eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.
BMC Bioinformatics
; 25(1): 113, 2024 Mar 15.
Article
em En
| MEDLINE
| ID: mdl-38486150
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 two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression.CONCLUSIONS:
eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Perfilação da Expressão Gênica
/
Análise da Expressão Gênica de Célula Única
Limite:
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Ano de publicação:
2024
Tipo de documento:
Article