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

ABSTRACT: 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 demonstra

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC10630124 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature