SwarnSeq: An improved statistical approach for differential expression analysis of single-cell RNA-seq data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) is a powerful technology that is capable of generating gene expression data at the resolution of individual cell. The scRNA-seq data is characterized by the presence of dropout events, which severely bias the results if they remain unaddressed. There are limited Differential Expression (DE) approaches which consider the biological processes, which lead to dropout events, in the modeling process. So, we develop, SwarnSeq, an improved method for DE, and other downstream analysis that considers the molecular capture process in scRNA-seq data modeling. The performance of the proposed method is benchmarked with 11 existing methods on 10 different real scRNA-seq datasets under three comparison settings. We demonstrate that SwarnSeq method has improved perfo

SUBMITTER: Das S 

PROVIDER: S-EPMC10150572 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature