用外部对照重分析基因图谱，找回缺失控制下的转录因子效应。
Re-analysis of the Human Transcription Factor Atlas Recovers TF-Specific Signatures from Pooled Single-Cell Screens with Missing Controls
- 用类胚体细胞作外部基准，通过背景剔除恢复信号
- 87个转录因子中60%成功定位，59个获特异性表达信号
- 结果与已发表排名显著相关，适合做功能验证研究
公共的池化单细胞扰动图谱是研究转录因子（TF）功能的重要资源，但下游重分析常受限于不完整的元数据和缺失的内部对照。本文重新分析人类转录因子图谱数据集（GSE216481），该数据基于MORF的过表达筛选，覆盖3,550个转录因子开放阅读框和254,519个细胞。采用可复现的质控、MORF条形码解复用、每转录因子差异表达及功能富集流程。从池化筛选中的77,018个细胞中，将60,997个（79.2%）分配至87个转录因子身份。因原始库中缺少GFP和mCherry阴性对照，使用胚胎体（EB）细胞作为外部基线，并通过背景减法去除共有的批次/转导干扰。该策略使59/61个可测转录因子获得特异性信号，优于一 vs 余方法检测到的27个。结果显示HOPX、MAZ、PAX6、FOS和FEZF2为强转录重塑因子；各因子富集分析揭示FEZF2调控分化，EGR1关联Hippo与心脏通路，FOS参与黏附斑，NFIC调控胶原合成。条件水平分析显示Wnt、神经发生、EMT和Hippo通路的汇聚信号，Harmony分析表明池化重复间批次效应极小。每转录因子效应大小与Joung等人发表排名显著一致（Spearman ρ = -0.316, p = 0.013；负号表示排名越低效应越强）。结果表明，结合合理外部对照、去噪和可复现计算，即使缺乏内部对照，该数据仍可用于验证性的转录因子与通路分析。
原文摘要 · Abstract (English)
Public pooled single-cell perturbation atlases are valuable resources for studying transcription factor (TF) function, but downstream re-analysis can be limited by incomplete deposited metadata and missing internal controls. Here we re-analyze the human TF Atlas dataset (GSE216481), a MORF-based pooled overexpression screen spanning 3,550 TF open reading frames and 254,519 cells, with a reproducible pipeline for quality control, MORF barcode demultiplexing, per-TF differential expression, and functional enrichment. From 77,018 cells in the pooled screen, we assign 60,997 (79.2\%) to 87 TF identities. Because the deposited barcode mapping lacks the GFP and mCherry negative controls present in the original library, we use embryoid body (EB) cells as an external baseline and remove shared batch/transduction artifacts by background subtraction. This strategy recovers TF-specific signatures for 59 of 61 testable TFs, compared with 27 detected by one-vs-rest alone, showing that robust TF-level signal can be rescued despite missing intra-pool controls. HOPX, MAZ, PAX6, FOS, and FEZF2 emerge as the strongest transcriptional remodelers, while per-TF enrichment links FEZF2 to regulation of differentiation, EGR1 to Hippo and cardiac programs, FOS to focal adhesion, and NFIC to collagen biosynthesis. Condition-level analyses reveal convergent Wnt, neurogenic, EMT, and Hippo signatures, and Harmony indicates minimal confounding batch effects across pooled replicates. Our per-TF effect sizes significantly agree with Joung et al.'s published rankings (Spearman $ρ= -0.316$, $p = 0.013$; negative because lower rank indicates stronger effect). Together, these results show that the deposited TF Atlas data can support validated TF-specific transcriptional and pathway analyses when paired with principled external controls, artifact removal, and reproducible computation.
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