A novel Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening

Abstract

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 the sparsity and complex structure of data, analysis of single-cell CRISPR screening data remains challenging. In particular, standard differential expression analysis methods are often under-powered to detect genes affected by CRISPR perturbations. We developed a novel method for such data, called Guided Sparse Factor Analysis (GSFA). GSFA infers latent factors that represent co-regulated genes or gene modules, and by borrowing information from these factors, 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.