MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Genome Biol. 2015 Dec 10:16:278. doi: 10.1186/s13059-015-0844-5.


Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at .

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Data Interpretation, Statistical
  • Dendritic Cells / metabolism
  • Gene Expression Profiling / methods*
  • Genetic Variation
  • Humans
  • Linear Models
  • Mice
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis
  • Transcriptome