A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data.

ABSTRACT: The development of single-cell RNA-sequencing (scRNA-seq) technologies has offered insights into complex biological systems at the single-cell resolution. In particular, these techniques facilitate the identifications of genes showing cell-type-specific differential expressions (DE). In this paper, we introduce MARBLES, a novel statistical model for cross-condition DE gene detection from scRNA-seq data. MARBLES employs a Markov Random Field model to borrow information across similar cell types and utilizes cell-type-specific pseudobulk count to account for sample-level variability. Our simulation results showed that MARBLES is more powerful than existing methods to detect DE genes with an appropriate control of false positive rate. Applications of MARBLES to real data identified novel dise

SUBMITTER: Zhu B 

PROVIDER: S-EPMC9487630 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature