Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking.

ABSTRACT: To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platfo

SUBMITTER: Gagnon J 

PROVIDER: S-EPMC9225332 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature