A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening.
A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening.
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DOI：
10.1038/s41592-023-02017-4
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发表时间：
2023-11
期刊：
影响因子：
48
通讯作者：
He, Xin
中科院分区：
文献类型：
--
作者：
Zhou, Yifan;Luo, Kaixuan;Liang, Lifan;Chen, Mengjie;He, Xin
随机间隔短回文重复序列（CRISPR）筛选与单细胞RNA测序相结合，已成为在单细胞水平上表征遗传扰动对整个转录组影响的有力工具。然而，由于其稀疏性和复杂结构，单细胞CRISPR筛选数据的分析具有挑战性。特别是，标准的差异表达分析方法通常不足以检测受CRISPR扰动影响的基因。我们开发了一种用于此类数据的统计方法，称为引导稀疏因子分析（GSFA）。GSFA推断代表共调节基因或基因模块的潜在因子;通过从这些因子中借用信息，它推断遗传扰动对单个基因的影响。我们通过广泛的模拟研究表明，GSFA检测扰动效应的功率比最先进的方法高得多。使用来自人类CD 8 + T细胞和神经祖细胞的单细胞CRISPR数据，我们发现GSFA识别了受CRISPR扰动影响的生物学相关基因模块和特定基因，其中许多基因被现有方法遗漏，为T细胞活化和神经发育相关基因的功能提供了新的见解。引导稀疏因子分析（GSFA）是一个强大的统计框架，用于检测由于单细胞CRISPR筛选中的扰动而导致的基因表达变化。
Clustered regularly interspaced short palindromic repeats (CRISPR) screening coupled with single-cell RNA sequencing has emerged as a powerful tool to characterize the effects of genetic perturbations on the whole transcriptome at a single-cell level. However, due to its sparsity and complex structure, analysis of single-cell CRISPR screening data is challenging. In particular, standard differential expression analysis methods are often underpowered to detect genes affected by CRISPR perturbations. We developed a statistical method for such data, called guided sparse factor analysis (GSFA). GSFA infers latent factors that represent coregulated genes or gene modules; by borrowing information from these factors, it infers the effects of genetic perturbations on individual genes. We demonstrated through extensive simulation studies that GSFA detects perturbation effects with much higher power than state-of-the-art methods. Using single-cell CRISPR data from human CD8+ T cells and neural progenitor cells, we showed that GSFA identified biologically relevant gene modules and specific genes affected by CRISPR perturbations, many of which were missed by existing methods, providing new insights into the functions of genes involved in T cell activation and neurodevelopment. Guided sparse factor analysis (GSFA) is a powerful statistical framework to detect changes in gene expression as a result of perturbations in single-cell CRISPR screening.
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作者：
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DOI：
10.1126/science.aaz6063
发表时间：
2020-11-27
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Jin X;Simmons SK;Guo A;Shetty AS;Ko M;Nguyen L;Jokhi V;Robinson E;Oyler P;Curry N;Deangeli G;Lodato S;Levin JZ;Regev A;Zhang F;Arlotta P
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Arlotta P