Bayesian approach to single-cell differential expression analysis

Nat Methods. 2014 Jul;11(7):740-2. doi: 10.1038/nmeth.2967. Epub 2014 May 18.


Single-cell data provide a means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression-magnitude distortions typical of single-cell RNA-sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.

Publication types

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

MeSH terms

  • Algorithms
  • Bayes Theorem
  • Gene Expression Profiling / methods*
  • Oligonucleotide Array Sequence Analysis
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / methods*