Hepes (pH 7.4)] using a bead homogenizer (Benchmark Scientific, Sayreville, NJ, USA) for 30 s at 6.5 m/s. The homogenates were then centrifuged at 1000 _g_ for 5 min at 4°C, and the supernatants were collected. Protein concentrations were determined, with retinal samples showing optimal responses to substrates at concentrations of 10 μg in the assays. This procedure yields a mixed mitochondrial homogenate containing disrupted mitochondria and submitochondrial particles with varying membrane orientations. Because the samples were previously frozen, exogenous NADH can access the matrix- facing NADH- binding site of Complex I. Thus, NADH was used directly as the substrate (1 mM) to assess Complex I–linked respiration, rather than pyruvate/malate.
Complex II–linked respiration was measured using 5 mM succinate in the presence of 2 μM rotenone to inhibit Complex I. To inhibit the ETC upstream of Complex IV, 4 μM antimycin A (a Complex III inhibitor) and 2 μM rotenone (a Complex I inhibitor) were used. Complex IV activity was assessed by supplying electrons through 0.5 mM _N_ , _N_ , _N_ ', _N_ '- tetramethyl- p- phenylenediamine (TMPD; maintained in a reduced state by 1 mM ascorbate), with 50 mM azide serving as a Complex IV inhibitor. Oxygen consumption rates were accurately measured and normalized to protein content and mitochondrial density using MTDR, to account for variations in sample processing or intrinsic mitochondrial differences. Because of limited retinal material, two complexes were typically measured per well. In the HyFS assay, the hydrolytic capacity of Complex V (ATP synthase) was assessed under uncoupled conditions. The assay was initiated with 5 mM succinate and 2 μM rotenone to measure respiratory capacity through Complex II. The ETC was then shut down with 2 μM antimycin A, and 1 μM carbonyl cyanide _p_ - trifluoromethoxyphenylhydrazone was added to ensure complete uncoupling. Subsequently, 20 mM ATP was injected to drive ATP synthase in the reverse (hydrolytic) direction, while 5 μM oligomycin was added to inhibit Complex V activity. Because the mitochondrial membranes are disrupted, ATP freely accesses the matrix- facing catalytic site of Complex V, allowing direct measurement of ATP hydrolysis–driven oxygen consumption independent of ADP/ATP translocase function. The output data from three technical replicates were averaged for analysis. The ATP hydrolytic capacity measurements were normalized to Complex V expression, as determined using immunoblotting for ATP5A1.
## MALDI- TOF MS imaging
To investigate metabolomics changes in the mouse retina, MALDITOF MS imaging was performed at the MALDI MS Imaging Facility, Advanced Science Research Center, The City University of New York. Mouse eyeballs were harvested at 200 days of age, embedded in 4% CMC (no. 419273, Sigma- Aldrich) at −10°C, and snap frozen on dry ice. Cryosections (10- μm thickness) were prepared using a CryoStar NX70 (Thermo Fisher Scientific), mounted on indium tin oxide–coated slides (no. 8237001, Bruker Daltonics), and desiccated under vacuum for 30 min. Matrix deposition was performed with an HTX M5 sprayer (HTX Technologies) using 2,5- dihydroxybenzoic acid (DHB) (no. D2933, TCI Chemicals) 40 mg/ml in methanol/water, 70/30 at 85°C for 8 cycles or _N_ - (1- naphthyl) ethylenediamine dihydrochloride (NEDC, no. 222488, Sigma- Aldrich) 10 mg/ml in isopropanol/water, 70/30 at 80°C for 30 cycles. The same spray parameters were used for both matrices: velocity of 1300 mm/min; track spacing of 2 mm; N2 pressure of 10 psi (68.95 kPa); flow rate of 3 liters/min; and nozzle height of 40 mm. Initial spectra acquisition was conducted using a MALDI- TOF MS Autoflex (Bruker Daltonics) in positive ion (DHB) or negative ion (NEDC) mode, which was calibrated with red phosphorus (no. 343242, Sigma- Aldrich). The following settings were used for both ion modes: raster width of 25 μm, laser smartbeam of “minimum,” laser frequency of 500 Hz, 500 shots per position, and mass/charge ratio ( _m_ / _z_ ) range of 60 to 1200. Ion images were processed using FlexImaging (v3.0) and SCiLS Lab (v2015b), normalized via root mean square, and a bin width of ±0.10 to ±0.20 according to peak width at a certain _m_ / _z_ . The spectra were interpreted manually, and the analytes were assigned according to a method described previously ( _106_ ). To validate and extend metabolic coverage, high- resolution imaging was subsequently performed using a timsTOF fleX MALDI- 2 instrument (Bruker Daltonics) in both positive (DHB)
and negative (NEDC) ion modes. The instrument was operated with the following settings: raster width 20 μm, SmartBeam laser in “Single” mode, laser frequency 10,000 Hz, 200 shots per pixel (positive mode), 250 shots per pixel (negative mode), and an _m_ / _z_ acquisition range of 50 to 1000. Data were acquired using timsControl software and processed with SCiLS Lab using the same normalization strategy described above. Key metabolites were detected as follows: AMP at _m_ / _z_ 346.1 as [AMP- H]− , G6P at _m_ / _z_ 171.0 as [G6P- H]− , pyruvate at _m_ / _z_ 87.0 as [pyruvate- H]− , and ATP at _m_ / _z_ 508.0 as [ATP + H]+ . Quantification was performed within defined regions of interest in the tissue.
## Hematoxylin and eosin staining
Hematoxylin and eosin staining was performed on tissue sections after MALDI imaging, to access the histology of the MALDI images. The residual matrix was removed by rinsing slides with 95% ethanol, after which the sections were stained with Hematoxylin Gill No. 1 and Eosin Y (Sigma- Aldrich) according to the manufacturer’s instructions. The stained sections were imaged using a Leica Aperio CS2 slide scanner at ×20 magnification with a 0.75–numerical aperture Plan Apo objective. These images provided anatomical context for mass spectral data, allowing the establishment of precise correlations between molecular and histological features. Quantification was performed within defined regions of interest in the tissue.
## Immunostaining
To assess protein expression distribution in mouse retinal histology, immunofluorescence was performed on cryosections of mouse retinas at 360 days of age according to previously established protocols ( _100_ ). Briefly, slides were prepared using mouse retinas embedded in optimal cutting temperature compound (Tissue- Tek O.C.T. Compound, Sakura Finetek). The primary and secondary antibodies listed in table S4 were used for staining. Imaging was carried out using a Zeiss LSM 900 microscope equipped with an Airyscan super- resolution image scanning system (Carl Zeiss, Germany). Z- stack images spanning 5 μm with a step size of 0.3 μm were acquired from all retinal sections. Postacquisition processing and deconvolution were performed using the Airyscan Joint Deconvolution feature in the ZEN Blue software (v3.7). Images from matched mutant and _WT_ samples were captured during the same experimental session under identical imaging settings. The fluorescence intensity in each maximum projection image was manually segmented and quantitatively measured using the ImageJ software (https://imagej.net/ij/).
## ATP measurements from mouse retinas
ATP levels were measured in the retinas using a commercially available kit [ab83355, ATP Assay Kit (Colorimetric), Abcam] according to the manufacturer’s instructions. Fresh retinal tissue from both eyes of each mouse was carefully dissected and homogenized in the assay buffer. The homogenate was centrifuged at 13,000 _g_ for 5 min at 4°C, and the resulting supernatant was collected for protein quantification and subsequent analysis. To prevent enzyme interference in the assay, deproteinization was performed using a kit (ab204708, Deproteinizing Sample Preparation Kit, Abcam). After a 30- min incubation, the ATP assay was conducted, and optical density readings were taken at 570 nm using a microplate reader.
## NAD****+** **measurements from mouse retinas
To assess the levels of NAD+ and NADH in mice, we used a commercially available kit [ab65348, NAD+ /NADH Assay Kit (Colorimetric),
Abcam] following the manufacturer’s instructions. We collected retinas from each mouse, homogenized them, and centrifuged the mixture at 14,000 _g_ for 5 min at 4°C. Next, we transferred the supernatant to a 10- kDa spin column (ab93349, 10kD Spin Column, Abcam) and centrifuged it at 10,000 _g_ for 20 min at 4°C. The filtrate was collected for protein quantification and the NAD assay. Optical density readings were taken at 450 nm using a microplate reader at room temperature 1 hour after the procedure.
## GSH measurements from mouse retinas
Total GSH and reduced GSH levels were measured using a commercially available kit [ab239709, GSH+GSSG/GSH Assay Kit (Colorimetric), Abcam], following the manufacturer’s instructions. Retinal tissues were collected from both eyes of each mouse and homogenized in the buffer supplied with the kit. Protein quantification was conducted before adding 5% 5- sulfosalicylic acid to precipitate the proteins in the samples. Next, the reaction mix and substrate solution were added to the samples and incubated for 10 min. Optical density readings were taken at 415 nm using a microplate reader at room temperature 10 min after the procedure. The levels of GSH and GSSG were calculated on the basis of the optical density readings.
## SOD measurements from mouse retinas
SOD levels were measured using a commercial kit [ab65354, Superoxide Dismutase Activity Assay Kit (Colorimetric), Abcam] following the manufacturer’s instructions. Retinal samples were homogenized in ice- cold immunoprecipitation lysis buffer (no. 87787, Thermo Fisher Scientific) that contained 1 mM phenylmethylsulfonyl fluoride protease inhibitor (no. 36978, Thermo Fisher Scientific). The homogenates were then centrifuged at 14,000 _g_ for 5 min at 4°C, and the supernatants were collected for analysis. The SOD assay was performed by mixing the supernatant with the working solution provided in the kit, followed by incubation at 37°C for 20 min. Optical density readings were obtained at 450 nm using a microplate reader to quantify SOD activity.
## Lactate measurements in mouse retinas
The levels of lactate in the retinas were measured using a commercially available kit [ab65331, l- Lactate Assay Kit (Colorimetric), Abcam] according to the manufacturer’s instructions. Fresh retinal tissue from both eyes of each mouse was carefully dissected and homogenized. The homogenate was then centrifuged at 14,000 _g_ for 5 min at 4°C, and the resulting supernatant was collected. Deproteinization (ab204708, Deproteinizing Sample Preparation Kit, Abcam) was carried out to prevent lactate degradation by endogenous LDH. The deproteinized supernatant was then used for the assay. After a 30- min incubation at room temperature, the optical density was measured at 450 nm on a microplate reader.
## Single- nucleus RNA sequencing
To investigate the impact of this _Opa1_ variant on the retinal transcriptomes at the single- cell level, we performed snRNA- seq on pooled frozen retinal tissues. Nucleus extraction was performed using the Miltenyi Nuclei Extraction Buffer (Miltenyi Biotec) according to the manufacturer’s guidelines. Upon isolation, the nuclei were counted using trypan blue and a Countess III Automated Cell Counter (Thermo Fisher Scientific, Waltham, MA, USA). snRNA libraries were prepared using the Chromium Single Cell 3′ kit (10x Genomics) and sequenced on an Illumina platform using standard protocols. After obtaining the sequencing data, we used Cell Ranger
(v8.0) with default parameters to generate a filtered_feature_bc_ matrix.h5 file containing cell barcodes and transcript counts for each sample. The data were aggregated using the Cell Ranger aggr program. The integrated dataset was first imported into the Rosalind platform (www.rosalind.bio/) for dimension reduction and unsupervised clustering using Cell Ranger Graph Based Clustering (10x Genomics). The dataset was then loaded into R (v4.2) and the Seurat package (v5.0) ( _107_ ). Cell types were annotated using SC- type (v1.0) ( _108_ ) with cell markers for major retinal cells (table S3). A pathway enrichment analysis was performed using clusterProfiler (v4.10.1) ( _109_ ) with the REACTOME ( _110_ ) and WikiPathways ( _111_ ) databases. The results of differential gene expression analyses were visualized using heatmaps and dot plots wrapped in the Seurat package, and normalization was performed using log2 transformation. The snRNA- seq data have been deposited into the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus repository (GSE292269).
## High- resolution spatial transcriptomics of the mouse retinas
Mouse eyes from 280- day _WT_ and _Opa1__V291D/+_ mice were enucleated after euthanasia. Whole eyecups were fixed in 10% neutralbuffered formalin for 12 to 24 hours, dehydrated, and paraffin embedded using standard histological procedures. Retinal sections (10- μm thickness) were collected onto 10x Genomics Visium HD FFPE Spatial Gene Expression slides. Sections were deparaffinized, stained with hematoxylin and eosin, and imaged to document tissue morphology and orientation. Target retrieval, probe hybridization, and on- slide chemistry were performed according to the 10x Genomics Visium HD FFPE protocol, with minor optimizations for retinal tissue integrity. Spatial gene expression libraries were constructed per manufacturer instructions, sequenced on an Illumina platform, and processed using Space Ranger (10x Genomics) for alignment, segmentation, and feature quantification. Annotation of ganglion cell–enriched regions was done by using QuPath ( _112_ ). Downstream spot- level analysis and clustering were performed in Seurat package (v5.0) ( _107_ ).
## Generation of RGC- specific** **_MitoLbNOX_ overexpression in
## _Opa1_****_V291D/+_** **mice
In this study, we generated _Rosa26__LSL- MitoLbNOX_ ( _LoxP- Stop- Lox[LSL]MitoLbNOX_ ) mice using a method similar to that used for the _Rosa26__LSL- MitoTag_ line (JAX no. 032290, the Jackson Laboratory), which incorporates _3XHA- EGFP- OMP25_ ( _MitoTag_ cassette) into the _Rosa26_ locus for targeted mitochondrial _EGFP_ expression ( _113_ ). We constructed a targeting vector containing a _CAG_ promoter, a _loxP_ - flanked reversed neomycin cassette, an _SV40 poly- adenylation_ sequence, and cDNA encoding _MitoLbNOX_ from the pUC57- mito _Lb_ NOX plasmid (Addgene plasmid no. 74448), which was linearized and targeted to intron 1 of the mouse _Rosa26_ gene. To achieve conditional _mitoLbNOX_ overexpression in _Opa1__V291D/+_ mice, we crossed _LSL- MitoLbNOX_ mice with _Opa1__V291D/+_ mice, generating _Opa1__V291D/+_ _; Rosa26__LSL- MitoLbNOX/+_ offspring ( _V291D- MitoLbNOX_ ). For the RGC- specific mitochondrial reporter _Cre_ line, we created double homozygous _Vglut2__Cre_ _; Rosa26__LSL- MitoTag_ ( _VG2- MitoTag_ ) mice by crossing _Vglut2- Ires- Cre_ mice (JAX no. 28863, the Jackson Laboratory) with _MitoTag_ reporter mice (JAX no. 032290, the Jackson Laboratory) over two generations. Last, to compare mice with and without _mitoLbNOX_ overexpression in the RGC of _Opa1__V291D/+_ mice, we crossbred _V291D- MitoLbNOX_ mice with _VG2- MitoTag_ mice and selected _V291D- VG2- MitoTag_ and _V291DVG2- MitoTag- MitoLbNOX_ offspring for experiments (Fig. 8A).
## Statistical analysis
Study mice were matched for sex and age between the littermatecontrolled _WT_ and mutant groups. Statistical analyses were conducted using GraphPad Prism (v10.4), SPSS Statistics (v21), and R (v4.2). Unpaired independent _t_ tests or linear regression analyses were used to compare the continuous parameters between the two groups. One- way analysis of variance (ANOVA) was used for comparisons of the continuous parameters between three groups. Continuous variables are expressed as the means ± SEM in the plots. _P_ values derived from multiple testing were corrected using the BenjaminiHochberg method. A two- tailed _P_ value of <0.05 and a _q_ value of <0.1 indicated statistical significance.
## Supplementary Materials
**The PDF file includes:** Figs. S1 to S9 tables S1 to S4 legends for supplementary excel files
**Other Supplementary Material for this manuscript includes the following:**
Supplementary excel Files
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**Acknowledgments:** We would like to express our gratitude to t. c. Swayne and the confocal and Specialized Microscopy Shared Resource at the herbert irving comprehensive cancer center, columbia University, for technical assistance. We also thank n. nolan, J. Zhao, c. P.- Y. Su, and S. chang from the department of Ophthalmology at columbia University irving Medical center for support and A. h.- F. lin and B. Y.- l. chou from Raising Statistic consultant inc. for
assistance with the statistical analyses. the salary of S.h.t. was supported by the national eye institute (nei), national institutes of health, under awards U01eY034590, R24eY028758, P30eY019007, R01eY033770, R01eY018213, and R01eY024698, and by the Richard Jaffe Foundation, the nYee Foundation, the Rosenbaum Family Foundation, and unrestricted funds from Research to Prevent Blindness (RPB). **Funding:** this work was funded by chang Gung Memorial hospital, taiwan (cMRPG3n1001 and cMRPG3Q0451) (e.Y.- c.K.); national Science and technology council, taiwan (nStc 113- 2314- B- 182A- 150- MY3) (e.Y.- c.K.); chang Gung University, taiwan (UARPd1n0031 and UARPd1P0261) (e.Y.- c.K.); national eye institute of the national institutes of health grant R01eY033359 (G.t.); national eye institute of the national institutes of health grants R01eY031354 and R21eY037007 (n.- K.W.); Gerstner Philanthropies (n.- K.W.); the United Mitochondrial disease Foundation (n.- K.W.); Genetically Modified Mouse Model Shared Resource irving comprehensive cancer center at columbia University, national institutes of health nci cancer center Support Grant P30cA013696 (c.- S.l.); national institute of General Medical Sciences of the national institutes of health grant 1S10Od030401- 01A1 (t.- d.l.) and S10Od036268 (Y. h.); national eye institute of the national institutes of health Shared instrument grant S10Od028637 and national eye institute of the national institutes of health grants U01eY034590, R24eY028758, 5P30eY019007, R01eY033770, R01eY018213, and R01eY024698 (S.h.t.); the Richard Jaffe Foundation (S.h.t.); the nYee Foundation (S.h.t.); the Rosenbaum Family Foundation (S.h.t.); and an unrestricted grant to the department of Ophthalmology, columbia University, from Research to Prevent Blindness, new York, nY. **Author contributions:** conceptualization: c.- n.t., e.Y.- c.K., c.- c.l., c.- S.l., n.- K.W., S.h.t., Y.- J.t., and O.S. Methodology: t.- d.l., i.Y.- F.c., J.P., e.Y.- c.K., c.- c.l., c.K., G.t., h.- c.h., c.- S.l., n.- K.W., S.h.t., J.c., c.- Y.h., e.h.W., and Y.- J.t. investigation: t.- d.l., c.- n.t., J.P., e.Y.- c.K., P.- h.l., c.- c.l., K.P.M., c.- l.t., c.- S.l., n.- K.W., S.h.t., J.c., l.S., W.- h.P., e.h.W., and Y.- J.t. visualization: Y.- c.t., Y.h., i.Y.- F.c., c.- c.l., K.P.M., c.- S.l., n.- K.W., J.c., e.h.W., and Y.- J.t. validation: t.- d.l., c.- n.t., i.Y.- F.c., J.P., e.Y.- c.K., c.- c.l., c.- S.l., n.- K.W., S.h.t., J.c., W.- h.P., e.h.W., and Y.- J.t. data curation: Y.- c.t., c.- n.t., i.Y.- F.c., e.Y.- c.K., c.K., c.- S.l., n.- K.W., J.c., c.- Y.h., e.h.W., and Y.- J.t. Formal analysis: Y.- c.t., i.Y.- F.c., e.Y.- c.K., c.- l.t., c.K., c.- S.l., n.- K.W., S.h.t., J.c., W.- h.P., c.- Y.h., e.h.W., e.S., and Y.- J.t. Software: Y.- c.t., i.Y.- F.c., G.t., n.- K.W., c.- Y.h., and e.h.W. Resources: t.- d.l., c.- n.t., J.P., e.Y.- c.K., G.t., h.- c.h., c.- S.l., n.- K.W., and Y.- J.t. Funding acquisition: e.Y.- c.K., G.t., c.- S.l., and n.- K.W. Project administration: e.Y.- c.K., c.- S.l., n.- K.W., and S.h.t. Supervision: c.- n.t., i.Y.- F.c., e.Y.- c.K., c.- c.l., G.t., c.- S.l., n.- K.W., S.h.t., and O.S. Writing—original draft: Y.- c.t., e.Y.- c.K., c.- c.l., c.- S.l., n.- K.W., S.h.t., J.c., and e.h.W. Writing—review and editing: t.- d.l., c.- n.t., i.Y.- F.c., e.Y.- c.K., c.- c.l., G.t., c.- S.l., n.- K.W., S.h.t., J.c., l.S., c.- Y.h., e.h.W., Y.- J.t., and O.S. **Competing interests:** the authors declare that they have no competing interests. **Data, code, and materials availability:** snRnA- seq data have been deposited into the ncBi GeO repository (GSe292269, www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSe292269). All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. this study did not generate any new materials.
Submitted 27 March 2025 Accepted 13 January 2026 Published 18 February 2026 10.1126/sciadv.adx7815
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---
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---
## 目录（TOC）
- `Supplementary Materials for` — 行 18–19（part01）
- `Disrupted energy metabolism is associated with retinal ganglion cell degeneration in autosomal dominant optic atrophy` — 行 20–26（part01）
- `The PDF file includes:` — 行 27–29（part01）
- `Other Supplementary Material for this manuscript includes the following:` — 行 30–176（part01）
- `Captions for the Supplementary Excel Files. Pathway enrichment analyses for retinal cell types.` — 行 177–178（part01）
---
# Supplementary Materials for
## Disrupted energy metabolism is associated with retinal ganglion cell degeneration in autosomal dominant optic atrophy
Eugene Yu-Chuan Kang _et al._
Corresponding author: Chi-Neu Tsai, pink7@mail.cgu.edu.tw; Chyuan-Sheng Lin, csl5@cumc.columbia.edu; Nan-Kai Wang, wang.nankai@gmail.com
_Sci. Adv._ **12** , eadx7815 (2026) DOI: 10.1126/sciadv.adx7815
### The PDF file includes:
Figs. S1 to S9 Tables S1 to S4 Legends for supplementary Excel files
### Other Supplementary Material for this manuscript includes the following:
Supplementary Excel files
**Fig. S1. Hunched-back posture observed in** **_Opa1_****_V291D/+_** **mice.**
Representative photograph showing the characteristic hunched-back posture in _Opa1__V291D/+_ mice at 220 days.
**Fig. S2. Mitochondrial morphological and ultrastructural alterations in the optic nerve and retinal ganglion cells (RGCs) of** **_Opa1_****_V291D/+_** **mice.**
**(A)** Representative confocal images of mKate-labeled mitochondria across the prelaminar region, unmyelinated optic nerve head, and myelinated optic nerve in _wild-type (WT)_ and _Opa1__V291D/+_ mice. Quantitative analysis revealed significantly increased mitochondrial sphericity and reduced morphological variability in _Opa1__V291D/+_ mice across all regions. ( _n_ = 3–4 mice per group; ** _P_ < 0.01, *** _P_ < 0.001, **** _P_ < 0.0001).
**(B)** Transmission electron microscopy (TEM) images of RGC somata in the ganglion cell layer showing normal mitochondrial morphology in the _WT_ retina and fragmented, vacuolated mitochondria with loss of cristae (arrowheads) and accumulation of autophagosomes (arrows) in _Opa1__V291D/+_ retinas. N, nucleus.
**Fig. S3. Analysis of mitochondrial DNA (mtDNA) integrity and copy number in retinas of** **_Opa1_****_V291D/+_** **and** **_wild-type (WT)_ mice.**
( **A** ) Quantitative PCR analysis of mtDNA copy number revealed a significant increase in _Opa1__V291D/+_ retinas compared with _WT_ controls, suggesting enhanced mitochondrial fission and compensatory mitochondrial turnover. Data are presented as mean ± SEM ( _n_ = 3 mice per group); * _P_ < 0.05.
( **B** ) Assessment of mtDNA integrity using qPCR-based mtDNA damage assay and long-extension PCR showed no detectable differences in mtDNA deletions or damage between _Opa1__V291D/+_ and _WT_ retinas, indicating preserved mtDNA stability.
**Fig. S4. Western blot analysis of long (l-OPA1) and short (s-OPA1) isoforms in retinas of** **_Opa1_****_V291D/+_** **and** **_wild-type_ (** **_WT_ ) mice.**
Representative immunoblot and quantification showing significantly reduced levels of both l-OPA1 and s-OPA1 isoforms in _Opa1__V291D/+_ retinas compared with _WT_ controls ( _n_ = 6 mice per group). The reduction was more pronounced in the short (soluble) form. Data are presented as mean ± SEM. ** _P_ < 0.01, **** _P_ < 0.0001.
**Fig. S5. Differential expression of genes involved in metabolic and mitochondrial quality-control pathways in RGCs of** **_Opa1_****_V291D/+_** **retinas.** ( **A** ) Heat maps of the snRNA-seq data showing the expression levels of genes associated with pyruvate metabolism & the citric acid cycle, mitophagy, and autophagy pathways in the RGC-2 cluster from _Opa1__V291D/+_ mice compared with _wild-type_ ( _WT)_ controls at 360 days. ( _n_ = 5 mice per group; adjusted _P_ = 0.0288, 0.0075, and 0.0002, respectively; WikiPathways database)
( **B** ) Heat maps of the spatial transcriptomic analysis showing the expression levels of genes associated with mitophagy and autophagy pathways in RGCs of _Opa1__V291D/+_ mice compared with _WT_ at 280 days ( _n_ = 1 mouse retina per group; adjusted _P_ = 0.1542 and < 0.0001, respectively; REACTOME database)
**Fig. S6. Analysis of integrated stress response (ISR) in** **_V291D-VG2-MitoTag_ -** **_MitoLbNOX_ mice.** Representative immunostaining images of NRF2, eIF2α, phospho-eIF2α (p-eIF2α), and ATF4 in retinal sections from _wild-type_ ( _WT_ ), _V291D-VG2-MitoTag_ , and _V291D-VG2-MitoTag_ - _MitoLbNOX_ mice. Analysis of the fluorescence intensity of NRF2 ( _n_ = 4 mice per group; one‐way ANOVA with Tukey’s test _P_ = 0.0026, 0.7637, and 0.0072, for _WT_ compared to _V291D-VG2-MitoTag_ , _WT_ compared to _V291D-VG2-MitoTag_ - _MitoLbNOX_ , and _V291D-VG2-MitoTag_ compared to _V291D-VG2-MitoTag_ - _MitoLbNOX_ , respectively), eIF2α ( _P_ = 0.8994, 0.1517, and 0.2840), p-eIF2α ( _P_ = 0.3041, 0.6293, and 0.0766), and ATF4 ( _P_ = 0.1046, 0.2983, and 0.7557) immunostaining in the ganglion cell layer. Data are presented as mean ± SEM. ** _P_ < 0.01. ns: non-significant
**Fig. S7. Representative matrix-assisted laser desorption/ionization (MALDI) mass spectrometry images from** **_V291D-VG2-MitoTag_ and** **_V291D-VG2-MitoTag-MitoLbNOX_ mouse retinas.** Representative MALDI imaging results and corresponding bar charts demonstrating the ATP signal intensity in positive ion mode ( _n_ = 3 per group; independent _t_ -test _P_ = 0.0331), AMP signal intensity in negative ion mode ( _P_ = 0.2382), and G-6-P signal intensity in negative ion mode ( _P_ = 0.3115) in the inner retinal layer of mouse retinas at 100 days.
**Fig. S8. Assessment of Complex I subunit abundance and supercomplex assembly in** **_Opa1_****_V291D/+_** **retinas.**
( **A** ) SDS–PAGE immunoblotting of retinal lysates from _wild-type_ ( _WT)_ and _Opa1__V291D/+_ mice showing protein levels of the representative Complex I subunits NDUFS3 and NDUFA9. Quantification revealed no significant differences between genotypes, indicating preserved steady-state abundance of these subunits at the whole retina level.
( **B** ) Blue-native PAGE (BN-PAGE) followed by immunoblotting with antibodies against NDUFS3 and NDUFA9 to assess Complex I assembly and supercomplex formation. While the abundance of fully assembled Complex I did not differ significantly between _WT_ and _Opa1__V291D/+_ retinas, a trend toward reduced levels of Complex I–containing supercomplexes was observed in _Opa1__V291D/+_ samples with both antibodies (NDUFS3: _P_ = 0.0529; NDUFA9: _P_ = 0.0867). No abnormal or partially assembled Complex I subcomplexes were detected. ( _n_ = 4 mice per group)
**Fig. S9. Optical coherence tomography (OCT) imaging and retinal thickness analysis in mouse retinas.**
**(A)** Representative Bioptigen OCT images showing dynamic scan control (left), horizontal B-scan alignment (middle), and vertical B-scan alignment (right).
**(B)** Heat maps and thickness analyses of the total retina (top row) and retinal nerve fiber layer (RNFL; bottom row). For each layer, representative maps include the raw heat map, heat map showing values above two standard deviations, segmented vitreoretinal interface (VIP) image, and the corresponding average thickness map across the Early Treatment Diabetic Retinopathy Study (ETDRS) sectors.
**Table S1. Aggregated data on the pathogenicity of the** **_OPA1_****_V346D_** **variant.**
|**Evidence**|**Pathogenicity**|**Details**|
|---|---|---|
|In-silico Prediction|Pathogenic Strong|PP3,MetaRNN score: 0.975|
|Population Database|Pathogenic Moderate|PM2, the variant is absent from gnomAD databases|
|Protein Effect|Pathogenic Moderate|PM5, another variant at the same position, Val346Leu,is classified as likely pathogenic|
|Variant Location|Pathogenic Moderate|PM1; located in a hot-spot with 13 missense/in- frame variants,11 of which arepathogenic|
|Prediction Tools|||
|AlphaMissense|StrongPathogenic|0.9997|
|MutPred|StrongPathogenic|0.863|
|REVEL|StrongPathogenic|0.985|
|MetaLR|Moderatepathogenic|0.958|
|SIFT|Supporting pathogenic|0|
**Table S2. Primers used in the study.**
|**Oligonucleotides**|**Sequence (5’-3’)**|
|---|---|
|Genotyping||
|_Opa1_Forward|AGAGCTGAGAGGGAGTGAAGAGAGG|
|_Opa1_Reverse|CCCAAAACTCCTTTATCCCAGTGAC|
|Quantitative real-time|PCR|
|_Opa1_Forward|GGAAAGGAACACGACGACATA|
|_Opa1_Reverse|TCAAGCTATCCTCGGCAAAG|
|_Actb_Forward|GAGGTATCCTGACCCTGAAGTA|
|_Actb_Reverse|GCTCGA AGTCTAGAGCAACATAG|
|Long extension PCR|for mitochondrial DNA damage|
|_mtDNA_Forward|CATAGTGGGGTATCTAATCCCA|
|_mtDNA_Reverse|CCTACTAGCAATTATCCCCA|
**Table S3. Cell markers for cell type annotation in single-nucleus RNA sequencing**
|**Cell type **|**Genes**|
|---|---|
|Rodphotoreceptors|_Cnga1, Cngb1, Gnat1, Rho, Rp1, Sag, Ush2a_|
|Conephotoreceptors|_Arr3, Cngb3, Gnat2, Opn1mw_|
|Retinalganglion cells|_Rbpms, Slc17a6, Thy1 _|
|Amacrine cells|_Gad1, Gad2, Grm5, Slc6a5, Tfap2b_|
|Muller cells|_Rlbp1, Slc1a3_|
|Uveal cells|_Gpnmb, Tyr _|
|Bipolar cells|_Cabp5, Grm6, Kcnb2, Prkca_|
|Horizontal cells|_Lhx1, Onecut2, Prox1_|
|Pericytes|_Pdgfrb, Rgs5_|
**Table S4. Antibodies and materials used for Western blotting (WB) and Immunofluorescence (IF).**
|**Item**|**Dilution and** **Application**|**Source**|
|---|---|---|
|BRN3A|1:50(IF)|Millipore MAB1585|
|OPA1|1:1000(WB)|Proteintech 27733-1-AP|
|β-actin|1:2000(WB)|Proteintech HRP-60008|
|GAPDH|1:1000(WB)|Cell Signaling#2118|
|p-AMPKα|1:50(IF)|Cell Signaling#2535|
|PFKFB3|1:1000(WB)|Proteintech 13763-1-AP|
|p-PFKFB3|1:500(WB),1:50(IF)|ThermoFisher #PA5-114619|
|GLUT1|1:2000(WB), 1:2500(IF)|Proteintech 21829-1-AP|
|p-GLUT1|1:200(WB)|Millipore ABN991|
|Hexokinase 1 (HK1)|1:1000(WB), 1:800(IF)|Cell Signaling #2024|
|Hexokinase 2(HK2)|1:5000(WB)|Proteintech 22029-1-AP|
|LDHB|1:50(IF)|Proteintech 14824-1-AP|
|4-Hydroxynonenal antibody (4-HNE)|1:25(IF)|Abcam ab48506|
|PDHE1|1:50(IF)|Proteintech 18068-1-AP|
|IDH3|1:200(IF)|Proteintech 15909-1-AP|
|RBPMS|1:200(IF)|Millipore ABN1362|
|GFP|1:2000(IF)|Aveslabs GFP-1020|
|Hoechst Nucleic Acid|1:1000(IF)|Thermo Scientific 62249|
|Donkey anti-mouse HRP|1:5000(WB)|Invitrogen #A16017|
|Donkeyanti-rabbit HRP|1:5000(WB)|Invitrogen #31458|
|Cy™2 AffiniPure Donkey Anti-Mouse IgG(H+L)|1:200(IF)|Jackson ImmunoResearch 715-225-151|
|Cy™3 AffiniPure Donkey Anti-Mouse IgG(H+L)|1:200(IF)|Jackson ImmunoResearch 715-165-151|
|Cy™2 AffiniPure Donkey Anti-Rabbit IgG(H+L)|1:200(IF)|Jackson ImmunoResearch 711-225-152|
|Cy™3 AffiniPure Donkey Anti-Rabbit IgG(H+L)|1:200(IF)|Jackson ImmunoResearch 711-165-152|
|Cy™2 AffiniPure™|1:200(IF)|Jackson ImmunoResearch 703-545-155|
|Donkey Anti-Chicken IgG (H+L)|||
|---|---|---|
|NuPAGE™ MOPS SDS|- Invitrogen|NP0001|
|RunningBuffer|||
|Bolt™ MES SDS Running|- Invitrogen|B000202|
|Buffer|||
|Novex™ Tris-Glycine SDS RunningBuffer|- Invitrogen|LC2675|
|Novex™ Tris-Glycine SDS Sample Buffer|- Invitrogen|LC2676|
|Bolt™ LDS Sample Buffer|- Invitrogen|B0007|
|NuPAGE™ Sample|- Invitrogen|NP0004|
|ReducingAgent|||
|Bolt™ Sample Reducing|- Invitrogen|B0009|
|Agent|||
|Novex™ Tris-Glycine|- Invitrogen|XP00100PK2|
|Mini Protein Gels, 10%, 1.0 mm|||
|Bolt™ Bis-Tris Plus Mini|- Invitrogen|NW00122BOX|
|Protein Gels,12%,1.0 mm|||
|Bolt™ Bis-Tris Plus Mini Protein Gels, 4-12%, 1.0|- Invitrogen|NW04122BOX|
|mm|||
|SuperKine™ Enhanced AntibodyDilution Buffer|- Abbkine|BMU103-EN|
|SuperKine™ West Femto|- Abbkine|BMU102-EN|
|Maximum Sensitivity|||
|Substrate|||
|SuperBlock (TBS)|- Thermo Sci|entific 37535|
|BlockingBuffer|||
|EveryBlot BlockingBuffer|- Bio-Rad|#12010020|
|PageRuler™ Plus|- Thermo Sci|entific 26619|
|Prestained Protein Ladder, 10 to 250 kDa|||
### Captions for the Supplementary Excel Files. Pathway enrichment analyses for retinal cell types.
The archive contains Gene Ontology (GO), KEGG, Reactome, and WikiPathways enrichment analysis results derived from differential gene expression analyses of individual retinal cell types, including cone photoreceptors, rod photoreceptors, retinal ganglion cell cluster 1 (RGC-1), and retinal ganglion cell cluster 2 (RGC-2). Each Excel file lists pathways with corresponding gene sets, enrichment statistics, and adjusted _P_ values.
script/01_explore_raw.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
01_explore_raw.py — GSE292269 原始矩阵初步探索
- 加载 3 个样本的 raw feature-barcode 矩阵
- 计算每个 barcode 的 UMI/基因数分布，绘制 knee plot 辅助确定空液滴过滤阈值
- 输出：output/01_qc/*.png（300 ppi）+ 汇总打印
"""
import gzip
import numpy as np
import pandas as pd
import scanpy as sc
import scipy.io as sio
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from pathlib import Path
DATA = Path("data/GSE292269")
OUT = Path("output/01_qc")
OUT.mkdir(parents=True, exist_ok=True)
SAMPLES = ["WT", "Opa1V291D_S1", "Opa1V291D_S2"]
plt.rcParams.update({"figure.dpi": 300, "savefig.dpi": 300, "font.size": 9})
def load_raw(sample: str) -> sc.AnnData:
d = DATA / sample
with gzip.open(d / "matrix.mtx.gz", "rb") as f:
X = sio.mmread(f).tocsr().T # barcodes x genes
bc = pd.read_csv(d / "barcodes.tsv.gz", header=None, sep="\t")[0].astype(str).values
feat = pd.read_csv(d / "features.tsv.gz", header=None, sep="\t")
adata = sc.AnnData(X=X)
adata.obs_names = bc
adata.var["gene_ids"] = feat[0].values
adata.var_names = feat[1].astype(str).values
adata.var_names_make_unique()
return adata
summary = []
fig, axes = plt.subplots(1, 3, figsize=(11, 3.2), sharey=False)
for ax, s in zip(axes, SAMPLES):
adata = load_raw(s)
umi = np.asarray(adata.X.sum(axis=1)).ravel()
n_genes = np.asarray((adata.X > 0).sum(axis=1)).ravel()
# knee plot：UMI 降序，log-log
umi_sorted = np.sort(umi)[::-1]
ax.plot(np.arange(1, len(umi_sorted) + 1), umi_sorted, lw=0.7)
ax.set_xscale("log"); ax.set_yscale("log")
ax.set_xlabel("Barcode rank"); ax.set_ylabel("UMI counts")
ax.set_title(s)
for thr, c in [(100, "grey"), (500, "orange"), (1000, "red")]:
n_pass = int((umi >= thr).sum())
ax.axhline(thr, ls="--", lw=0.6, color=c)
ax.text(5, thr * 1.15, f"≥{thr}: {n_pass:,}", fontsize=7, color=c)
for thr in (100, 500, 1000):
m = umi >= thr
summary.append({
"sample": s, "umi_threshold": thr,
"n_barcodes": int(m.sum()),
"median_umi": float(np.median(umi[m])) if m.any() else np.nan,
"median_genes": float(np.median(n_genes[m])) if m.any() else np.nan,
"total_genes_detected": int((np.asarray(adata.X[m].sum(axis=0)).ravel() > 0).sum()) if m.any() else 0,
})
print(f"[{s}] raw barcodes={adata.n_obs:,}, genes={adata.n_vars:,}, nnz={adata.X.nnz:,}")
fig.suptitle("Knee plots — GSE292269 raw matrices")
fig.tight_layout()
fig.savefig(OUT / "knee_plots.png")
plt.close(fig)
df = pd.DataFrame(summary)
df.to_csv(OUT / "barcode_threshold_summary.csv", index=False)
print("\n", df.to_string(index=False))
script/02_qc_filter.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
02_qc_filter.py — GSE292269 质控与过滤（第一轮 P0, Step 1）
策略（决策记录 #2）：简单阈值法
- 每样本：UMI ≥ 500、检出基因 ≥ 300、mt% < min(10%, median + 3*MAD)
- scrublet 去双联体（expected_doublet_rate=0.06）
- 基因过滤：至少在 10 个核中检出
产出：
- data/01_filtered.h5ad（合并三样本，含 QC 注释）
- output/02_qc/*.png（300 ppi）、qc_summary.csv
"""
import gzip
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc
import scipy.io as sio
import scrublet as scr
DATA = Path("data/GSE292269")
OUT = Path("output/02_qc")
OUT.mkdir(parents=True, exist_ok=True)
SAMPLES = {"WT": "WT", "Opa1V291D_S1": "V291D", "Opa1V291D_S2": "V291D"}
plt.rcParams.update({"figure.dpi": 300, "savefig.dpi": 300, "font.size": 9})
sc.settings.set_figure_params(dpi=300, dpi_save=300)
def load_raw(sample: str) -> sc.AnnData:
d = DATA / sample
with gzip.open(d / "matrix.mtx.gz", "rb") as f:
X = sio.mmread(f).tocsr().T
feat = pd.read_csv(d / "features.tsv.gz", header=None, sep="\t")
bc = pd.read_csv(d / "barcodes.tsv.gz", header=None, sep="\t")[0].astype(str)
adata = sc.AnnData(X=X)
adata.obs_names = (sample + "_" + bc).values
adata.var["gene_ids"] = feat[0].values
adata.var_names = pd.Index(feat[1].astype(str).values)
adata.var_names_make_unique()
return adata
adatas = []
summary = []
for sample, genotype in SAMPLES.items():
print(f"\n===== {sample} =====")
adata = load_raw(sample)
adata.obs["sample"] = sample
adata.obs["genotype"] = genotype
# QC 指标
adata.var["mt"] = adata.var_names.str.startswith("mt-")
sc.pp.calculate_qc_metrics(adata, qc_vars=["mt"], inplace=True, log1p=False)
n0 = adata.n_obs
# 阈值
mt_frac = adata.obs["pct_counts_mt"] / 100.0
mad = np.median(np.abs(mt_frac - mt_frac.median())) * 1.4826
mt_thr = min(10.0, (mt_frac.median() + 3 * mad) * 100)
mt_thr = max(mt_thr, 3.0) # 保底 3%
keep = (
(adata.obs["total_counts"] >= 500)
& (adata.obs["n_genes_by_counts"] >= 300)
& (adata.obs["pct_counts_mt"] < mt_thr)
)
print(f"阈值: UMI>=500, genes>=300, mt%<{mt_thr:.1f} -> {keep.sum():,}/{n0:,}")
adata = adata[keep].copy()
n1 = adata.n_obs
# scrublet 双联体
scrub = scr.Scrublet(adata.X, expected_doublet_rate=0.06)
scores, pred = scrub.scrub_doublets(verbose=False)
adata.obs["doublet_score"] = scores
adata.obs["predicted_doublet"] = pred
print(f"scrublet: 检出双联体 {pred.sum():,} ({pred.mean()*100:.1f}%)")
adata = adata[~adata.obs["predicted_doublet"].values].copy()
n2 = adata.n_obs
summary.append({
"sample": sample, "genotype": genotype, "raw_barcodes": n0,
"after_threshold": n1, "after_doublet": n2,
"mt_threshold_pct": round(mt_thr, 2),
"median_umi": float(adata.obs["total_counts"].median()),
"median_genes": float(adata.obs["n_genes_by_counts"].median()),
"median_mt_pct": round(float(adata.obs["pct_counts_mt"].median()), 2),
})
adatas.append(adata)
# 合并 + 基因过滤
adata = sc.concat(adatas, join="outer", fill_value=0)
sc.pp.filter_genes(adata, min_cells=10)
print(f"\n合并后: {adata.n_obs:,} 核 × {adata.n_vars:,} 基因")
adata.write_h5ad("data/01_filtered.h5ad")
pd.DataFrame(summary).to_csv(OUT / "qc_summary.csv", index=False)
print(pd.DataFrame(summary).to_string(index=False))
# ---- QC 图 ----
adata.obs["genotype"] = pd.Categorical(adata.obs["genotype"], categories=["WT", "V291D"])
fig, axes = plt.subplots(1, 3, figsize=(10, 3))
for ax, key, lab in zip(
axes,
["total_counts", "n_genes_by_counts", "pct_counts_mt"],
["Total UMI", "Detected genes", "mt%"],
):
for i, s in enumerate(SAMPLES):
v = adata.obs.loc[adata.obs["sample"] == s, key]
parts = ax.violinplot([np.log10(v + 1) if key != "pct_counts_mt" else v], positions=[i], widths=0.7)
for b in parts["bodies"]:
b.set_alpha(0.7)
ax.set_xticks(range(len(SAMPLES)), list(SAMPLES), rotation=20)
ax.set_title(lab)
fig.suptitle("Post-filter QC metrics")
fig.tight_layout()
fig.savefig(OUT / "qc_violins_postfilter.png")
plt.close(fig)
print("\nDone -> data/01_filtered.h5ad, output/02_qc/")
script/03_integrate_cluster.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
03_integrate_cluster.py — 归一化、Harmony 整合、聚类（第一轮 P0, Step 2）
决策记录 #4：Harmony（按 sample）仅用于聚类/注释；定量比较后续用原始归一化表达。
产出：data/02_clustered.h5ad；output/03_cluster/*.png
"""
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import scanpy as sc
OUT = Path("output/03_cluster")
OUT.mkdir(parents=True, exist_ok=True)
sc.settings.figdir = OUT
plt.rcParams.update({"figure.dpi": 300, "savefig.dpi": 300, "font.size": 9})
adata = sc.read_h5ad("data/01_filtered.h5ad")
print(f"载入: {adata.n_obs:,} 核 × {adata.n_vars:,} 基因")
adata.layers["counts"] = adata.X.copy()
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
adata.raw = adata # 保留归一化表达供下游定量
sc.pp.highly_variable_genes(adata, n_top_genes=3000, flavor="seurat_v3", layer="counts")
print(f"HVG: {adata.var['highly_variable'].sum()}")
sc.pp.scale(adata, max_value=10)
sc.pp.pca(adata, n_comps=50, mask_var="highly_variable")
# Harmony 按 sample 整合（harmonypy 2.x 返回 (n_cells, n_pcs)，scanpy 封装不兼容，直接调用）
import harmonypy as hm
_ho = hm.run_harmony(adata.obsm["X_pca"], adata.obs, "sample", verbose=False)
adata.obsm["X_pca_harmony"] = _ho.Z_corr
sc.pp.neighbors(adata, use_rep="X_pca_harmony", n_neighbors=15)
for res in (0.4, 0.8, 1.2):
sc.tl.leiden(adata, resolution=res, key_added=f"leiden_{res}", flavor="igraph", n_iterations=2)
print(f"leiden_{res}: {adata.obs[f'leiden_{res}'].nunique()} clusters")
sc.tl.umap(adata)
adata.write_h5ad("data/02_clustered.h5ad")
# UMAP 图
for key in ["sample", "genotype", "leiden_0.4", "leiden_0.8", "leiden_1.2"]:
fig = sc.pl.umap(adata, color=key, show=False, return_fig=True, size=3,
title=f"UMAP — {key}")
fig.savefig(OUT / f"umap_{key.replace('.', 'p')}.png", bbox_inches="tight")
plt.close(fig)
print("Done -> data/02_clustered.h5ad, output/03_cluster/")
script/04_annotate.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
04_annotate.py — 细胞类型注释 + microglia 捕捞 + RGC 亚群（第一轮 P0, Step 3）
- 基准 marker：原文表 S3（9 类）+ 补充 microglia/astrocyte/血管/少突 marker
- 按 leiden_0.8 cluster 打分注释（z-scored mean expression）
- microglia：多 marker 共表达门控（Aif1/C1qa/Tmem119/P2ry12/Hexb）
- RGC 亚群重聚类，复现 RGC-1/RGC-2
产出：data/03_annotated.h5ad；output/04_annotation/
"""
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc
OUT = Path("output/04_annotation")
OUT.mkdir(parents=True, exist_ok=True)
plt.rcParams.update({"figure.dpi": 300, "savefig.dpi": 300, "font.size": 9})
MARKERS = {
"Rod": ["Rho", "Gnat1", "Sag", "Cnga1"],
"Cone": ["Arr3", "Gnat2", "Opn1mw"],
"Bipolar": ["Grm6", "Prkca", "Cabp5", "Vsx2"],
"Horizontal": ["Lhx1", "Onecut2", "Prox1"],
"Amacrine": ["Gad1", "Gad2", "Tfap2b"],
"RGC": ["Rbpms", "Slc17a6", "Thy1"],
"Muller": ["Rlbp1", "Slc1a3", "Glul"],
"Microglia": ["Aif1", "C1qa", "C1qb", "Tmem119", "Hexb"],
"Astrocyte": ["Gfap", "S100b", "Aqp4"],
"Pericyte": ["Pdgfrb", "Rgs5"],
"Endothelial": ["Pecam1", "Cldn5", "Kdr"],
"Uveal_Melanocyte": ["Gpnmb", "Tyr", "Mlana"],
"Oligodendrocyte": ["Mog", "Plp1", "Mbp"],
}
adata = sc.read_h5ad("data/02_clustered.h5ad")
print(f"载入: {adata.n_obs:,} 核")
CLU = "leiden_0.8"
# ---------- 1. cluster 级 marker 打分 ----------
genes_all = [g for gs in MARKERS.values() for g in gs if g in adata.raw.var_names]
# 用 raw（log-normalized）按 cluster 求均值（one-hot × X 手动聚合，稳健）
import scipy.sparse as sp
raw_ad = adata.raw.to_adata()
cats = pd.Categorical(adata.obs[CLU].astype(str))
onehot = sp.csr_matrix(
(np.ones(len(cats)), (cats.codes, np.arange(len(cats)))),
shape=(len(cats.categories), len(cats)),
)
sums = onehot @ raw_ad.X
if sp.issparse(sums):
sums = sums.toarray()
counts = np.bincount(cats.codes, minlength=len(cats.categories))
X_agg = sums / counts[:, None]
df = pd.DataFrame(X_agg, index=cats.categories.astype(str), columns=raw_ad.var_names)
df = df[[g for g in genes_all if g in df.columns]]
z = (df - df.mean()) / df.std(ddof=0)
z = z.fillna(0.0)
ct_score = {}
for ct, gs in MARKERS.items():
gs_ok = [g for g in gs if g in z.columns]
ct_score[ct] = z[gs_ok].mean(axis=1)
score_df = pd.DataFrame(ct_score).astype(float).fillna(0.0)
score_df.to_csv(OUT / "cluster_marker_scores.csv")
assign = score_df.idxmax(axis=1)
margin = score_df.apply(lambda r: r.nlargest(2).iloc[0] - r.nlargest(2).iloc[1], axis=1)
assign_tbl = pd.DataFrame({
"cluster": score_df.index, "assigned": assign, "margin": margin,
"n_cells": adata.obs[CLU].value_counts(),
"top3": score_df.apply(lambda r: ", ".join(f"{k}:{v:.2f}" for k, v in r.nlargest(3).items()), axis=1),
})
assign_tbl.to_csv(OUT / "cluster_assignment_auto.csv", index=False)
print(assign_tbl.sort_values("assigned").to_string(index=False))
# ---------- 2. marker dotplot（证据图） ----------
order = assign_tbl.sort_values(["assigned", "cluster"])["cluster"].tolist()
fig = sc.pl.dotplot(adata, var_names={k: [g for g in v if g in adata.raw.var_names] for k, v in MARKERS.items()},
groupby=CLU, categories_order=order, use_raw=True,
show=False, return_fig=True)
fig.savefig(OUT / "marker_dotplot.png", bbox_inches="tight")
plt.close("all")
# ---------- 3. 应用注释（margin < 0.5 判为 LowConf，低信度 cluster 不进定量比较） ----------
mapping = assign.to_dict()
adata.obs["cell_type"] = adata.obs[CLU].map(mapping).astype(str)
lowconf = margin[margin < 0.5].index.astype(str)
adata.obs.loc[adata.obs[CLU].astype(str).isin(lowconf), "cell_type"] = "LowConf"
adata.obs["cell_type"] = pd.Categorical(adata.obs["cell_type"])
print(f"\n低信度 cluster（margin<0.5）: {sorted(lowconf)} -> 标记 LowConf")
print("\n细胞类型分布:")
print(adata.obs.groupby(["cell_type", "genotype"], observed=True).size().unstack(fill_value=0))
# ---------- 4. microglia 捕捞核查 ----------
adata_raw = adata.raw.to_adata()
adata_raw.obs = adata.obs
mg_markers = [g for g in ["Aif1", "C1qa", "C1qb", "Tmem119", "Hexb", "P2ry12", "Cx3cr1"] if g in adata_raw.var_names]
mg_expr = sc.get.obs_df(adata_raw, keys=mg_markers)
mg_hits = (mg_expr > 0).sum(axis=1)
adata.obs["mg_marker_hits"] = mg_hits
cand = adata.obs[mg_hits >= 3]
print(f"\nmicroglia 候选（≥3 marker 共表达）: {len(cand)} 核")
print(cand.groupby(["cell_type", "genotype"], observed=True).size().unstack(fill_value=0))
if len(cand):
adata.obs["is_microglia_candidate"] = adata.obs.index.isin(cand.index)
# ---------- 5. RGC 亚群重聚类 ----------
rgc = adata[adata.obs["cell_type"] == "RGC"].copy()
print(f"\nRGC 亚群重聚类: {rgc.n_obs:,} 核")
if rgc.n_obs > 200:
rgc.X = rgc.layers["counts"].copy()
sc.pp.normalize_total(rgc, target_sum=1e4)
sc.pp.log1p(rgc)
sc.pp.highly_variable_genes(rgc, n_top_genes=2000, flavor="seurat_v3", layer="counts")
sc.pp.scale(rgc, max_value=10)
sc.pp.pca(rgc, n_comps=30, mask_var="highly_variable")
import harmonypy as hm
_ho = hm.run_harmony(rgc.obsm["X_pca"], rgc.obs, "sample", verbose=False)
rgc.obsm["X_pca_harmony"] = _ho.Z_corr
sc.pp.neighbors(rgc, use_rep="X_pca_harmony")
sc.tl.leiden(rgc, resolution=0.2, key_added="rgc_sub", flavor="igraph", n_iterations=2)
sc.tl.umap(rgc)
print(rgc.obs.groupby(["rgc_sub", "genotype"], observed=True).size().unstack(fill_value=0))
# OXPHOS/ETC 模块分（WT 中能量需求最高者对应原文 RGC-2）
etc_genes = [g for g in rgc.var_names if g.startswith(("mt-Nd", "mt-Co", "mt-Atp", "mt-Cytb", "Nduf", "Cox", "Atp5", "Uqcr", "Sdh"))]
sc.tl.score_genes(rgc, gene_list=etc_genes, score_name="ETC_score", use_raw=False)
wt_score = rgc.obs[rgc.obs["genotype"] == "WT"].groupby("rgc_sub", observed=True)["ETC_score"].mean().sort_values(ascending=False)
print("\nWT 中各 RGC 亚群 ETC 模块分（高者 ≈ 原文 RGC-2）:")
print(wt_score)
fig = sc.pl.umap(rgc, color=["rgc_sub", "genotype", "ETC_score"], show=False, return_fig=True, size=8)
fig.savefig(OUT / "rgc_subcluster_umap.png", bbox_inches="tight")
plt.close("all")
# 写回主对象
adata.obs["rgc_sub"] = np.nan
adata.obs.loc[rgc.obs_names, "rgc_sub"] = rgc.obs["rgc_sub"].astype(str)
adata.obs.loc[rgc.obs_names, "ETC_score_rgc"] = rgc.obs["ETC_score"]
rgc.write_h5ad("data/03b_rgc_subset.h5ad")
adata.write_h5ad("data/03_annotated.h5ad")
print("\nDone -> data/03_annotated.h5ad, output/04_annotation/")

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