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

BMC Bioinformatics. 2021 Oct 26;22(1):524. doi: 10.1186/s12859-021-04412-0.


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 has better power to detect cell-type specific DEGs than conventional methods while appropriately controlling type I error rate. The usefulness of our method is demonstrated through its application to study the pathogenesis and biological processes of idiopathic pulmonary fibrosis (IPF) using a single-cell RNA-sequencing (scRNA-seq) data set, which contains 18,150 protein-coding genes across 38 cell types on lung tissues from 32 IPF patients and 28 normal controls.

Conclusions: The proposed MRF model is implemented in the R package MRFscRNAseq available on GitHub. By utilizing gene-gene and cell-cell networks, our method increases statistical power to detect differentially expressed genes from scRNA-seq data.

Keywords: Differential expression; Markov random field; scRNA-seq.

MeSH terms

  • Algorithms
  • Gene Expression Profiling*
  • Gene Regulatory Networks*
  • Humans
  • RNA-Seq
  • Sequence Analysis, RNA
  • Single-Cell Analysis