Power analysis of single-cell RNA-sequencing experiments

Nat Methods. 2017 Apr;14(4):381-387. doi: 10.1038/nmeth.4220. Epub 2017 Mar 6.


Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For our workflow, we developed a flexible tool for counting the number of unique molecular identifiers (https://github.com/vals/umis/). We compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols.

MeSH terms

  • Animals
  • Embryonic Stem Cells / physiology
  • Freezing
  • Mice
  • Poly A
  • RNA, Messenger
  • Sensitivity and Specificity
  • Sequence Analysis, RNA / methods*
  • Sequence Analysis, RNA / standards
  • Sequence Analysis, RNA / statistics & numerical data
  • Single-Cell Analysis / methods*
  • Single-Cell Analysis / standards
  • Single-Cell Analysis / statistics & numerical data
  • Workflow


  • RNA, Messenger
  • Poly A