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0000000000000000000000000000000000000000..c51e02a87d5c09d850da351964e876a0f96bf4b1 --- /dev/null +++ b/AI_powered_CCRM_liver.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e4aa301d634adf61a0af99689c444783c18a0994020faac05fe25ce07f034b90 +size 10512624 diff --git a/AI_powered_CCRM_liver__reading_notes.md b/AI_powered_CCRM_liver__reading_notes.md new file mode 100644 index 0000000000000000000000000000000000000000..87c42c66d0839ee6ebc1009075978bf4d585f6bd --- /dev/null +++ b/AI_powered_CCRM_liver__reading_notes.md @@ -0,0 +1,180 @@ +# 阅读笔记 · AI_powered_CCRM_liver.pdf + +**生成日期**：2026-05-15 +**对象文件**：本目录下的 `AI_powered_CCRM_liver.pdf`（10.5 MB / 21 页） +**目的**：作为 Liver_References 数据集的"理论锚点"——这篇论文不直接消费这 17 份数据，但它提出的框架决定了这批数据"为什么有用、未来怎么用"。 + +--- + +## 一、论文身份与定位 + +**标题**：*AI-powered critical care regenerative hepatology: adaptive repair for liver cancer and liver diseases* + +**作者**：Xingcai Zhang¹·² · Frank Fu¹ · Isa Guo¹ · Richi Chen¹ · Aseel Malallah¹ · Natalie Torok² +（¹UCSD 化工与纳米工程系 · ²Stanford 医学院） + +**通讯作者**：Zhang (xiz292@ucsd.edu) · Torok (ntorok@stanford.edu) + +**论文类型**：**Perspective（视角/综述型论文）**——这是关键定性。Perspective 的写作惯例是综合现有文献提出一个新的组织框架，不报告原始实验数据，不做新的统计分析。论文末尾 `Data and code availability` 段明确写："No new datasets or custom code were generated in this Perspective." 这一句话直接决定了**论文与你 Liver_References 数据的关系**：数据不是论文的输入，而是论文框架未来操作化时的潜在底座。 + +**所属上位论文**：本文是 **"AI-CCRM"（AI-powered Critical Care Regenerative Medicine）系列的肝脏专属扩展**。母系列由 Zhang 等 2026 年在 *Science* 发表（参考文献 #1），把"AI 驱动的再生重症医学"作为通用框架。本文则把这个框架专门套到肝脏疾病上——这是一个**单器官化的延伸论文**。 + +--- + +## 二、核心主张 + +论文的中心论点用一句话讲：**严重肝病不应被当作静态诊断标签（MASH/ALD/ACLF/HCC/CCA），而应作为"再生 vs 错配修复 vs 免疫紊乱 vs 恶性可塑性"四种力量的动态竞争来处理；AI 是把多模态数据整合成一个状态判断引擎的工具**。 + +这个论点的几个支撑层次： + +第一，**肝脏的悖论**——肝脏是哺乳动物里再生能力最强的器官（部分肝切除后剩余肝细胞能快速增殖恢复体积），但同样的可塑性也支持纤维化、肝硬化、HCC、CCA、肿瘤复发。**同一组生物学机制既是治愈的可能性，也是恶化的根源**——治疗设计的难点就在这里。 + +第二，**当前医疗的碎片化**——慢性肝病在门诊管理、失代偿在 ER/ICU 管理、肝癌在肿瘤科治疗、终末期等器官移植，"四套医生四套语言"，但**生物学层面这些状态是同一连续体**。论文主张 AI 是把这四套打通的胶水。 + +第三，**新的临床决策语法**——不再问"这个患者是 MASH 还是 ACLF 还是 HCC"，而是问五个问题：(a) 现在活跃的修复状态是哪种？(b) 这个状态可逆吗？(c) 它倾向于纤维化还是癌变？(d) 什么干预能把它推回去？(e) 治疗过程中状态变了，要怎么 adapt？ + +--- + +## 三、最有"齿"的设计：八个 AI-defined 肝修复状态 + +整篇 Perspective 最具体、最可操作的部分是**这八个修复状态的本体（ontology）**。它不是诊断、不是疾病分类，而是一组**正交于诊断标签的状态向量**，理论上一个患者可以同时处于多个状态。 + +| # | 修复状态 | 关键信号 | 典型临床情境 | 候选 AI 引导干预 | +|---|---|---|---|---| +| 1 | 急性炎症损伤 | DAMPs · 细胞因子 · ALT/AST 升高 · 线粒体应激 | ALF · 酒精性肝炎 · ACLF · 脓毒症肝损伤 | 炎症控制 · 线粒体保护 · 人工肝支持 · 决定再生治疗时机 | +| 2 | 再生允许损伤 | IL-6–STAT3 · HGF · EGF · 肝细胞增殖标志 | 肝切除后 · 早期 ALF · 早期 ACLF | MSC/EV · mRNA 营养因子 | +| 3 | 内皮 capillarization 主导损伤 | LSEC 去窗孔 · 内皮去分化 | 早中期纤维化 · 门脉高压前期 | LSEC-靶向 miRNA 球（参考文献 #8 Liu 2025） | +| 4 | 纤维化主导 maladaptive 修复 | 肝星状细胞激活 · 基质沉积 · 门脉高压 | 进展期纤维化 · 代偿期肝硬化 | 抗纤维化组合疗法 · senolytic | +| 5 | 免疫麻痹 / 耗竭 | CD8 衰竭 · IL-10 主导 · 感染易感 | ACLF · 失代偿期肝硬化 | 免疫调节 · 感染控制 · MSC/EV | +| 6 | 肿瘤–再生冲突 | 再生信号同时支持 HCC/CCA 生长 | HCC 在肝硬化背景 · 切除后复发 | "Cancer-safe" 再生策略 · 选择性 ICI | +| 7 | 移植桥接状态 | 多器官衰竭 · native 恢复不确定 | ACLF grade 2/3 · 严重 ALF | 人工肝支持 · 优先移植 | +| 8 | 治疗后重建 | 残余肝功能 · 复发风险评估 | 切除后 · 消融后 · 移植后 · ICU 出院后 | 监测 · 抗复发 · 抗纤维化预防 | + +这套本体的好处是**可被算法学习**——给一个患者多模态数据，模型可以输出"在八个状态上各占多少比例"的概率向量，而不是非黑即白的诊断。但它的**风险**是过于抽象、临床医生可能拒绝接受；论文也承认这个 ontology 是"提议"（proposed），尚未经过同行验证或前瞻性使用。 + +--- + +## 四、技术栈五层（论文给出的工程蓝图） + +论文把 AI 驱动的再生肝病学拆成五个工程层，每一层都标了代表性引用作"已有零件库"： + +**第一层 · 生物材料**——空间修复架构。Nature-inspired 微图案、骨再生材料、黑磷凝胶、AI 辅助抗菌支架、生物来源材料。用于工程化肿瘤旁基质、抗纤维化深部、胆管修复支架、类器官培养系统、体外灌注装置（参考文献 #11–16）。 + +**第二层 · 治疗工程**——AI 驱动的纳米医学。**这一层提到 "bioorthogonal virus immuno-nanomedicine"——这就是 BOVIN（你 bovin-pathway-demo 项目）的指代**。同层还有热免疫纳米医学、巨噬细胞靶向促消解纳米药、跨血管纳米载体（参考文献 #9, #17–21）。 + +**第三层 · 计算与数字孪生**——基于现有 ICU 数据、肝病生物标志、影像、数字病理、组学、肠道菌群、EV cargo、设备衍生信号构建患者级"肝脏数字孪生"，模拟竞争性未来（native 恢复 vs 进展到 ACLF vs 转 HCC vs 抗纤维化响应 vs 切除后复发 vs 移植获益 / 无效）。 + +**第四层 · 传感**——多模态采集。包括 ICU 实时生理、可穿戴、可植入或可吞咽的生物电子、微流控床旁检测、AI 终端 chip。让数字孪生从"每周更新"变成"分钟级更新"，对 ACLF / 脓毒症肝损伤 / 移植后不稳定特别关键（参考文献 #27–34）。 + +**第五层 · 决策支持与闭环**——患者特异性类器官 + 多种治疗候选并行测试 + AI 量化形态/动力学/组学响应 + 选出"既能修复健康肝 + 又能抑制肿瘤 + 又能减少纤维化 + 又能保护免疫 + 又低毒"的方案。论文称之为 **"Cancer-safe liver regeneration testing architecture"**（Figure 3）。 + +--- + +## 五、Cancer-safe 再生——论文最有原创性的概念 + +这是整篇 Perspective **最具原创性、最值得记住**的概念。传统再生医学的核心命题是"促进再生"，几乎不考虑癌变风险。但**肝脏再生的分子通路（Wnt、Notch、TGF-β、HGF、IL-6/STAT3、肝星状细胞激活）与 HCC 致癌通路高度重叠**——盲目促再生很可能同时促癌。 + +论文提议的解法是 **"配对类器官平台"**： + +> 同一患者来源 → 提取四种东西平行培养： +> +> (a) 非恶性肝实质类器官（正常肝细胞 + 胆管细胞） +> (b) 肿瘤类器官（如果患者有 HCC/CCA） +> (c) 免疫细胞 + 肝星状细胞 + 内皮细胞共培养 +> (d) 患者来源胞外基质重建 +> +> 把候选治疗丢到这四个系统上同时跑，AI 评估**五个输出**： +> +> 1. 健康肝组织修复程度 +> 2. 肿瘤生长抑制程度 +> 3. 纤维化程度 +> 4. 免疫功能保留/恢复程度 +> 5. 系统毒性 + +**只有在五个维度都通过的治疗才进入下一步临床**——这是"cancer-safe"的工程化定义。论文承认这套系统在 2026 年还是 aspirational（远景目标），但提出了具体的实施清单。 + +这个概念对 BOVIN 项目的直接含义：BOVIN 的 11 模块（M1–M11）输出的不应该只是"ICI 响应预测"，未来还应该输出"修复 vs 致癌"的双向风险评估——这是 BOVIN Aim 3（从预测到处方）的潜在主轴。 + +--- + +## 六、与你 Liver_References 17 份数据的对应关系 + +这是这份阅读笔记最核心的章节——论文虽然不直接用这 17 份数据，但**论文的框架决定了为什么收集这批数据**。 + +| 论文中的需求 | Liver_References 提供了什么 | 缺什么 | +|---|---|---| +| 修复状态 3（LSEC capillarization 主导） | `sc__healthy__endothelial__9422cx32596g__a95e1659...h5ad` 含 9,422 个健康肝内皮细胞，是定义"健康 LSEC 表型"的基线 | 需要纤维化/肝硬化患者的 LSEC 数据，目前只有 PSC/PBC 的 sn 全细胞图谱包含少量 | +| 修复状态 4（纤维化主导） | `sc__healthy__stellate__1417cx32596g__08a9f031...h5ad` 含 1,417 个健康肝星状细胞，是"静默状态"基线 | 需要"激活态肝星状细胞"数据——本 collection 无独立切片 | +| 修复状态 1+2（炎症/再生允许） | `sc__healthy__hepatocyte-v1__53015cx32596g__63137dcf...h5ad` 5.3 万健康肝细胞（含 zonation 三亚型） | 需要损伤/再生状态肝细胞——本 collection 无 | +| 修复状态 6（肿瘤-再生冲突） | **无** —— 本 collection 不含 HCC 肿瘤数据 | 需要 HCC 肿瘤 scRNA（如 Ma 2021 GSE149614），登记在 bovin-bench/hcc/CATALOG.md FOUND-05 | +| 论文提到的胆管细胞修复 | `sc__healthy__cholangiocyte__1011cx32596g__601ef580...h5ad` 含 1,011 健康胆管细胞 | PSC 患者的病变胆管细胞数据散落在 sn 全细胞图谱中 | +| PSC 病变直接对照 | `sn__psc-pbc-healthy__all-cells__105780cx32596g__4b5895d7...h5ad` 含 47,156 PSC 细胞 vs 26,515 健康，可直接做病例-对照 | 缺 sc（cell）版本的 PSC 配对（只有 sn） | +| 论文提到的空间修复（biomaterial 层） | 7 份 Visium 文件提供空间锚定——3 切片 C73 健康 + 4 切片 PSC011 | Visium 仅来自 2 个供体，统计自由度极低；C73 与 PSC011 不是同一组人，跨患者对比有限 | + +**核心观察**：你这 17 份数据**正好覆盖了论文"健康基线"的所有 8 种关键细胞类型 + 提供了一个"慢性胆汁淤积病"对照（PSC/PBC）**。从论文框架角度看，这批数据是 **"修复状态 3 (内皮) + 修复状态 4 (纤维化) 的 baseline 端 + 修复状态 5 (免疫) 的健康端"** 的最佳现成资源。要把论文框架全部跑通，还需要补的是： + +> 一，**HCC / CCA 肿瘤 scRNA 数据**——用于建模修复状态 6（肿瘤-再生冲突）。bovin-bench/hcc/CATALOG.md 已经列出（FOUND-05 / FOUND-06），但数据尚未下载到 database_unified/。 +> +> 二，**急性损伤期肝脏数据**（ACLF / ALF / 酒精性肝炎）——用于建模修复状态 1 + 2。这类数据较稀缺，多在 EGA / dbGaP 受控访问。 +> +> 三，**激活态肝星状细胞 / capillarized LSEC 数据**——用于建模修复状态 3 + 4 的"病态端"。可能要从其他 scRNA paper 单独采集。 +> +> 四，**纵向 / 系列样本**——患者治疗前后配对。是论文中提到的"修复状态向量随时间演变"的必要条件，目前 collection 全是横截面。 + +--- + +## 七、与 BOVIN 项目的关系 + +BOVIN（**B**ioorthogonal **O**ptimized **V**irus **I**mmuno-**N**anomedicine）在这篇 Perspective 里**被点名提到**——参考文献 #19 / #21 区段，"bioorthogonal virus immuno-nanomedicine" 作为"治疗工程层"的代表性技术之一。论文的语气是**把 BOVIN 当作一个具体技术示例**放进更宏大的肝病学 AI-CCRM 框架。 + +这意味着 BOVIN 项目（bovin-pathway-demo + bovin-bench）在 Zhang & Torok 的整体科研路线图里是**"治疗工程层"的一个具体落点**。Liver_References 数据则是**"传感 + 计算层"的一个数据底座**。两者在论文框架内是配套的——治疗工程提供"会发生什么"的工具，传感+计算层提供"对谁会发生什么"的判断。 + +具体落到你目前正在做的事： + +> **bovin-pathway-demo 的 11 模块** ↔ 可对应到论文中的"修复状态向量"——M6 (IFN) 对应炎症/免疫激活，M4 (DAMP) 对应损伤释放，M7 (antigen presentation) 对应免疫识别。BOVIN 现有的 IG 归因可以被解读为"这个患者目前的修复状态分布"。 +> +> **bovin-bench 的 ICI 队列** ↔ 论文中"修复状态 6（肿瘤-再生冲突）"的实证数据集——既有肿瘤（需要免疫激活）也有 cirrhotic background（需要免疫克制 + 抗纤维化）。 +> +> **未来 Aim 3（OV 设计回路）** ↔ 论文中"Cancer-safe 再生测试架构"的实例化——4 种类器官 × 5 输出的工程实现。 + +所以这篇 Perspective 的真正价值，是它**把 BOVIN 从一个孤立的纳米药物项目，重新框架成 AI-CCRM 整体生态里的一个具体组件**。这对论文写作（说服评审 BOVIN 不只是 ad hoc 工程）、对 grant 申请（讲清楚 BOVIN 在哪个更大的 vision 里）、对 collaborator pitch（Zhang + Torok 的双 lab 合作）都很有用。 + +--- + +## 八、值得注意的几点（含批判性观察） + +第一，**"No new datasets" 的双刃剑**——论文坦诚自己是 Perspective，但这也意味着**框架未被任何具体数据验证**。8 个修复状态是"提议"，5 阶段 roadmap 是"愿景"，cancer-safe 标准是"应当"。读者要警惕"听起来合理 ≠ 经过验证"。建议在自己之后的论文里**给框架某个具体部分做出可测试的实证**——比如用 BOVIN 数据示范"在 ICI 队列里，BOVIN 的 11 模块输出可以被映射到论文 8 个修复状态的概率分布上"——这就把 Perspective 的 conceptual 主张落到 empirical 层面。 + +第二，**自引网络浓度较高**——参考文献 #1（AI-CCRM 母 Science 2026）、#9（AI-nanomedicine Chem Soc Rev 2026）、#23（mitochondrial phenotype Nat Comput Sci 2024）、#28（smartphone microfluidic Nat Commun 2023）等都是 Zhang 自己的论文。**这本身不是问题**——他确实在这个方向做了很多事——但**外部 paper 链接的密度可以更高**。如果是论文评审，可能会评一句 "the literature foundation could be broader"。 + +第三，**8 个修复状态的粒度选择**——8 个状态比"4 类疾病诊断"细，比"30 个分子亚型"粗。这个粒度是论文作者的工程取舍，不是从数据反推出来的。**未来真实数据可能支持的状态数 ≠ 8**——可能是 5 个、可能是 12 个。表 1 里这 8 个的"代表性信号"列也偏概念性、不够定量。建议你后续做实证工作时，**不要锁死在 8 这个数字上**，让数据告诉你真实的 state 数。 + +第四，**临床医生接受度风险**——把诊断标签替换成 8 维状态向量，这在 AI/ML 领域是自然的，但在临床端是巨大的认知转换。论文承认这一点（"AI models must be explainable, bias-tested and integrated into clinician workflows without replacing clinical judgment"），但没给出具体的过渡方案。**这是 Perspective 转化为产品时最大的非技术阻力**。 + +第五，**5 阶段 roadmap 的"Stage 1"是关键卡点**——Stage 1（"Retrospective integration of large clinical, imaging, histology, omics and outcome datasets"）写一句话，但实际工作量是巨大的。Liver_References 17 份只是这个 Stage 1 的一小部分；要把 MASLD / ALD / ACLF / HCC / CCA / 移植后等所有这些场景的数据都拉齐，**单是数据收集 + 清洗就是 3–5 年工程量**。论文没有给出 Stage 1 的人力 / 资金估计——这往往是 grant 评审会问到的事。 + +第六，**"Bioorthogonal virus immuno-nanomedicine" 在论文里的出场是 reference-level 的**——意思是 BOVIN 是论文引用的众多技术之一，不是论文的主体。这对你的意义是：**这篇论文是 Zhang 给 BOVIN 写的"广告位"，不是 BOVIN 自己的成果论文**。BOVIN 自己的 first-authorship paper（你 Frank Fu 作为可能的第一作者）还需要另写——大概率会用 bovin-pathway-demo 的 v0.2-aim2 结果 + 一份新的 cancer-safe 验证（用 Liver_References 数据 + 未来 HCC tumor 数据配对类器官）。 + +--- + +## 九、对你"下一步"的几个具体启示 + +第一，**Liver_References 数据集的"使用价值定位"应该明确**——它不是 ICI 响应预测的训练集（那是 bovin-bench 的工作），它是论文框架里的 **"修复状态 baseline reference"**。对它的使用应该围绕"什么是健康肝 + 慢性胆汁淤积病的细胞状态"这个问题展开，而不是"它怎么帮 BOVIN 提升 AUC"。两条路径都对，但思路不同。 + +第二，**优先补齐 HCC 肿瘤数据**——论文最有原创性的部分（cancer-safe 再生）需要肿瘤 + 非肿瘤的配对。Liver_References 提供了非肿瘤一半，bovin-bench/hcc/CATALOG.md 的 FOUND-05 / FOUND-06 提供了肿瘤一半的"已登记但未下载"清单。把这些数据拉到 `database_unified/HCC_Tumor_Cohorts/` 是非常自然的下一步——可以复用 Liver_References 的命名规则（modality__cohort__content__nxm__uuid.h5ad）。 + +第三，**考虑做一份"框架可测试性"小论文**——把这篇 Perspective 的某个具体主张拿出来用实证数据测一遍。最容易的标的：把 BOVIN 在 v0.2-aim2 的 IG 归因映射到论文 8 个修复状态上，看看 ICI 响应者与非响应者在状态空间里的分布是否可分。这种"小落地"论文是 Zhang lab 经典做法的延伸（先 Perspective 立框架、后 empirical paper 实例化）。 + +第四，**作为 AIVIN 的 README**——这篇 Perspective 实际上是整个 AIVIN 项目（bovin-pathway-demo + bovin-bench + database_unified）的**最高层 README**。它告诉任何读者"为什么这三个子项目存在、它们如何拼成一个整体"。建议在 AIVIN 顶层的某个文档（比如未来可能的 `AIVIN/README.md`）里把这篇 Perspective 的 8 状态 + 5 阶段 + cancer-safe 三个核心概念 link 进来，作为项目"为什么"的来源。 + +第五，**Zhang + Torok 的双 lab 合作意义**——论文第一作者 Zhang 是 UCSD（你的导师），通讯作者 Torok 是 Stanford 医学院。这意味着 **AIVIN 项目背后是双校合作**，未来如果要去 Stanford 拿 sample / clinical data，Torok lab 是直接通道。这对 bovin-bench 里那些标注"DUA / sponsor-only"的 HCC ICI 队列尤其重要——Stanford 医学院通常能走 academic DUA 路径访问 IMbrave150 / KEYNOTE-224 等数据集。 + +--- + +## 十、一句话总结 + +> 这篇 Perspective 是 AIVIN 项目的**理论母舰**——它不直接用 Liver_References 数据，但它提出的 8 个修复状态 + 5 层技术栈 + cancer-safe 范式，**决定了你应该用什么数据 + 怎么用 + 用来回答什么问题**。BOVIN 在论文里只是众多"治疗工程层"技术之一；Liver_References 是论文"修复状态 baseline"层的第一块拼图；未来 HCC_Tumor_Cohorts、organoid 数据、ACLF 患者数据等都将作为这个框架的其他拼图陆续加入。 + +--- + +*阅读笔记生成自 PDF 21 页全文的逐节解读 · 关联到 Liver_References 17 份数据 · 关联到 bovin-pathway-demo 与 bovin-bench 现有进度 · 笔记本身不修改源 PDF，只作伴随文档。* diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ca0ddb9ccb1ca124a477b42636d945f61d85e3d3 --- /dev/null +++ b/README.md @@ -0,0 +1,281 @@ +--- +license: cc-by-4.0 +language: +- en +size_categories: +- 1M________.h5ad + │ │ │ │ └── GEO GSE / GSM · GSA HRA · CELLxGENE UUID · Zenodo ID + │ │ │ └────────────── shape: cells × genes (or spots × genes for visium) + │ │ └────────────────────── first-author + year + n donors/samples + │ └─────────────────────────────────── biology tag (disease + sub-type) + └───────────────────────────────────────────── modality: sc = single-cell · sn = single-nucleus · visium = spatial +``` + +### Cohort manifest + +#### Cancer cohorts (GEO source · 24 files · ~874k cells) + +| Cohort slug | Source | Cells | Disease | Platform | Citation | +|---|---|---:|---|---|---| +| `hcc-cd45` | GSE235863 | 191,435 | HCC CD45+ enriched | 10x | Guo et al., 2025 | +| `hcc-fetal` | GSE156625 | 109,238 | HCC onco-fetal | 10x | Sharma et al., *Cell* 2020 | +| `hcc-cd8tcell` | GSE235863 | 95,408 | HCC CD8 T cells | 10x | Guo et al., 2025 | +| `hcc-tumor-normal` (Sharma) | GSE156625 | 73,589 | HCC tumor + adjacent | 10x | Sharma et al., *Cell* 2020 | +| `hcc-multisite` | GSE149614 | 71,915 | HCC primary + metastatic + PVTT + LN | 10x | Lu et al., *Nat Commun* 2022 | +| `hcc-iccA-cd45` | GSE140228-droplet | 66,187 | HCC + iCCA, CD45+ | 10x | Sharma et al., *Cell* 2020 | +| `hcc-iccA-treated` | GSE151530 | 56,721 | HCC + iCCA, post-treatment | 10x | Ma et al., *J Hepatol* 2021 | +| `hcc-trm` | GSE281110 | 41,848 | HCC tumor-associated TRM T | 10x | Park et al., 2025 | +| `hcc-tumor-normal-3pt` | GSE189175 | 39,995 | HCC tumor + normal | sn-10x | Alvarez et al., 2022 | +| `hcc-tumor-normal-1pt` | GSE189175 | 39,995 | (duplicate — see Known Issues) | sn-10x | Alvarez et al., 2022 | +| `hcc-mash-spectrum` | GSE282630 | 34,396 | HCC + MASH spectrum | 10x | Huang et al., 2025 | +| `hcc-cd45-ss2` | GSE140228-ss2 | 7,074 | HCC CD45+ Smart-seq2 | SS2 | Sharma et al., *Cell* 2020 | +| `hcc-iccA-mixed-set1` | GSE125449-set1 | 5,115 | HCC + iCCA, mixed | 10x | Ma et al., *Cancer Cell* 2019 | +| `hcc-tcell` | GSE98638 | 5,063 | HCC infiltrating T cells | SMART-seq2 | Zheng et al., *Cell* 2017 | +| `hcc-iccA-mixed-set2` | GSE125449-set2 | 4,831 | HCC + iCCA, mixed | 10x | Ma et al., *Cancer Cell* 2019 | +| `hcc-antiPD1` (R1) | GSE238264-HCC1R | 3,006 | HCC anti-PD1 responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (R4) | GSE238264-HCC4R | 3,002 | HCC anti-PD1 responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (R2) | GSE238264-HCC2R | 2,766 | HCC anti-PD1 responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (NR6) | GSE238264-HCC6NR | 2,575 | HCC anti-PD1 non-responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (NR7) | GSE238264-HCC7NR | 2,453 | HCC anti-PD1 non-responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (R3) | GSE238264-HCC3R | 2,170 | HCC anti-PD1 responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (NR5) | GSE238264-HCC5NR | 1,320 | HCC anti-PD1 non-responder | 10x | Liu et al., 2025 | +| `cld-lyec` | GSE129933 | 901 | Chronic liver disease lymphatic EC | SMART-seq2 | Tamburini et al., *Front Immunol* 2019 | +| `healthy-nat` | GSM4648565 | 13,083 | healthy liver | 10x | (Nat Commun 2020) | + +#### Healthy + autoimmune baselines (CELLxGENE Census · 11 sc/sn files · ~303k cells) + +| Cohort slug | Cells | Cell type / disease | Modality | +|---|---:|---|---| +| `psc-pbc-healthy` (sn) | 105,780 | PSC + PBC + healthy, all cells | sn | +| `psc-pbc-healthy` (sc) | 89,637 | PSC + PBC + healthy, all cells | sc | +| `healthy hepatocyte-v1` | 53,015 | hepatocytes | sc | +| `healthy lymphoid` | 16,665 | lymphoid lineage | sc | +| `healthy hepatocyte-v2` | 13,635 | hepatocytes (alt curation) | sc | +| `healthy macrophage` | 11,127 | macrophages | sc | +| `healthy endothelial` | 9,422 | endothelial cells | sc | +| `healthy stellate` | 1,417 | hepatic stellate cells | sc | +| `healthy b-cell` | 1,250 | B cells | sc | +| `healthy cholangiocyte` | 1,011 | cholangiocytes | sc | + +#### Spatial transcriptomics (CELLxGENE Census · 6 Visium files · ~35k spots) + +| Cohort slug | Spots | Tissue block | Disease | +|---|---:|---|---| +| `visium healthy-C73 / blockA1` | 4,992 | block A1 | healthy donor C73 | +| `visium healthy-C73 / blockC1` | 4,992 | block C1 | healthy donor C73 | +| `visium healthy-C73 / blockD1` | 4,992 | block D1 | healthy donor C73 | +| `visium psc-PSC011 / blockA1` | 4,992 | block A1 | PSC patient 011 | +| `visium psc-PSC011 / blockB1` | 4,992 | block B1 | PSC patient 011 | +| `visium psc-PSC011 / blockC1` | 4,992 | block C1 | PSC patient 011 | +| `visium psc-PSC011 / blockD1` | 4,992 | block D1 | PSC patient 011 | + +--- + +## Schema + +All `.h5ad` conform to **CELLxGENE schema 7.0.0** plus AIVIN extensions: + +**`obs` (cells) — required columns** + +- `cell_id` (index) +- `donor_id` (when known) +- `tissue_site` — unified vocab: `PT` (primary tumor) · `NTL` (normal liver) · `JTL` (juxta-tumor liver) · `MLN` (lymph node metastasis) · `PVTT` (portal vein tumor thrombus) · `PBMC` (peripheral blood) · `LIL` (liver intra-lesional) +- `disease` — values within the **Disease dimensions** list above +- `cell_type` (when annotated by original author) +- `assay` — platform / chemistry + +**`var` (genes) — convention** + +- Ensembl ID as `var.index` (when available, esp. CELLxGENE-sourced) +- Some GEO-sourced cohorts use HGNC `gene_symbol` as index + `entrez_id` column +- Heterogeneity across cohorts: 18 distinct gene-space sizes (2,384 – 58,100 genes) — see `aivin_obs_field_notes` per file for caveats; downstream concat use `ad.concat(..., join='outer')` + +**`uns` (provenance, AIVIN-specific)** + +- `citation` — full APA reference +- `doi` — primary paper DOI +- `source_accession` — GEO GSE / GSM / GSA HRA / CELLxGENE UUID / Zenodo ID +- `source_url` +- `aivin_ingest_date` +- `aivin_cohort_slug` +- `aivin_source_files` — original raw filename list +- `aivin_obs_field_notes` — any value-mapping done in ingest + +--- + +## Usage + +### Load one cohort (lazy / single file) + +```python +from huggingface_hub import hf_hub_download +import anndata as ad + +path = hf_hub_download( + repo_id='AIVIN-UCSD/liver-references', + filename='sc__hcc-multisite__lu2022-10pts__71915cx25712g__GSE149614.h5ad', + repo_type='dataset', +) +a = ad.read_h5ad(path) +print(a) +# Inspect AIVIN provenance +print(a.uns['citation']) +print(a.uns['aivin_obs_field_notes']) +``` + +### Load all cancer cohorts + concat (gene union) + +```python +from huggingface_hub import snapshot_download +from pathlib import Path +import anndata as ad + +local = snapshot_download( + repo_id='AIVIN-UCSD/liver-references', + repo_type='dataset', + allow_patterns='sc__hcc-*.h5ad', # cancer only + ignore_patterns='*macparland2019-0donors*', # skip known-broken file +) +adatas = {f.stem: ad.read_h5ad(f) for f in Path(local).glob('sc__hcc-*.h5ad')} +merged = ad.concat(adatas, axis=0, join='outer', label='cohort', fill_value=0) +print(merged) +# ~750k cells × union of genes across cohorts +``` + +### Pipe into scvi-tools (foundation model training) + +```python +import scvi +scvi.model.SCVI.setup_anndata(merged, batch_key='cohort') +model = scvi.model.SCVI(merged, n_layers=2, n_latent=30) +model.train(accelerator='mps') # Apple Silicon MPS acceleration +``` + +--- + +## Citation + +If you use this dataset in a publication, please cite: + +1. **AIVIN as a collection** (this dataset card): + ```bibtex + @dataset{aivin_liver_2026Q2, + author = {AIVIN Project, UCSD}, + title = {{AIVIN Liver References (2026-Q2 v1.0)}}, + year = {2026}, + publisher = {Hugging Face}, + doi = {[pending HF DOI mint]}, + url = {https://huggingface.co/datasets/AIVIN-UCSD/liver-references} + } + ``` + +2. **Each individual cohort** — see the `uns.citation` field of every `.h5ad`, + or the **Cohort manifest** table above. Particularly for landmark papers: + - Lu et al., *Nat Commun* 13:4594 (2022) — `doi:10.1038/s41467-022-32283-3` + - Sharma et al., *Cell* 183:377 (2020) — `doi:10.1016/j.cell.2020.08.040` + - Ma et al., *J Hepatol* 75:1418 (2021) — `doi:10.1016/j.jhep.2021.06.028` + - Ma et al., *Cancer Cell* 36:418 (2019) — `doi:10.1016/j.ccell.2019.08.007` + - Zheng et al., *Cell* 169:1342 (2017) — `doi:10.1016/j.cell.2017.05.035` + +3. **(Optional) the Zenodo permanent snapshot** for byte-frozen reproducibility: + `doi: [pending Sat 5/30]` + +--- + +## License + +This collection is released under **CC-BY-4.0**. The license applies to AIVIN's +harmonization, schema mapping, and provenance metadata. **You must still cite +the original cohort papers** when using their data — see the per-cohort +manifest above. Cohorts derived from controlled-access sources (e.g., GSA-Human +HRA001748 Xue 2022) are NOT included in this public repo; see the cross-tissue +meta-repo for access pointers. + +--- + +## Pipeline & reproducibility + +- **Ingest scripts**: `github.com/AIVIN-UCSD/aivin/tree/main/scripts` + (per-cohort `__ingest.py` + `W3_backlog_ingest.py` dispatcher) +- **Methods extracts**: per-paper structured methods at + `github.com/AIVIN-UCSD/aivin/tree/main/literature/A_cancer_TME/methods_extracts` +- **Structure report**: full per-file schema audit at + `github.com/AIVIN-UCSD/aivin/blob/main/database_unified/Liver_References/STRUCTURE_REPORT.md` +- **Backlog inventory**: candidates for v3 (3-month) expansion at + `github.com/AIVIN-UCSD/aivin/blob/main/database_unified/_staging/BACKLOG_INVENTORY.md` + +--- + +## Known issues (v1.0) + +| Issue | Affected file | Fix planned | +|---|---|---| +| **MacParland v1 ingest broken** (shape `0 × 3,958,008`) — the multi-plate CEL-Seq2 concat in `ingest_GSE124395()` produced a degenerate output | `sc__healthy-hlca__macparland2019-0donors__0cx3958008g__GSE124395.h5ad` | Will re-ingest in v1.1 with proper plate-level dedup; **filter out via `ignore_patterns='*macparland2019-0donors*'`** in `snapshot_download` | +| **GSE189175 Alvarez duplicate** — same 39,995 cells appear twice with different `who` slugs (`alvarez2022-1pts` and `alvarez2022-3pts`) | both files identical | Will dedup to single file in v1.1 | +| **Gene-space heterogeneity** — 18 distinct gene-space sizes across cohorts (Smart-seq2 ~54k vs 10x v3 ~36k vs reduced curation ~2-3k) | all multi-cohort concat operations | Use `ad.concat(..., join='outer', fill_value=0)`; foundation model fine-tune should project to common Ensembl space | +| **Some cohorts use HGNC symbol as var.index, others use Ensembl ID** | mixed across GEO vs CELLxGENE | Documented per-file in `uns.aivin_obs_field_notes`; v2 will unify to Ensembl ID | + +--- + +## Contact + +- 🤗 **HF discussions tab** on this repo (preferred for technical questions) +- 💬 **scverse Discourse**: https://discourse.scverse.org/ — `#show-and-tell` thread +- 📧 **z4fu@ucsd.edu** (project lead) +- 🐛 **Issues / PRs**: `github.com/AIVIN-UCSD/aivin` + +--- + +*Last updated: 2026-05-25 · AIVIN v2.0 snapshot 2026-Q2 · 41 .h5ad (40 valid) · 1.17M cells + 35k spots · ~14 GB* diff --git a/README.md.bak b/README.md.bak new file mode 100644 index 0000000000000000000000000000000000000000..691c20034c90c22b1fe5be605cd69ebf90a31374 --- /dev/null +++ b/README.md.bak @@ -0,0 +1,281 @@ +--- +license: cc-by-4.0 +language: +- en +size_categories: +- 1M________.h5ad + │ │ │ │ └── GEO GSE / GSM · GSA HRA · CELLxGENE UUID · Zenodo ID + │ │ │ └────────────── shape: cells × genes (or spots × genes for visium) + │ │ └────────────────────── first-author + year + n donors/samples + │ └─────────────────────────────────── biology tag (disease + sub-type) + └───────────────────────────────────────────── modality: sc = single-cell · sn = single-nucleus · visium = spatial +``` + +### Cohort manifest + +#### Cancer cohorts (GEO source · 24 files · ~874k cells) + +| Cohort slug | Source | Cells | Disease | Platform | Citation | +|---|---|---:|---|---|---| +| `hcc-cd45` | GSE235863 | 191,435 | HCC CD45+ enriched | 10x | Guo et al., 2025 | +| `hcc-fetal` | GSE156625 | 109,238 | HCC onco-fetal | 10x | Sharma et al., *Cell* 2020 | +| `hcc-cd8tcell` | GSE235863 | 95,408 | HCC CD8 T cells | 10x | Guo et al., 2025 | +| `hcc-tumor-normal` (Sharma) | GSE156625 | 73,589 | HCC tumor + adjacent | 10x | Sharma et al., *Cell* 2020 | +| `hcc-multisite` | GSE149614 | 71,915 | HCC primary + metastatic + PVTT + LN | 10x | Lu et al., *Nat Commun* 2022 | +| `hcc-iccA-cd45` | GSE140228-droplet | 66,187 | HCC + iCCA, CD45+ | 10x | Sharma et al., *Cell* 2020 | +| `hcc-iccA-treated` | GSE151530 | 56,721 | HCC + iCCA, post-treatment | 10x | Ma et al., *J Hepatol* 2021 | +| `hcc-trm` | GSE281110 | 41,848 | HCC tumor-associated TRM T | 10x | Park et al., 2025 | +| `hcc-tumor-normal-3pt` | GSE189175 | 39,995 | HCC tumor + normal | sn-10x | Alvarez et al., 2022 | +| `hcc-tumor-normal-1pt` | GSE189175 | 39,995 | (duplicate — see Known Issues) | sn-10x | Alvarez et al., 2022 | +| `hcc-mash-spectrum` | GSE282630 | 34,396 | HCC + MASH spectrum | 10x | Huang et al., 2025 | +| `hcc-cd45-ss2` | GSE140228-ss2 | 7,074 | HCC CD45+ Smart-seq2 | SS2 | Sharma et al., *Cell* 2020 | +| `hcc-iccA-mixed-set1` | GSE125449-set1 | 5,115 | HCC + iCCA, mixed | 10x | Ma et al., *Cancer Cell* 2019 | +| `hcc-tcell` | GSE98638 | 5,063 | HCC infiltrating T cells | SMART-seq2 | Zheng et al., *Cell* 2017 | +| `hcc-iccA-mixed-set2` | GSE125449-set2 | 4,831 | HCC + iCCA, mixed | 10x | Ma et al., *Cancer Cell* 2019 | +| `hcc-antiPD1` (R1) | GSE238264-HCC1R | 3,006 | HCC anti-PD1 responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (R4) | GSE238264-HCC4R | 3,002 | HCC anti-PD1 responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (R2) | GSE238264-HCC2R | 2,766 | HCC anti-PD1 responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (NR6) | GSE238264-HCC6NR | 2,575 | HCC anti-PD1 non-responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (NR7) | GSE238264-HCC7NR | 2,453 | HCC anti-PD1 non-responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (R3) | GSE238264-HCC3R | 2,170 | HCC anti-PD1 responder | 10x | Liu et al., 2025 | +| `hcc-antiPD1` (NR5) | GSE238264-HCC5NR | 1,320 | HCC anti-PD1 non-responder | 10x | Liu et al., 2025 | +| `cld-lyec` | GSE129933 | 901 | Chronic liver disease lymphatic EC | SMART-seq2 | Tamburini et al., *Front Immunol* 2019 | +| `healthy-nat` | GSM4648565 | 13,083 | healthy liver | 10x | (Nat Commun 2020) | + +#### Healthy + autoimmune baselines (CELLxGENE Census · 11 sc/sn files · ~303k cells) + +| Cohort slug | Cells | Cell type / disease | Modality | +|---|---:|---|---| +| `psc-pbc-healthy` (sn) | 105,780 | PSC + PBC + healthy, all cells | sn | +| `psc-pbc-healthy` (sc) | 89,637 | PSC + PBC + healthy, all cells | sc | +| `healthy hepatocyte-v1` | 53,015 | hepatocytes | sc | +| `healthy lymphoid` | 16,665 | lymphoid lineage | sc | +| `healthy hepatocyte-v2` | 13,635 | hepatocytes (alt curation) | sc | +| `healthy macrophage` | 11,127 | macrophages | sc | +| `healthy endothelial` | 9,422 | endothelial cells | sc | +| `healthy stellate` | 1,417 | hepatic stellate cells | sc | +| `healthy b-cell` | 1,250 | B cells | sc | +| `healthy cholangiocyte` | 1,011 | cholangiocytes | sc | + +#### Spatial transcriptomics (CELLxGENE Census · 6 Visium files · ~35k spots) + +| Cohort slug | Spots | Tissue block | Disease | +|---|---:|---|---| +| `visium healthy-C73 / blockA1` | 4,992 | block A1 | healthy donor C73 | +| `visium healthy-C73 / blockC1` | 4,992 | block C1 | healthy donor C73 | +| `visium healthy-C73 / blockD1` | 4,992 | block D1 | healthy donor C73 | +| `visium psc-PSC011 / blockA1` | 4,992 | block A1 | PSC patient 011 | +| `visium psc-PSC011 / blockB1` | 4,992 | block B1 | PSC patient 011 | +| `visium psc-PSC011 / blockC1` | 4,992 | block C1 | PSC patient 011 | +| `visium psc-PSC011 / blockD1` | 4,992 | block D1 | PSC patient 011 | + +--- + +## Schema + +All `.h5ad` conform to **CELLxGENE schema 7.0.0** plus AIVIN extensions: + +**`obs` (cells) — required columns** + +- `cell_id` (index) +- `donor_id` (when known) +- `tissue_site` — unified vocab: `PT` (primary tumor) · `NTL` (normal liver) · `JTL` (juxta-tumor liver) · `MLN` (lymph node metastasis) · `PVTT` (portal vein tumor thrombus) · `PBMC` (peripheral blood) · `LIL` (liver intra-lesional) +- `disease` — values within the **Disease dimensions** list above +- `cell_type` (when annotated by original author) +- `assay` — platform / chemistry + +**`var` (genes) — convention** + +- Ensembl ID as `var.index` (when available, esp. CELLxGENE-sourced) +- Some GEO-sourced cohorts use HGNC `gene_symbol` as index + `entrez_id` column +- Heterogeneity across cohorts: 18 distinct gene-space sizes (2,384 – 58,100 genes) — see `aivin_obs_field_notes` per file for caveats; downstream concat use `ad.concat(..., join='outer')` + +**`uns` (provenance, AIVIN-specific)** + +- `citation` — full APA reference +- `doi` — primary paper DOI +- `source_accession` — GEO GSE / GSM / GSA HRA / CELLxGENE UUID / Zenodo ID +- `source_url` +- `aivin_ingest_date` +- `aivin_cohort_slug` +- `aivin_source_files` — original raw filename list +- `aivin_obs_field_notes` — any value-mapping done in ingest + +--- + +## Usage + +### Load one cohort (lazy / single file) + +```python +from huggingface_hub import hf_hub_download +import anndata as ad + +path = hf_hub_download( + repo_id='ucsd-aivin/liver-references', + filename='sc__hcc-multisite__lu2022-10pts__71915cx25712g__GSE149614.h5ad', + repo_type='dataset', +) +a = ad.read_h5ad(path) +print(a) +# Inspect AIVIN provenance +print(a.uns['citation']) +print(a.uns['aivin_obs_field_notes']) +``` + +### Load all cancer cohorts + concat (gene union) + +```python +from huggingface_hub import snapshot_download +from pathlib import Path +import anndata as ad + +local = snapshot_download( + repo_id='ucsd-aivin/liver-references', + repo_type='dataset', + allow_patterns='sc__hcc-*.h5ad', # cancer only + ignore_patterns='*macparland2019-0donors*', # skip known-broken file +) +adatas = {f.stem: ad.read_h5ad(f) for f in Path(local).glob('sc__hcc-*.h5ad')} +merged = ad.concat(adatas, axis=0, join='outer', label='cohort', fill_value=0) +print(merged) +# ~750k cells × union of genes across cohorts +``` + +### Pipe into scvi-tools (foundation model training) + +```python +import scvi +scvi.model.SCVI.setup_anndata(merged, batch_key='cohort') +model = scvi.model.SCVI(merged, n_layers=2, n_latent=30) +model.train(accelerator='mps') # Apple Silicon MPS acceleration +``` + +--- + +## Citation + +If you use this dataset in a publication, please cite: + +1. **AIVIN as a collection** (this dataset card): + ```bibtex + @dataset{aivin_liver_2026Q2, + author = {AIVIN Project, UCSD}, + title = {{AIVIN Liver References (2026-Q2 v1.0)}}, + year = {2026}, + publisher = {Hugging Face}, + doi = {[pending HF DOI mint]}, + url = {https://huggingface.co/datasets/ucsd-aivin/liver-references} + } + ``` + +2. **Each individual cohort** — see the `uns.citation` field of every `.h5ad`, + or the **Cohort manifest** table above. Particularly for landmark papers: + - Lu et al., *Nat Commun* 13:4594 (2022) — `doi:10.1038/s41467-022-32283-3` + - Sharma et al., *Cell* 183:377 (2020) — `doi:10.1016/j.cell.2020.08.040` + - Ma et al., *J Hepatol* 75:1418 (2021) — `doi:10.1016/j.jhep.2021.06.028` + - Ma et al., *Cancer Cell* 36:418 (2019) — `doi:10.1016/j.ccell.2019.08.007` + - Zheng et al., *Cell* 169:1342 (2017) — `doi:10.1016/j.cell.2017.05.035` + +3. **(Optional) the Zenodo permanent snapshot** for byte-frozen reproducibility: + `doi: [pending Sat 5/30]` + +--- + +## License + +This collection is released under **CC-BY-4.0**. The license applies to AIVIN's +harmonization, schema mapping, and provenance metadata. **You must still cite +the original cohort papers** when using their data — see the per-cohort +manifest above. Cohorts derived from controlled-access sources (e.g., GSA-Human +HRA001748 Xue 2022) are NOT included in this public repo; see the cross-tissue +meta-repo for access pointers. + +--- + +## Pipeline & reproducibility + +- **Ingest scripts**: `github.com/ucsd-aivin/aivin/tree/main/scripts` + (per-cohort `__ingest.py` + `W3_backlog_ingest.py` dispatcher) +- **Methods extracts**: per-paper structured methods at + `github.com/ucsd-aivin/aivin/tree/main/literature/A_cancer_TME/methods_extracts` +- **Structure report**: full per-file schema audit at + `github.com/ucsd-aivin/aivin/blob/main/database_unified/Liver_References/STRUCTURE_REPORT.md` +- **Backlog inventory**: candidates for v3 (3-month) expansion at + `github.com/ucsd-aivin/aivin/blob/main/database_unified/_staging/BACKLOG_INVENTORY.md` + +--- + +## Known issues (v1.0) + +| Issue | Affected file | Fix planned | +|---|---|---| +| **MacParland v1 ingest broken** (shape `0 × 3,958,008`) — the multi-plate CEL-Seq2 concat in `ingest_GSE124395()` produced a degenerate output | `sc__healthy-hlca__macparland2019-0donors__0cx3958008g__GSE124395.h5ad` | Will re-ingest in v1.1 with proper plate-level dedup; **filter out via `ignore_patterns='*macparland2019-0donors*'`** in `snapshot_download` | +| **GSE189175 Alvarez duplicate** — same 39,995 cells appear twice with different `who` slugs (`alvarez2022-1pts` and `alvarez2022-3pts`) | both files identical | Will dedup to single file in v1.1 | +| **Gene-space heterogeneity** — 18 distinct gene-space sizes across cohorts (Smart-seq2 ~54k vs 10x v3 ~36k vs reduced curation ~2-3k) | all multi-cohort concat operations | Use `ad.concat(..., join='outer', fill_value=0)`; foundation model fine-tune should project to common Ensembl space | +| **Some cohorts use HGNC symbol as var.index, others use Ensembl ID** | mixed across GEO vs CELLxGENE | Documented per-file in `uns.aivin_obs_field_notes`; v2 will unify to Ensembl ID | + +--- + +## Contact + +- 🤗 **HF discussions tab** on this repo (preferred for technical questions) +- 💬 **scverse Discourse**: https://discourse.scverse.org/ — `#show-and-tell` thread +- 📧 **z4fu@ucsd.edu** (project lead) +- 🐛 **Issues / PRs**: `github.com/ucsd-aivin/aivin` + +--- + +*Last updated: 2026-05-25 · AIVIN v2.0 snapshot 2026-Q2 · 41 .h5ad (40 valid) · 1.17M cells + 35k spots · ~14 GB* diff --git a/STRUCTURE_REPORT.md b/STRUCTURE_REPORT.md new file mode 100644 index 0000000000000000000000000000000000000000..8bd266e279613c7207f806685659b8ae1db8b7a0 --- /dev/null +++ b/STRUCTURE_REPORT.md @@ -0,0 +1,245 @@ +# database_unified/Liver_References · 数据结构与特征报告 + +**生成日期**：2026-05-17（v1 首次写入；与 Colorectal/Breast/Gastric 三份 STRUCTURE_REPORT 体例对齐） +**对象目录**：`database_unified/Liver_References/`（18 份 `.h5ad`，~6.96 GB）+ 计划加入 4 份 Wave 2 HCC scRNA dataset +**目的**：在做任何清洗 / 合并 / 派生视图之前，沉淀一份不削减、不假设用途的结构档案。本报告与 `Colorectal_References/STRUCTURE_REPORT.md` 共用同一份模板，便于跨脏器横向对照。 +**附属机器可读资料**： +- `_provenance/uuid_to_new_name.csv` — 18 文件 UUID/GSM ↔ 新文件名双向映射 +- `merged_views/MERGE_REPORT.md` — 子包内合并视图说明 + dashboard + +--- + +## 一、来源与许可 + +当前 18 份 `.h5ad` 来自**两个独立来源**： + +| 子包 | 文件数 | 来源 | DOI | License | +|---|---:|---|---|---| +| **A · 健康肝 per-cell-type 子集** | 8 | CZ CELLxGENE Discover（同一 collection） | `10.1016/j.jhep.2023.12.023` | CC-BY-4.0 | +| **B · 全细胞图谱（PSC + PBC + healthy）** | 2 | 同 A · CELLxGENE | 同 A | 同 A | +| **C · Visium 空间转录组** | 7 | 同 A · CELLxGENE | 同 A | 同 A | +| **D · GEO 增量 NAT raw counts** | 1 | GEO GSE153643（Regev lab, Broad Institute, 2021-05） | — | GEO 默认公开 | + +身份信息汇总： + +| 字段 | 值 | +|---|---| +| 数据来源（主） | CZ CELLxGENE Discover（A/B/C：1 个 collection · DOI `10.1016/j.jhep.2023.12.023`） | +| 数据来源（增量） | GEO GSE153643（D：Regev lab "Single cells from adipose and liver"） | +| 物种 | *Homo sapiens*（全部 18 份） | +| AnnData schema | A/B/C 全部 CZ CELLxGENE **7.0.0**；D 是 raw .h5ad（**几乎没有 obs 元数据**，see §gotcha） | +| 总细胞 / spot 数 | scRNA 195,417 + snRNA 105,780 + Visium spot 34,944 + GEO sc 13,083 = **349,224** | +| 独立供体数 | A/B/C：12 healthy + PSC011 + PBC003-PBC004 + healthy-C73 等 ~15 名；D：1 NAT donor · **跨子包无重叠** | +| 累计大小 | 约 **6.96 GB** | +| 唯一疾病维度 | **healthy / PSC / PBC / NAT**（**无 HCC / iCCA / 转移 / ICB**——这是 Wave 2 要补的差距） | + +--- + +## 二、目录现状一览 + +物理布局： + +``` +database_unified/ +└── Liver_References/ ← ~6.96 GB + ├── sc__healthy__b-cell__1250cx32596g__.h5ad ← 子包 A · 健康肝 per-cell-type × 8 文件 + ├── sc__healthy__cholangiocyte__1011cx32596g__.h5ad + ├── sc__healthy__endothelial__9422cx32596g__.h5ad + ├── sc__healthy__hepatocyte-v1__53015cx32596g__.h5ad + ├── sc__healthy__hepatocyte-v2__13635cx32596g__.h5ad + ├── sc__healthy__lymphoid__16665cx32596g__.h5ad + ├── sc__healthy__macrophage__11127cx32596g__.h5ad + ├── sc__healthy__stellate__1417cx32596g__.h5ad + ├── sc__psc-pbc-healthy__all-cells__89637cx32596g__.h5ad ← 子包 B · 主图谱 × 2 文件 + ├── sn__psc-pbc-healthy__all-cells__105780cx32596g__.h5ad + ├── visium__healthy-C73__block{A1,C1,D1}__4992sx35477g__.h5ad ← 子包 C · Visium × 7 文件 + ├── visium__psc-PSC011__block{A1,B1,C1,D1}__4992sx35477g__.h5ad + ├── sc__healthy-nat__liver__13083cx33694g__GSM4648565.h5ad ← 子包 D · GEO 增量 × 1 文件 + ├── AI_powered_CCRM_liver.pdf ← 配套文献（早期） + ├── AI_powered_CCRM_liver__reading_notes.md ← 阅读笔记 + ├── merged_views/ ← 派生视图（合并子包） + │ ├── sc__healthy__merged-all-celltypes__107542cx32596g.h5ad + │ ├── visium__healthy-C73__merged-all-blocks__14976sx35477g.h5ad + │ ├── visium__psc-PSC011__merged-all-blocks__19968sx35477g.h5ad + │ ├── MERGE_REPORT.md + │ └── dashboard.html + └── _provenance/ + └── uuid_to_new_name.csv ← 18 行双向映射 +``` + +完整文件命名规则与对照表见 [`database_unified/NAMING_INDEX.md`](../NAMING_INDEX.md) §一-§七。 + +### 2.1 子包技术参数总览 + +| 子包 | Assay 平台 | 大小 | 细胞 / spot | 独立 donors | 基因数 | obsm 嵌入 | raw.X | 疾病维度 | +|---|---|---:|---:|---:|---:|---|:---:|---| +| **A · 健康肝 per-cell-type** | 10x 3'v3 主导（部分 5' tx） | ~2.5 GB | 107,542 | ~12 healthy | 32,596 | `X_umap`, `X_scvi`(some) | ✓ 8/8 | healthy only | +| **B · 全细胞主图谱** | 10x 3'v3 (sc) + 10x sn-3'v3 (sn) | ~3.0 GB | 195,417 | PSC011-PSC03 + PBC003-04 + healthy 对照 | 32,596 | `X_umap`, `X_scvi` | ✓ 2/2 | PSC + PBC + healthy | +| **C · Visium 空间** | Visium Spatial Gene Expression V1 | ~1.4 GB | 34,944 spots (7 × 4992) | healthy-C73 + psc-PSC011 各 1 donor | 35,477 | `spatial` only | ✗ 0/7 | healthy + PSC（无 HCC）| +| **D · GEO NAT raw** | 10x 3' v2 | 53 MB | 13,083 | 1 NAT donor (tumor-adjacent) | 33,694（**gene symbol 不是 Ensembl**） | ∅ | ✓ raw=True，**但矩阵转置**（X.attrs[shape] 是 [genes, cells]） | NAT（有 cancer field effect） | +| **合计 / 4 子包** | 4 种 assay | ~6.96 GB | **349,224** | ~15 donors | 多版本 | 异质 | 19/18（D 矩阵转置）| **4 种 disease level** | + +**重要现状**：所有 18 文件**完全没有 HCC / iCCA / 肿瘤组织** — 当前 Liver_References 实际是"健康肝 + 慢性肝病（PSC/PBC）reference"，**HCC tumor 维度完全缺失**。这正是 Wave 2 的核心补丁。 + +--- + +## 三、每文件 schema 详细档案 + +由于本报告 v1 篇幅约束 + 信息已大量记录在 `NAMING_INDEX.md` 第二节改名对照表（18 行含中文 + UUID + cells × genes），本节给出**简化版**，不重复 NAMING_INDEX 已有的内容。 + +### 3.1 子包 A · 健康肝 per-cell-type 子集（8 份） + +**Publication**：[`10.1016/j.jhep.2023.12.023`](https://doi.org/10.1016/j.jhep.2023.12.023)（Andrews et al.-style 健康肝多模态 atlas；CELLxGENE collection title 含 PSC + PBC + healthy 对照系列） + +8 份 .h5ad 是健康肝的 **per-cell-type 切片**（b-cell / cholangiocyte / endothelial / hepatocyte-v1 / hepatocyte-v2 / lymphoid / macrophage / stellate），细胞数从 1,011（cholangiocyte）到 53,015（hepatocyte-v1）跨两个量级。所有 8 份共享： + +- 32,596 基因（Ensembl IDs，schema-v7 var.feature_id） +- `X_umap` UMAP 坐标 +- 部分含 `X_scvi` latent embedding（scvi-tools 整合时的产物） +- `raw.X = True`（保留 UMI count），下游可重做归一化 + +**Gotcha**：hepatocyte-v1（53k cells）vs hepatocyte-v2（13.6k cells）来自不同 sub-pipeline，**不是子集关系**——上游论文应该给了不同的处理粒度，本数据无法分辨"哪个更权威"。命名上 v1 / v2 中性，不抢决策（详见 NAMING_INDEX §1.2 第一条）。 + +### 3.2 子包 B · 全细胞主图谱（2 份） + +- `sc__psc-pbc-healthy__all-cells__89637cx32596g__7d4d0da4-...` — scRNA-seq 全细胞，含 PSC + PBC + healthy 对照 89,637 cells +- `sn__psc-pbc-healthy__all-cells__105780cx32596g__4b5895d7-...` — snRNA-seq 全核，含同 cohort 105,780 nuclei + +这两份是子包 A 的**反向视图**——A 是按 cell type 切片（每个文件单一 lineage），B 是按全细胞类型联合在一份 .h5ad 里（一个 master file 跨所有 lineage）。**两者细胞 ID 体系一致，可以做 sc/sn cross-modality 对照**。 + +### 3.3 子包 C · Visium 空间转录组（7 份） + +7 份 Visium V1 切片来自两个 donor： +- **healthy-C73**（3 个 block：A1 / C1 / D1）——健康对照供体 +- **psc-PSC011**（4 个 block：A1 / B1 / C1 / D1）——PSC 患者 + +每份 4,992 spots × 35,477 genes，`obsm['spatial']` 存切片坐标。**关键缺陷**：`raw.X = False`——下游做 spot deconvolution（cell2location / RCTD / Tangram）必须从 GEO / Space Ranger 原始 output 补 raw counts。 + +### 3.4 子包 D · GEO 增量 NAT（1 份） + +`sc__healthy-nat__liver__13083cx33694g__GSM4648565.h5ad` — 来自 GSE153643（Regev lab，2021-05），是 HCC tumor-adjacent normal liver（NAT），不是真 healthy。**3 个 gotcha**（详见 NAMING_INDEX §7.3）： + +1. **矩阵转置**：X.attrs[shape] = [33694, N]，obs/_index 装的是基因名，var/_index 装的是 barcode。读后必须 `adata = adata.T` +2. **obs / var 元信息几乎为零**：obs 只有 barcode，var 只有 gene symbol（不是 Ensembl），没有 cell_type / disease / donor_id / suspension_type / assay +3. **是 NAT 不是 healthy**：source 写明 "liver tissue tumor adjacent from liver resection"——有 cancer field effect 风险，**不能跟 healthy donor 数据混用** + +--- + +## 四、Wave 2 计划补齐（HCC scRNA tumor 维度） + +详见 [`_downloads/WAVE2_STATUS.md`](../_downloads/WAVE2_STATUS.md) 与 [`NAMING_INDEX.md`](../NAMING_INDEX.md) §八。当前进度： + +| Wave 2 # | 数据 | accession | 状态（2026-05-17） | 预定文件名（落地后） | +|---|---|---|---|---| +| L1 | Lu 2022 HCC 4-site multisite | GSE149614 | raw staged 1.5 GB · ingest script ready · **需 user host 跑** | `sc__hcc-multisite__lu2022-10pts__71915cx25712g__GSE149614.h5ad` | +| L2 | Sharma 2020 HCC CD45+ Droplet | GSE140228-droplet | raw staged 308 MB（含 ss2 分支）· ingest 未启动 | `sc__hcc-cd45__sharma2020-droplet-16pts__NcxMg__GSE140228-droplet.h5ad` | +| L2b | Sharma 2020 同 series Smart-seq2 | GSE140228-ss2 | 同 L2 staged 53 MB csv | `sc__hcc-cd45__sharma2020-ss2-16pts__NcxMg__GSE140228-ss2.h5ad` | +| L8 | Ma 2021 HCC+iCCA treatment | GSE151530 | raw staged 292 MB · ingest 未启动 | `sc__hcc-iccA-treated__ma2021-37pts__NcxMg__GSE151530.h5ad` | +| L7 | Xue 2022 HCC TIME 124pts | HRA001748 | ⛔ DAC apply pending | `sc__hcc-time__xue2022-124pts__~1Mcx~20kg__HRA001748.h5ad` | + +Wave 2 全落地后 Liver_References 将从**当前 18 个健康/PSC/PBC 文件**扩展为 **23 个文件**（增 4-5 个 HCC scRNA），覆盖 healthy / PSC / PBC / NAT / HCC multisite / HCC immune CD45+ / HCC+iCCA treatment / HCC TIME 共 8 个疾病维度。 + +--- + +## 五、命名规范 + +完整命名规则、改名对照表、GEO/GSA 增量约定见：[`database_unified/NAMING_INDEX.md`](../NAMING_INDEX.md)。 + +简要：`______cxg__.h5ad` +- modality: `sc` / `sn` / `visium` +- cohort 词汇库（详见 NAMING_INDEX §1.1 + §8.1）：包含 `healthy` / `psc-pbc-healthy` / `healthy-C73` / `psc-PSC011` / `healthy-nat` / `hcc-multisite` / `hcc-cd45` / `hcc-iccA` / `hcc-treated` / `hcc-time` 等 +- id: CELLxGENE UUID（36 字符） OR GEO GSM/GSE OR GSA HRA accession + +--- + +## 六、Gotchas（下游使用前必读） + +1. **GSE153643 sample 矩阵转置**（详见 §3.4）——D 子包 1 份 .h5ad 读后必须 `adata.T` +2. **Visium raw.X 缺失**——C 子包 7 份做 deconvolution 必须从 GEO 补 raw counts +3. **Adipose 已拆出独立目录**——GSE153643 同 series 的 `GSM4648564_adipose` 在 `database_unified/Adipose_References/`，不在本目录（避免跨组织污染） +4. **iCCA 驼峰特例**——cohort 槽内 `iccA` 是 §NAMING_INDEX 8.4 的精确例外，其他字段仍全小写 +5. **当前无 HCC tumor 数据**——任何"HCC 相关 reference"使用前，先检查是否需等 Wave 2 L1 / L2 / L8 落地 +6. **C73 是 healthy donor，PSC011 是 PSC 患者**——Visium 文件名内编码了 donor 身份，分析时不要混用 + +--- + +## 七、横向对照 + +| 脏器 References | 文件数 | 总细胞/spot | 子包数 | 疾病覆盖 | HCC tumor? | +|---|---:|---:|---:|---|:---:| +| **Liver**（本目录） | 18（+ 4-5 Wave 2 计划） | 349,224 | 4（+ Wave 2 计划） | healthy / PSC / PBC / NAT | ❌（Wave 2 后 ✅）| +| **Colorectal** | 34 | 4,082,955 | 4 | healthy / IBD / CRC / CRC-Met | n/a (CRC tumor ✅) | +| **Breast** | 38 | (TBD) | 4+ | TNBC / MBC / Pan-cancer TME | (Breast tumor ✅) | +| **Gastric** | 12 | (TBD) | 3 | healthy / GC / iCCA-mixed | (Gastric tumor ✅) | + +完整跨脏器横向对照见各自 STRUCTURE_REPORT，跨脏器 DOWNLOAD 优先级见 [`_provenance/db_search_reports/db_search__{liver,breast,crc,gastric}.md`](../_provenance/db_search_reports/) 4 份子报告。 + +--- + +*v1 生成于 2026-05-17. 触发：Wave 2 启动 + 补齐 STRUCTURE_REPORT gap（之前 4 个 References 中仅 Liver 缺）.* +*下次更新触发：L1 ingest 落地（host run） + L2/L8 ingest 落地（沙盒可跑）.* + +--- + +## 九、Wave 1+2 ingest 实际完成（2026-05-17 同会话） + +本会话沙盒里实际完成了 5 个 dataset ingest（C1 落到 Colorectal_References，其余 4 个落到本目录）。Liver_References 从 18 → 23 文件，新增 5 个 HCC/iCCA tumor reference——**这是 AIVIN Liver 第一次有真正的肿瘤数据**。 + +### 9.1 新增 5 个文件（**3 个新子包 E / F / G**） + +| 子包 | 文件 | 大小 | shape | cells × genes | donors | cohort 字段值 | +|---|---|---:|---|---:|---:|---| +| **E · Ma 2019 HCC/iCCA mixed Cancer Cell** | `sc__hcc-iccA-mixed__ma2019-set1-12pts__5115cx20124g__GSE125449-set1.h5ad` | 22 MB | 5,115 × 20,124 | 5,115 cells | 12 patients (P01-P13 missing P08) | `hcc-iccA-mixed` | +| **E · 同子包** | `sc__hcc-iccA-mixed__ma2019-set2-7pts__4831cx19572g__GSE125449-set2.h5ad` | 16 MB | 4,831 × 19,572 | 4,831 cells | 7 patients | `hcc-iccA-mixed` | +| **F · Ma 2021 HCC/iCCA treatment J Hepatol** | `sc__hcc-iccA-treated__ma2021-46pts__56721cx18667g__GSE151530.h5ad` | 230 MB | 56,721 × 18,667 | 56,721 cells | 46 sample_ids（37 paper patients，含 16 paired pre/post） | `hcc-iccA-treated` | +| **G · Sharma 2020 HCC CD45+ Cell** | `sc__hcc-iccA-cd45__sharma2020-droplet-5pts__66187cx54574g__GSE140228-droplet.h5ad` | 192 MB | 66,187 × 54,574 | 66,187 cells | 5 donors | `hcc-iccA-cd45`（**实测 5 donors HCC + CC mixed**，db_search 写的 "16 patients" 不准确 — 16 是含 ss2 sub-cohort 加 fetal 的总数）| +| **G · 同子包 ss2 分支** | `sc__hcc-cd45-ss2__sharma2020-ss2-6pts__7074cx54574g__GSE140228-ss2.h5ad` | 90 MB | 7,074 × 54,574 | 7,074 cells | 6 donors（与 droplet 部分重叠：D20171109） | `hcc-cd45-ss2` | + +### 9.2 子包对照（**v1 vs v2**） + +| 子包 | 文件数 v1 → v2 | cells v1 → v2 | 新增内容 | +|---|:---:|:---:|---| +| A · 健康肝 per-cell-type | 8 → 8 | 107,542 → 107,542 | 无变化 | +| B · 全细胞主图谱 | 2 → 2 | 195,417 → 195,417 | 无变化 | +| C · Visium 空间 | 7 → 7 | 34,944 → 34,944 | 无变化 | +| D · GEO NAT raw | 1 → 1 | 13,083 → 13,083 | 无变化 | +| **E · Ma 2019 HCC/iCCA mixed** | 0 → **2** | 0 → **9,946** | 新增（HCC + iCCA 共 cohort）| +| **F · Ma 2021 HCC/iCCA treatment** | 0 → **1** | 0 → **56,721** | 新增（含治疗前后 paired sub-cohort）| +| **G · Sharma 2020 HCC CD45+** | 0 → **2** | 0 → **73,261** | 新增（droplet + ss2 双平台，CD45+ 免疫富集）| +| **合计** | 18 → **23** | 349,224 → **489,152** | +5 文件 +139,928 cells | + +### 9.3 疾病维度扩展 + +| v1 (2026-05-15) | v2 (2026-05-17) | +|---|---| +| healthy, PSC, PBC, NAT | healthy, PSC, PBC, NAT, **HCC, iCCA, HCC-CC mixed, HCC pre/post treatment** | + +**最关键转变**：Liver_References 终于有了真正的肿瘤参考数据。下游做 cancer vs normal differential、TME 解析、tumor heterogeneity 都可以基于本目录内 self-contained 完成。 + +### 9.4 命名规范使用的新 cohort 词 + +本会话引入了 4 个新 cohort 词（已写入 §八 8.1 cohort 词汇库）： +- `hcc-iccA-mixed` — HCC + iCCA 同 cohort 无治疗信息（Ma 2019） +- `hcc-iccA-treated` — HCC + iCCA + 治疗前后维度（Ma 2021） +- `hcc-iccA-cd45` — HCC + iCCA + CD45+ 富集（Sharma droplet） +- `hcc-cd45-ss2` — HCC + CD45+ 富集 + Smart-seq2 平台（Sharma ss2） + +iCCA 驼峰特例（§八 8.4 gotcha #3）在 3 个新 cohort 词里被使用，与 §一 全小写原则的精确例外是一致的。 + +### 9.5 待 PDF / DAC 后的 v2 enrichment + +| 文件 | 缺什么 v1 字段 | 哪里来 | +|---|---|---| +| L3 Set1/Set2 | `cancer_type` (HCC vs iCCA per patient_code LCP##) | Ma 2019 *Cancer Cell* paper Supplementary Table 1 | +| L8 Ma 2021 | `cancer_type` (HCC vs iCCA), `timepoint` (pre/post), `treatment_type`, `treatment_response` | Ma 2021 *J Hepatol* paper Supplementary Table 1 | +| L2 / L2b | （fetal liver sub-cohort 在哪里？）| Sharma 2020 *Cell* paper 全文 + 可能 sample 名字解码 | + +详见 `_downloads/WAVE2_STATUS.md` §1.2 用户下一步操作清单 + `literature/A_cancer_TME/methods_extracts/{12,13}_*.md` 的 v2 enrichment 字段清单。 + +### 9.6 还没 ingest 的：L1 Lu 2022 + +L1 Lu 2022 HCC multisite (71,915 cells × 25,712 genes, 4 sites NTL/PT/PVTT/MLN) **沙盒 ingest 失败**（dense 71,915-col matrix pandas chunked read 超 45s/chunk × 32 chunks），已写 host-runnable script `scripts/L1_lu2022_ingest.py`，user 在 Mac 跑 3-8 分钟即可落地。 + +--- + +*v2 生成于 2026-05-17 · 沙盒同会话完成 5/6 P0 dataset ingest（4 个落 Liver + 1 个落 Colorectal）· Liver 子包数 4 → 7 · cells 349k → 489k · 疾病维度 4 类 → 8 类。L1 host script + L7 DAC pending。* diff --git a/_audit_report.md b/_audit_report.md new file mode 100644 index 0000000000000000000000000000000000000000..96fd240519f4607c29c4cca24a56f3116a717f60 --- /dev/null +++ b/_audit_report.md @@ -0,0 +1,55 @@ +# AIVIN h5ad Audit Report + +**Generated**: 2026-05-25T23:53:29.045338+00:00 + +**Files audited**: 40 + +**Pass**: 22 / 40 + +**View-only**: 3 / 40 + + +## Per-file summary + +| File | Pass | Shape | X state | uns OK | view-only | Notes | +|---|---|---|---|---|---|---| +| `sc__cld-lyec__tamburini2019-4pts__901cx11302g__GSE129933.h5ad` | ✓ | 901×11302 | raw_counts | 0/9 | | no patient/donor obs column found (Patient/patient_id/donor/subject); uns missing required | +| `sc__hcc-cd45-ss2__sharma2020-ss2-6pts__7074cx54574g__GSE140228-ss2.h5ad` | ✓ | 7074×54574 | unknown | 0/9 | 👁 | no patient/donor obs column found (Patient/patient_id/donor/subject); X state unknown — re | +| `sc__hcc-cd45__guo2025-9pts__191435cx28671g__GSE235863-9pt.h5ad` | ✓ | 191435×28671 | raw_counts | 12/9 | | | +| `sc__hcc-cd8tcell__guo2025-5pts__95408cx26256g__GSE235863-5pt.h5ad` | ✓ | 95408×26256 | raw_counts | 12/9 | | | +| `sc__hcc-fetal__sharma2020-62pts__109238cx2384g__GSE156625-HCCF.h5ad` | ✓ | 109238×2384 | scaled_HVG | 13/9 | 👁 | | +| `sc__hcc-iccA-cd45__sharma2020-droplet-5pts__66187cx54574g__GSE140228-droplet.h5ad` | ✓ | 66187×54574 | raw_counts | 0/9 | | no patient/donor obs column found (Patient/patient_id/donor/subject); uns missing required | +| `sc__hcc-iccA-mixed__ma2019-set1-12pts__5115cx20124g__GSE125449-set1.h5ad` | ✓ | 5115×20124 | raw_counts | 0/9 | | uns missing required keys: ['cohort_tag', 'ingest_date', 'ingested_by', 'modality', 'sourc | +| `sc__hcc-iccA-mixed__ma2019-set2-7pts__4831cx19572g__GSE125449-set2.h5ad` | ✓ | 4831×19572 | raw_counts | 0/9 | | uns missing required keys: ['cohort_tag', 'ingest_date', 'ingested_by', 'modality', 'sourc | +| `sc__hcc-iccA-treated__ma2021-46pts__56721cx18667g__GSE151530.h5ad` | ✓ | 56721×18667 | raw_counts | 0/9 | | no patient/donor obs column found (Patient/patient_id/donor/subject); uns missing required | +| `sc__hcc-mash-spectrum__huang2025-4pts__34396cx32285g__GSE282630.h5ad` | ✓ | 34396×32285 | raw_counts | 10/9 | | no patient/donor obs column found (Patient/patient_id/donor/subject); uns missing required | +| `sc__hcc-multisite__lu2022-10pts__71915cx25712g__GSE149614.h5ad` | ✓ | 71915×25712 | raw_counts | 0/9 | | uns missing required keys: ['cohort_tag', 'ingest_date', 'ingested_by', 'modality', 'sourc | +| `sc__hcc-trm__park2025-7pts__41848cx36601g__GSE281110.h5ad` | ✓ | 41848×36601 | raw_counts | 10/9 | | patient count mismatch: filename=7, obs.patient=6; uns missing required keys: ['x_state'] | +| `sc__hcc-tumor-normal__sharma2020-58pts__73589cx2608g__GSE156625-HCC.h5ad` | ✓ | 73589×2608 | scaled_HVG | 13/9 | 👁 | | +| `sc__healthy-nat__liver__13083cx33694g__GSM4648565.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: sc__healthy-nat__liver__13083cx33694g__GSM464856 | +| `sc__healthy__b-cell__1250cx32596g__6ade4ff5-368c-4276-b051-818dc954da6d.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: sc__healthy__b-cell__1250cx32596g__6ade4ff5-368c | +| `sc__healthy__cholangiocyte__1011cx32596g__601ef580-74ce-4a87-96e8-8e22bf4ed9fa.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: sc__healthy__cholangiocyte__1011cx32596g__601ef5 | +| `sc__healthy__endothelial__9422cx32596g__a95e1659-9a48-4c55-8062-621ee4df9160.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: sc__healthy__endothelial__9422cx32596g__a95e1659 | +| `sc__healthy__hepatocyte-v1__53015cx32596g__63137dcf-6236-464d-9018-e58c9323f59c.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: sc__healthy__hepatocyte-v1__53015cx32596g__63137 | +| `sc__healthy__hepatocyte-v2__13635cx32596g__6b241dde-25c0-4edc-9021-03603ec3a524.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: sc__healthy__hepatocyte-v2__13635cx32596g__6b241 | +| `sc__healthy__lymphoid__16665cx32596g__9bc7506b-7e73-4cea-bbb3-3603a016fbca.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: sc__healthy__lymphoid__16665cx32596g__9bc7506b-7 | +| `sc__healthy__macrophage__11127cx32596g__2cfec927-9163-4684-ae04-c15175a6d781.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: sc__healthy__macrophage__11127cx32596g__2cfec927 | +| `sc__healthy__stellate__1417cx32596g__08a9f031-1e76-405a-8050-0635743ce187.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: sc__healthy__stellate__1417cx32596g__08a9f031-1e | +| `sc__psc-pbc-healthy__all-cells__89637cx32596g__7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: sc__psc-pbc-healthy__all-cells__89637cx32596g__7 | +| `sn__hcc-tumor-normal__alvarez2022-1pts__39995cx58100g__GSE189175.h5ad` | ✓ | 39995×58100 | raw_counts | 10/9 | | var_names not unique: 1748 duplicates; patient count mismatch: filename=1, obs.Patient=3; | +| `sn__hcc-tumor-normal__alvarez2022-3pts__39995cx58100g__GSE189175.h5ad` | ✓ | 39995×58100 | raw_counts | 13/9 | | | +| `sn__psc-pbc-healthy__all-cells__105780cx32596g__4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: sn__psc-pbc-healthy__all-cells__105780cx32596g__ | +| `visium__hcc-antiPD1__liu2025-1pts__1320cx36601g__GSE238264-HCC5NR.h5ad` | ✓ | 1320×36601 | raw_counts | 10/9 | | no patient/donor obs column found (Patient/patient_id/donor/subject); uns missing required | +| `visium__hcc-antiPD1__liu2025-1pts__2170cx36601g__GSE238264-HCC3R.h5ad` | ✓ | 2170×36601 | raw_counts | 10/9 | | no patient/donor obs column found (Patient/patient_id/donor/subject); uns missing required | +| `visium__hcc-antiPD1__liu2025-1pts__2453cx36601g__GSE238264-HCC7NR.h5ad` | ✓ | 2453×36601 | raw_counts | 10/9 | | no patient/donor obs column found (Patient/patient_id/donor/subject); uns missing required | +| `visium__hcc-antiPD1__liu2025-1pts__2575cx36601g__GSE238264-HCC6NR.h5ad` | ✓ | 2575×36601 | raw_counts | 10/9 | | no patient/donor obs column found (Patient/patient_id/donor/subject); uns missing required | +| `visium__hcc-antiPD1__liu2025-1pts__2766cx36601g__GSE238264-HCC2R.h5ad` | ✓ | 2766×36601 | raw_counts | 10/9 | | no patient/donor obs column found (Patient/patient_id/donor/subject); uns missing required | +| `visium__hcc-antiPD1__liu2025-1pts__3002cx36601g__GSE238264-HCC4R.h5ad` | ✓ | 3002×36601 | raw_counts | 10/9 | | no patient/donor obs column found (Patient/patient_id/donor/subject); uns missing required | +| `visium__hcc-antiPD1__liu2025-1pts__3006cx36601g__GSE238264-HCC1R.h5ad` | ✓ | 3006×36601 | raw_counts | 10/9 | | no patient/donor obs column found (Patient/patient_id/donor/subject); uns missing required | +| `visium__healthy-C73__blockA1__4992sx35477g__9c731db0-9f44-4f8c-8139-9b9af2bcc782.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: visium__healthy-C73__blockA1__4992sx35477g__9c73 | +| `visium__healthy-C73__blockC1__4992sx35477g__b2287ef1-eac3-49cc-93de-65df74e26a61.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: visium__healthy-C73__blockC1__4992sx35477g__b228 | +| `visium__healthy-C73__blockD1__4992sx35477g__44453de7-1d66-4bd1-a83d-0b73a1690d57.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: visium__healthy-C73__blockD1__4992sx35477g__4445 | +| `visium__psc-PSC011__blockA1__4992sx35477g__7bc39166-b664-4c92-922d-f9a8047768d2.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: visium__psc-PSC011__blockA1__4992sx35477g__7bc39 | +| `visium__psc-PSC011__blockB1__4992sx35477g__6da751d4-63af-43b8-96db-395ca73dfb5f.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: visium__psc-PSC011__blockB1__4992sx35477g__6da75 | +| `visium__psc-PSC011__blockC1__4992sx35477g__0cc59004-2b35-4767-8278-83e097ef32d1.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: visium__psc-PSC011__blockC1__4992sx35477g__0cc59 | +| `visium__psc-PSC011__blockD1__4992sx35477g__721bfc1c-f77f-4c5a-afc6-04e3e3c675d3.h5ad` | ✗ | ?×? | ? | 0/9 | | filename does not match AIVIN convention: visium__psc-PSC011__blockD1__4992sx35477g__721bf | \ No newline at end of file diff --git a/_provenance/uuid_to_new_name.csv b/_provenance/uuid_to_new_name.csv new file mode 100644 index 0000000000000000000000000000000000000000..09ec6c8301e07e1afaa46830c61cc3e1f63628f8 --- /dev/null +++ b/_provenance/uuid_to_new_name.csv @@ -0,0 +1,24 @@ +old_uuid_filename,new_filename,size_mb,uuid_full,uuid8,cellxgene_dataset_url +08a9f031-1e76-405a-8050-0635743ce187.h5ad,sc__healthy__stellate__1417cx32596g__08a9f031-1e76-405a-8050-0635743ce187.h5ad,14.7,08a9f031-1e76-405a-8050-0635743ce187,08a9f031,https://datasets.cellxgene.cziscience.com/08a9f031-1e76-405a-8050-0635743ce187.h5ad +0cc59004-2b35-4767-8278-83e097ef32d1.h5ad,visium__psc-PSC011__blockC1__4992sx35477g__0cc59004-2b35-4767-8278-83e097ef32d1.h5ad,53.3,0cc59004-2b35-4767-8278-83e097ef32d1,0cc59004,https://datasets.cellxgene.cziscience.com/0cc59004-2b35-4767-8278-83e097ef32d1.h5ad +2cfec927-9163-4684-ae04-c15175a6d781.h5ad,sc__healthy__macrophage__11127cx32596g__2cfec927-9163-4684-ae04-c15175a6d781.h5ad,93.7,2cfec927-9163-4684-ae04-c15175a6d781,2cfec927,https://datasets.cellxgene.cziscience.com/2cfec927-9163-4684-ae04-c15175a6d781.h5ad +44453de7-1d66-4bd1-a83d-0b73a1690d57.h5ad,visium__healthy-C73__blockD1__4992sx35477g__44453de7-1d66-4bd1-a83d-0b73a1690d57.h5ad,1096.9,44453de7-1d66-4bd1-a83d-0b73a1690d57,44453de7,https://datasets.cellxgene.cziscience.com/44453de7-1d66-4bd1-a83d-0b73a1690d57.h5ad +4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad,sn__psc-pbc-healthy__all-cells__105780cx32596g__4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad,1543.8,4b5895d7-6d92-471a-b13a-5c59a000ddc4,4b5895d7,https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +601ef580-74ce-4a87-96e8-8e22bf4ed9fa.h5ad,sc__healthy__cholangiocyte__1011cx32596g__601ef580-74ce-4a87-96e8-8e22bf4ed9fa.h5ad,16.7,601ef580-74ce-4a87-96e8-8e22bf4ed9fa,601ef580,https://datasets.cellxgene.cziscience.com/601ef580-74ce-4a87-96e8-8e22bf4ed9fa.h5ad +63137dcf-6236-464d-9018-e58c9323f59c.h5ad,sc__healthy__hepatocyte-v1__53015cx32596g__63137dcf-6236-464d-9018-e58c9323f59c.h5ad,439.8,63137dcf-6236-464d-9018-e58c9323f59c,63137dcf,https://datasets.cellxgene.cziscience.com/63137dcf-6236-464d-9018-e58c9323f59c.h5ad +6ade4ff5-368c-4276-b051-818dc954da6d.h5ad,sc__healthy__b-cell__1250cx32596g__6ade4ff5-368c-4276-b051-818dc954da6d.h5ad,18.1,6ade4ff5-368c-4276-b051-818dc954da6d,6ade4ff5,https://datasets.cellxgene.cziscience.com/6ade4ff5-368c-4276-b051-818dc954da6d.h5ad +6b241dde-25c0-4edc-9021-03603ec3a524.h5ad,sc__healthy__hepatocyte-v2__13635cx32596g__6b241dde-25c0-4edc-9021-03603ec3a524.h5ad,85.1,6b241dde-25c0-4edc-9021-03603ec3a524,6b241dde,https://datasets.cellxgene.cziscience.com/6b241dde-25c0-4edc-9021-03603ec3a524.h5ad +6da751d4-63af-43b8-96db-395ca73dfb5f.h5ad,visium__psc-PSC011__blockB1__4992sx35477g__6da751d4-63af-43b8-96db-395ca73dfb5f.h5ad,52.3,6da751d4-63af-43b8-96db-395ca73dfb5f,6da751d4,https://datasets.cellxgene.cziscience.com/6da751d4-63af-43b8-96db-395ca73dfb5f.h5ad +721bfc1c-f77f-4c5a-afc6-04e3e3c675d3.h5ad,visium__psc-PSC011__blockD1__4992sx35477g__721bfc1c-f77f-4c5a-afc6-04e3e3c675d3.h5ad,44.6,721bfc1c-f77f-4c5a-afc6-04e3e3c675d3,721bfc1c,https://datasets.cellxgene.cziscience.com/721bfc1c-f77f-4c5a-afc6-04e3e3c675d3.h5ad +7bc39166-b664-4c92-922d-f9a8047768d2.h5ad,visium__psc-PSC011__blockA1__4992sx35477g__7bc39166-b664-4c92-922d-f9a8047768d2.h5ad,61.8,7bc39166-b664-4c92-922d-f9a8047768d2,7bc39166,https://datasets.cellxgene.cziscience.com/7bc39166-b664-4c92-922d-f9a8047768d2.h5ad +7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad,sc__psc-pbc-healthy__all-cells__89637cx32596g__7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad,1119.0,7d4d0da4-655e-438a-a2ec-b4371e2b80fc,7d4d0da4,https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +9bc7506b-7e73-4cea-bbb3-3603a016fbca.h5ad,sc__healthy__lymphoid__16665cx32596g__9bc7506b-7e73-4cea-bbb3-3603a016fbca.h5ad,127.7,9bc7506b-7e73-4cea-bbb3-3603a016fbca,9bc7506b,https://datasets.cellxgene.cziscience.com/9bc7506b-7e73-4cea-bbb3-3603a016fbca.h5ad +9c731db0-9f44-4f8c-8139-9b9af2bcc782.h5ad,visium__healthy-C73__blockA1__4992sx35477g__9c731db0-9f44-4f8c-8139-9b9af2bcc782.h5ad,1108.6,9c731db0-9f44-4f8c-8139-9b9af2bcc782,9c731db0,https://datasets.cellxgene.cziscience.com/9c731db0-9f44-4f8c-8139-9b9af2bcc782.h5ad +a95e1659-9a48-4c55-8062-621ee4df9160.h5ad,sc__healthy__endothelial__9422cx32596g__a95e1659-9a48-4c55-8062-621ee4df9160.h5ad,77.7,a95e1659-9a48-4c55-8062-621ee4df9160,a95e1659,https://datasets.cellxgene.cziscience.com/a95e1659-9a48-4c55-8062-621ee4df9160.h5ad +b2287ef1-eac3-49cc-93de-65df74e26a61.h5ad,visium__healthy-C73__blockC1__4992sx35477g__b2287ef1-eac3-49cc-93de-65df74e26a61.h5ad,1172.4,b2287ef1-eac3-49cc-93de-65df74e26a61,b2287ef1,https://datasets.cellxgene.cziscience.com/b2287ef1-eac3-49cc-93de-65df74e26a61.h5ad +GSM4648565_liver_raw_counts.h5ad,sc__healthy-nat__liver__13083cx33694g__GSM4648565.h5ad,120.5,GSM4648565,GSM4648565,https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4648565 +GSE125449_Set1_matrix.mtx.gz,sc__hcc-iccA-mixed__ma2019-set1-12pts__5115cx20124g__GSE125449-set1.h5ad,21.4,GSE125449-set1,sc-10x,https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE125449 +GSE125449_Set2_matrix.mtx.gz,sc__hcc-iccA-mixed__ma2019-set2-7pts__4831cx19572g__GSE125449-set2.h5ad,15.7,GSE125449-set2,sc-10x,https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE125449 +GSE151530_matrix.mtx.gz,sc__hcc-iccA-treated__ma2021-46pts__56721cx18667g__GSE151530.h5ad,229.8,GSE151530,sc-10x,https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151530 +GSE140228_UMI_counts_Droplet.mtx.gz,sc__hcc-iccA-cd45__sharma2020-droplet-5pts__66187cx54574g__GSE140228-droplet.h5ad,192.3,GSE140228-droplet,sc-10x,https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE140228 +GSE140228_read_counts_Smartseq2.csv.gz,sc__hcc-cd45-ss2__sharma2020-ss2-6pts__7074cx54574g__GSE140228-ss2.h5ad,90.0,GSE140228-ss2,sc-smartseq2,https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE140228 diff --git a/_view_only_list.md b/_view_only_list.md new file mode 100644 index 0000000000000000000000000000000000000000..7b2af33af76e3311c1ffe36647d65634ce52c7b7 --- /dev/null +++ b/_view_only_list.md @@ -0,0 +1,71 @@ +# Liver_References · View-Only List + +**Date**: 2026-05-25 +**目的**: 标记本目录下**已 ingest 但 X/X.raw 都不是 integer raw counts** 的 .h5ad 文件——这些文件可以做 UMAP / clustering / 看 marker 表达 / cell-type 注释验证，但**不能直接用于**： + +- DESeq2-style 差异表达（需 raw integer counts） +- 重新 normalize / batch correction（已经被作者 scale 过） +- 整合到 scVI / Scanorama / Harmony 的 raw-counts pipeline（数值范围不对） + +下游 census 整合时**这些文件应该被 skip**，或者从原始 GEO mtx 重 ingest 后替换。 + +--- + +## ⚠️ 当前 View-Only 文件清单 + +| 文件 | X 状态 | .raw.X 状态 | 真 raw counts 来源 | +|---|---|---|---| +| `sc__hcc-tumor-normal__sharma2020-58pts__73589cx2608g__GSE156625-HCC.h5ad` | scaled HVG (2,608 genes, neg values −2.1~10) | log-normalized full (19,852 genes, max 7.0) | `_staging/wave2_geo/GSE156625/HCCmatrix.mtx.gz` (562 MB) + HCCbarcodes + HCCgenes | +| `sc__hcc-fetal__sharma2020-62pts__109238cx2384g__GSE156625-HCCF.h5ad` | scaled HVG (2,384 genes, neg values −1.3~10) | log-normalized full (20,417 genes, max 6.3) | `_staging/wave2_geo/GSE156625/HCCFmatrix.mtx.gz` (687 MB) + HCCFbarcodes + HCCFgenes | + +--- + +## 怎么"反悔" — 拿回真 raw counts + +两个文件源自 GEO GSE156625 (Sharma 2020 Onco-fetal Cell paper)。raw 10x matrix 在 staging 里已经下好： + +```bash +ls /Users/zhongjunfu/Desktop/项目/AIVIN/database_unified/_staging/wave2_geo/GSE156625/ +# GSE156625_HCCbarcodes.tsv.gz 129M cell barcodes +# GSE156625_HCCgenes.tsv.gz 259K gene symbols +# GSE156625_HCCmatrix.mtx.gz 562M integer counts +# GSE156625_HCCFbarcodes.tsv.gz 138M +# GSE156625_HCCFgenes.tsv.gz 259K +# GSE156625_HCCFmatrix.mtx.gz 687M +``` + +重 ingest 用 `scripts/ingest_wave2_remaining.py` 的 `_read_10x_triplet()` 函数即可——已经有现成代码。重 ingest 之前先备份现 .h5ad（含 louvain / UMAP / cell type annotations）到 `derived_views/sharma2020_processed_view/` 保留作者的预处理产物。 + +--- + +## 防御性使用规则（团队约定） + +任何下游脚本读取 Liver_References 时，应先检查 `uns.x_state`： + +```python +import anndata as ad +a = ad.read_h5ad(fp, backed='r') +if a.uns.get("x_state") in ("scaled_HVG", "scaled"): + # 这是 view-only file + if "raw_state" in a.uns and a.uns["raw_state"].startswith("log_normalized"): + # 可以用 .raw 做 score_genes 但不能做 DE + ... + else: + # 不应用作 DE / re-normalize 输入 + raise ValueError(f"{fp.name} has scaled X, no raw counts — re-ingest from _staging/") +``` + +或者读取本目录的 `_view_only_list.md`（本文档）跑前过滤。 + +--- + +## 未来加入此清单的判定规则 + +新 ingest 的 .h5ad 应该自动跑 `scripts/verify_h5ad.py --report`，若发现： + +- `.X` 含负值 → scaled，自动加入 view-only +- `.X` 全为整数 + sparsity ≥ 90% → raw counts，正常 +- `.X` 全为浮点 [0, 10] + sparsity ≥ 80% → log-normalized，**borderline**（看具体场景） +- `.X` 列数远小于 `.raw.X` 列数 → HVG subset 而非全基因，view-only + +详见 `scripts/verify_h5ad.py` 自动判定逻辑。 diff --git a/merged_views/MERGE_REPORT.md b/merged_views/MERGE_REPORT.md new file mode 100644 index 0000000000000000000000000000000000000000..a98396a24e78bac12e50efe53661da290ab88cae --- /dev/null +++ b/merged_views/MERGE_REPORT.md @@ -0,0 +1,190 @@ +# MERGE_REPORT · Liver_References 同类项合并 + +**生成日期**：2026-05-15 +**对象目录**：`database_unified/Liver_References/merged_views/` +**合并范围**：Liver_References 下"同性质"原文件的派生整合视图——三类合并对应 sc healthy（per-cell-type 子集统一）/ visium healthy（C73 三切片统一）/ visium PSC（PSC011 四切片统一）。 +**硬约束**：**原 17 份文件未做任何修改**；本目录只放派生视图，与原文件平级独立存在。 + +--- + +## 一、合并范围与三个派生输出 + +| # | 合并类型 | 输入（原文件数）| 输出文件 | 输出体量 | 输出 cells/spots | +|---|---|---:|---|---:|---:| +| 1 | sc healthy per-cell-type 统一 | 8 | `sc__healthy__merged-all-celltypes__107542cx32596g.h5ad` | 319.3 MB | 107,542 cells × 32,596 genes | +| 2 | visium healthy C73 三切片统一 | 3 | `visium__healthy-C73__merged-all-blocks__14976sx35477g.h5ad` | 57.1 MB | 14,976 spots × 35,477 genes | +| 3 | visium PSC PSC011 四切片统一 | 4 | `visium__psc-PSC011__merged-all-blocks__19968sx35477g.h5ad` | 90.5 MB | 19,968 spots × 35,477 genes | + +**总计**：15 个原文件参与了 3 次合并（剩下 2 个 all-cells 文件未参与合并——它们各自就是独立的全集，没有"同类项"可合）。所有 3 个派生输出共 466.9 MB，相对于原 6.96 GB 总体积是约 **6.7%** 的派生层占比。 + +--- + +## 二、为什么是这三个合并组、为什么有 2 个文件不进合并 + +### 进合并的 3 组同类项 + +**组 1 · sc healthy per-cell-type** 是 Liver_References 子包 A 的全部 8 份——B 细胞 / 胆管细胞 / 内皮 / 肝细胞 v1 / 肝细胞 v2 / 淋巴样 / 巨噬 / 肝星状。它们的共同点是 `modality=sc` + `cohort=healthy` + 上游已分 cell type 切片好。把这 8 份合并的物理动机是**做一份"健康肝脏完整 sc 参考"**——下游做"任意细胞类型在 BOVIN 82 节点空间里的分布"或者"健康肝 vs 病肝细胞类型组成对比"时，单一文件比 8 文件方便得多。 + +**组 2 · visium healthy** 是 C73 健康捐献者的 3 个连续切片（A1 / C1 / D1）。它们都是同一名供体在同一时段的不同物理切片，**做合并是为了把"C73 这个人的肝组织空间表达"作为一个整体处理**，下游做"healthy liver lobule 的标志区域"或"C73 内部空间一致性"分析时无需手动循环 3 文件。 + +**组 3 · visium PSC** 是 PSC011 患者的 4 个连续切片（A1 / B1 / C1 / D1）。逻辑同组 2——这是 PSC011 这一名患者的"完整 Visium 病例"。 + +### 不进合并的 2 个文件 + +**子包 B 的 2 个 all-cells 文件**（`sc__psc-pbc-healthy__all-cells__89637c...` 与 `sn__psc-pbc-healthy__all-cells__105780c...`）**各自已是同类全集**——它们已经在上游 paper 里跨 PSC + PBC + healthy 全部供体做完了集成，**没有"同类项"可合**。强行把它们与本批合并产物再合并不仅没有信息增益，反而会引入 sc-vs-sn 模态混淆。**留作独立的全集参考**，自己单独使用。 + +如果将来想做"sc 全集（B-1）+ sc healthy per-cell-type（合并组 1）+ sn 全集（B-2）"的跨模态统一图谱，那是 v2 合并任务，需要 batch correction（如 Harmony / scVI）介入，不在本次"同类项合并"的 scope 里。 + +--- + +## 三、每个合并都做了什么 + 没做什么（透明记录） + +### 合并组 1 · sc healthy per-cell-type + +**做了的事**： + +读入 8 个原文件的 `X`（归一化表达）+ `obs`（细胞元信息）+ `var`（基因列表，8 份完全一致）。对每个细胞**加 3 列 obs provenance**——`source_subset`（如 `stellate` / `hepatocyte-v1`）、`source_uuid8`（原文件 UUID 前 8 位）、`cell_barcode_original`（原 cell barcode 备份）。细胞 index **加前缀 `|` 防止 barcode 重复**（不同 subset 可能用同一 barcode）。用 `anndata.concat(axis=0, join='inner', merge='unique')` 在基因维上做交集合并（实际上 8 份 var 完全一致，inner = outer）。`uns['merge_provenance']` 记录合并来源、日期、原因、注意事项。 + +**没做的事**： + +第一，**没有把 raw counts（`raw.X`）打包进合并文件**——原 8 文件各自的 raw counts 仍在原文件 `raw.X` 里。这个取舍是为了控制合并文件体积 + 合并耗时（原计划包含但耗时超 45s）。需要 raw counts 做自定义归一化的用户，请回到 `Liver_References/` 的原文件按 source_uuid8 反查。 + +第二，**没有保留 per-celltype 的 `obsm` 嵌入**（X_pca / X_harmony / X_umap / X_varimax）——这些嵌入是在**每个 cell type 子集内部**计算的（即"只在肝细胞群内做的 UMAP"vs"只在巨噬群内做的 UMAP"），跨 cell type 没有可比性。合并后保留它们反而会误导分析。下游用户应在合并对象上**重新跑 PCA + Harmony + UMAP**（标准 scanpy 流程，约 5–10 分钟在 32 GB RAM 机器上）。 + +第三，**没有做 batch correction**——8 个原文件用了不同的 10x 化学（3' v2 / 3' v3 / 5' v1）。合并对象的 `X` 直接是原归一化值串接，跨 subset 比较时存在化学批次效应。要做精细的跨 subset 分析建议跑 Harmony 用 `assay` 字段作 batch key。 + +### 合并组 2 · visium healthy C73 三切片 + +**做了的事**： + +用 `h5py` 直接读 `X` / `obs` / `var` / `obsm`（绕过 anndata `read_h5ad` 自动加载 uns 的开销——原文件 uns 含 2.6 GB fullres 图像）。每个 spot 加 3 列 obs provenance——`block_id`（`blockA1` / `blockC1` / `blockD1`）、`source_uuid8`、`cell_barcode_original`。spot index 加前缀 `|` 防重复。3 文件 var 完全一致（35,477 基因），inner join 直接 concat。`uns['spatial']` 用**字典形式**保留三个 library 的 hires 缩略图 + scalefactors——这是 scanpy/squidpy 处理 multi-library Visium 的标准格式。 + +**没做的事**： + +第一，**fullres 图像（每切片 2.6 GB，3 切片共 ~8 GB）从合并文件里 drop 掉了**——`uns['spatial'][]['images']` 只保留 `hires` 缩略图（每切片 ~12 MB，3 切片 ~36 MB）。原文件 fullres 完整保留。如果下游分析需要 fullres（如做高分辨率细胞分割），请按 `source_uuid8` 回原文件取。 + +第二，**没有做 Visium spot 之间的跨切片对齐**——3 个切片在物理上是连续的（A1/C1/D1 是同一组织块上不同位置），但它们的 `obsm['spatial']` 坐标是各切片独立的 pixel 坐标系，跨切片直接比较坐标无意义。下游想做"跨切片 3D 对齐"需要专门的 spatial registration 算法（如 PASTE / STalign），不在合并 scope 里。 + +第三，**没有做 spot 级别的 H&E 图像融合**——3 切片各自有独立的 hires 病理图，合并对象只是把它们字典存放，没有拼接。视化时可以并排展示，但物理对齐需要后处理。 + +### 合并组 3 · visium PSC PSC011 四切片 + +技术处理与合并组 2 完全相同。唯一差异：4 个 PSC011 原文件**不含 fullres 图像**（只有 hires），所以合并时没有 fullres 可以 drop。合并文件保留全部 4 个 library 的 hires 图像 + scalefactors。 + +--- + +## 四、合并文件的 obs schema（统一约定） + +合并后的所有 3 个文件遵循统一的 provenance schema： + +| 列名 | 含义 | 出现在哪个合并文件 | +|---|---|---| +| `source_subset` | 该细胞 / spot 原属哪个 sub-type（如 `stellate`, `hepatocyte-v1`） | sc healthy | +| `block_id` | 该 spot 原属哪个切片（`blockA1` / `blockB1` / `blockC1` / `blockD1`） | visium healthy + PSC | +| `source_uuid8` | 该细胞 / spot 原文件 UUID 前 8 位（用于反查 `_provenance/uuid_to_new_name.csv`） | 所有 3 个合并文件 | +| `cell_barcode_original` | 原始 cell barcode（合并后被前缀化以保证唯一性，此列保留原值） | 所有 3 个合并文件 | +| 其余 obs 列 | 直接继承自原文件（disease / tissue / cell_type / donor_id 等） | 所有 3 个合并文件 | + +合并文件的 cell/spot index 格式：`|`，全局唯一。 + +--- + +## 五、对账与完整性核验 + +### 数量对账 + +``` +合并组 1 · sc healthy: + b-cell: 1,250 + cholangiocyte: 1,011 + endothelial: 9,422 + hepatocyte-v1: 53,015 + hepatocyte-v2: 13,635 + lymphoid: 16,665 + macrophage: 11,127 + stellate: 1,417 + ----------------------- + 合并文件总细胞数: 107,542 ✅ 与 8 文件细胞数之和一致 + +合并组 2 · visium healthy: + blockA1: 4,992 + blockC1: 4,992 + blockD1: 4,992 + ----------------------- + 合并文件总 spot 数: 14,976 ✅ 与 3 文件 spot 数之和一致 + +合并组 3 · visium PSC: + blockA1: 4,992 + blockB1: 4,992 + blockC1: 4,992 + blockD1: 4,992 + ----------------------- + 合并文件总 spot 数: 19,968 ✅ 与 4 文件 spot 数之和一致 +``` + +### 原文件完整性 + +合并完成后，`Liver_References/` 下 17 个原 `.h5ad` 文件**逐一核对未被修改**（mtime 未变、size_mb 未变）。如有怀疑可对照 `_provenance/uuid_to_new_name.csv` 的 `size_mb` 列做完整性核对。 + +### 基因列表一致性 + +- sc healthy 合并：8 文件 var 完全一致（32,596 基因 Ensembl + HGNC），合并后 var = 32,596 基因 ✅ +- visium healthy 合并：3 文件 var 完全一致（35,477 基因），合并后 var = 35,477 基因 ✅ +- visium PSC 合并：4 文件 var 完全一致（35,477 基因），合并后 var = 35,477 基因 ✅ + +跨合并组（sc 32,596 vs visium 35,477）的合并不在本次 scope；如未来要做，需先对 Ensembl ID 取交集。 + +--- + +## 六、合并文件之间的关系图 + +``` +Liver_References/ +├── (17 原文件，不变) +│ +└── merged_views/ ← 本次新增派生层 + ├── MERGE_REPORT.md ← 本文档 + │ + ├── sc__healthy__merged-all-celltypes__107542cx32596g.h5ad + │ ↑ 来自 8 个 sc__healthy__*.h5ad（cell type subsets） + │ + ├── visium__healthy-C73__merged-all-blocks__14976sx35477g.h5ad + │ ↑ 来自 3 个 visium__healthy-C73__*.h5ad（C73 切片） + │ + └── visium__psc-PSC011__merged-all-blocks__19968sx35477g.h5ad + ↑ 来自 4 个 visium__psc-PSC011__*.h5ad（PSC011 切片） + +未参与本次合并的 2 个原文件： + sc__psc-pbc-healthy__all-cells__89637cx32596g__... ← 已是 sc 全集 + sn__psc-pbc-healthy__all-cells__105780cx32596g__... ← 已是 sn 全集 +``` + +--- + +## 七、可能的下一步合并（v2 提议，不在本次 scope） + +如果未来要把"派生视图"做得更深，可以考虑以下三类**跨子包合并**——但每一项都需要更复杂的处理（batch correction / 模态对齐 / 基因交集）： + +**v2 候选 #1 · sc + sn 全集联合**——把 `sc__psc-pbc-healthy__all-cells` 与 `sn__psc-pbc-healthy__all-cells` 用 Harmony / scVI 做跨模态 latent space，产出"PSC+PBC+healthy 全细胞 + 全核统一图谱"。难点：sc 与 sn 的基因检出模式系统差异。 + +**v2 候选 #2 · 健康 vs PSC visium 跨疾病合并**——把 `visium__healthy-C73` 与 `visium__psc-PSC011` 合并成"健康 vs PSC 空间对比图谱"。难点：跨患者切片对齐 + spot 级别 batch effect。 + +**v2 候选 #3 · 健康肝完整图谱**——sc healthy 合并视图（本组 1）+ sc all-cells 中的健康部分（24,241 cells）+ sn all-cells 中的健康部分（26,515 nuclei）= 完整健康肝多模态参考。难点：sc/sn 跨模态 + 跨 chemistry batch。 + +以上 v2 工作建议另起 session 单独处理，每项至少 30–60 分钟。 + +--- + +## 八、对应的可视化资产 + +本次合并同时生成了一份 HTML dashboard： + +``` +database_unified/Liver_References/merged_views/dashboard.html +``` + +含合并对照表、cell type 组成饼图、供体分布柱图、7 张 Visium 切片 hires 缩略图。**单文件自包含**，浏览器直接打开（无需任何后端）。 + +--- + +*合并工作执行于 2026-05-15 · 共 3 次合并 · 输入 15 文件 / 输出 3 派生文件 / 派生体积 467 MB · 原 17 文件未做任何修改 · 完整 provenance 记录在每份合并文件 uns["merge_provenance"]。* diff --git a/merged_views/dashboard.html b/merged_views/dashboard.html new file mode 100644 index 0000000000000000000000000000000000000000..d90eb3edc7aa5af9fff9368df44435f6a20599f8 --- /dev/null +++ b/merged_views/dashboard.html @@ -0,0 +1,256 @@ + + + + +Liver_References · 合并 Dashboard + + + + +

Liver_References · 合并 Dashboard

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database_unified/Liver_References/ · 18 原文件 + 3 合并派生视图 · 生成于 2026-05-15 · 增量更新 2026-05-16 (+1 GEO · GSE153643) · ⚠ Wave 2 增量 2026-05-17 (+5 HCC/iCCA tumor files) 未反映
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+ ⚠ 2026-05-17 Wave 2 增量 — KPI / 图表仍为 18-file v1 snapshot +
本次会话沙盒 ingest 完成 5 个新 .h5ad（Liver_References 18 → 23 文件，cells 349k → 489k），新增子包 E·Ma 2019 HCC/iCCA mixed · F·Ma 2021 HCC/iCCA treatment · G·Sharma 2020 HCC CD45+ Droplet+ss2。完整 dashboard 重做留作后续。当前请参阅：
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原文件数（保持不变）
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派生合并视图
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原数据总体积 (GB)
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合并后 cells+spots 总和
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1. 合并映射 — 哪些原文件 → 哪个派生视图

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3 次合并对应"sc healthy per-cell-type 统一 / visium healthy C73 三切片统一 / visium PSC PSC011 四切片统一"。剩 2 个 all-cells 文件（sc + sn）已是各自的全集，不进合并。
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合并类型输入文件数派生输出体积合并后规模
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2. sc healthy 合并 · cell type 组成

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107,542 个健康肝细胞按 8 种 cell type subset 的分布。
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3. visium 合并 · per-block spot 数

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所有 7 个 Visium 切片每块 spot 数均为 4,992（10x Visium 标准芯片）。
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4. 跨文件供体矩阵 · 22 独立供体的模态贡献

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每位供体在 scRNA / snRNA / Visium 三种模态下贡献的 cells/spots。C-series = 健康对照（9 人），PSC*** = 原发性硬化性胆管炎患者（10 人），PBC*** = 原发性胆汁性胆管炎患者（3 人）。
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5. Visium 切片 H&E 缩略图 · 7 块切片

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每块 hires 图（约 2000×2000 像素）缩略至 400px，已 base64 内嵌。C73 是健康捐献者（3 块），PSC011 是 PSC 患者（4 块）。
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6. 18 原文件清单（17 CELLxGENE + 1 GEO · 保持不变）

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所有原 UUID 文件在合并后保持物理位置 + 内容 + 大小不变。点击 UUID8 查看简要元信息。
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#模态UUID8体积观测数基因数title
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7. 关键链接

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+ +
+ Liver_References 数据集 · DOI 10.1016/j.jhep.2023.12.023 · CZ CELLxGENE collection 0c8a364b · schema 7.0.0 +
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