Graduate Program of Biophysical Sciences, University of Chicago, Chicago, IL, USA.
Department of Human Genetics, University of Chicago, Chicago, IL, USA.
Nat Methods. 2023 Nov;20(11):1693-1703. doi: 10.1038/s41592-023-02017-4. Epub 2023 Sep 28.
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.
成簇规律间隔短回文重复 (CRISPR) 筛选与单细胞 RNA 测序相结合，已成为在单细胞水平上描述遗传扰动对整个转录组影响的强大工具。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无法检测到受 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，称为有指导的稀疏因子分析 (GSFA)。GSFA 推断表示共调控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。通过广泛的模拟研究，我们证明 GSFA 比最先进的方法具有更高的检测扰动效果的能力。使用来自人 CD8 T 细胞和神经祖细胞的单细胞 CRISPR 数据，我们表明 GSFA 鉴定了具有生物学意义的基因模块和受 CRISPR 扰动影响的特定基因，其中许多基因被现有方法所忽略，为 T 细胞激活和神经发育中涉及的基因的功能提供了新的见解。
