IDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis.

Results

We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10463720 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature