A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data.

ABSTRACT:

Background

Recent development of single cell sequencing technologies has made it possible to identify genes with different expression (DE) levels at the cell type level between different groups of samples. In this article, we propose to borrow information through known biological networks to increase statistical power to identify differentially expressed genes (DEGs).

Results

We develop MRFscRNAseq, which is based on a Markov random field (MRF) model to appropriately accommodate gene network information as well as dependencies among cell types to identify cell-type specific DEGs. We implement an Expectation-Maximization (EM) algorithm with mean field-like approximation to estimate model parameters and a Gibbs sampler to infer DE status. Simulation study shows that our method

SUBMITTER: Li H 

PROVIDER: S-EPMC8549347 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature