Bias, robustness and scalability in single-cell differential expression analysis

Nat Methods. 2018 Apr;15(4):255-261. doi: 10.1038/nmeth.4612. Epub 2018 Feb 26.


Many methods have been used to determine differential gene expression from single-cell RNA (scRNA)-seq data. We evaluated 36 approaches using experimental and synthetic data and found considerable differences in the number and characteristics of the genes that are called differentially expressed. Prefiltering of lowly expressed genes has important effects, particularly for some of the methods developed for bulk RNA-seq data analysis. However, we found that bulk RNA-seq analysis methods do not generally perform worse than those developed specifically for scRNA-seq. We also present conquer, a repository of consistently processed, analysis-ready public scRNA-seq data sets that is aimed at simplifying method evaluation and reanalysis of published results. Each data set provides abundance estimates for both genes and transcripts, as well as quality control and exploratory analysis reports.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Gene Expression Profiling / methods
  • Gene Expression Regulation / physiology*
  • High-Throughput Nucleotide Sequencing / methods*
  • RNA / genetics
  • Sequence Analysis, RNA / methods*
  • Sequence Analysis, RNA / standards
  • Single-Cell Analysis / methods*
  • Software


  • RNA