ESVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.

ABSTRACT:

Background

Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes.

Results

We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel

SUBMITTER: Lin KZ 

PROVIDER: S-EPMC10690270 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature