muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

Nat Commun. 2020 Nov 30;11(1):6077. doi: 10.1038/s41467-020-19894-4.

Abstract

Single-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile the transcriptomes of individual cells on a large scale. Early analyses of differential expression have aimed at identifying differences between subpopulations to identify subpopulation markers. More generally, such methods compare expression levels across sets of cells, thus leading to cross-condition analyses. Given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis; however, it is not clear which statistical framework best handles this situation. Here, we surveyed methods to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated pseudobulk data. To evaluate method performance, we developed a flexible simulation that mimics multi-sample scRNA-seq data. We analyzed scRNA-seq data from mouse cortex cells to uncover subpopulation-specific responses to lipopolysaccharide treatment, and provide robust tools for multi-condition analysis within the muscat R package.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cerebellar Cortex / drug effects
  • Cerebellar Cortex / metabolism
  • Cluster Analysis
  • Computational Biology
  • Computer Simulation
  • Gene Expression Profiling / methods*
  • Lipopolysaccharides / adverse effects
  • Male
  • Mice
  • Models, Statistical
  • RNA, Small Cytoplasmic
  • Sequence Analysis, RNA / methods*
  • Signal Transduction
  • Single-Cell Analysis / methods*
  • Software
  • Transcriptome*

Substances

  • Lipopolysaccharides
  • RNA, Small Cytoplasmic

Associated data

  • figshare/10.6084/m9.figshare.8986193