The triumphs and limitations of computational methods for scRNA-seq

Nat Methods. 2021 Jul;18(7):723-732. doi: 10.1038/s41592-021-01171-x. Epub 2021 Jun 21.


The rapid progress of protocols for sequencing single-cell transcriptomes over the past decade has been accompanied by equally impressive advances in the computational methods for analysis of such data. As capacity and accuracy of the experimental techniques grew, the emerging algorithm developments revealed increasingly complex facets of the underlying biology, from cell type composition to gene regulation to developmental dynamics. At the same time, rapid growth has forced continuous reevaluation of the underlying statistical models, experimental aims, and sheer volumes of data processing that are handled by these computational tools. Here, I review key computational steps of single-cell RNA sequencing (scRNA-seq) analysis, examine assumptions made by different approaches, and highlight successes, remaining ambiguities, and limitations that are important to keep in mind as scRNA-seq becomes a mainstream technique for studying biology.

Publication types

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

MeSH terms

  • Animals
  • CD8-Positive T-Lymphocytes / cytology
  • CD8-Positive T-Lymphocytes / physiology
  • Computational Biology / methods*
  • Computer Graphics
  • Databases, Genetic
  • Humans
  • Mice
  • Principal Component Analysis
  • Sequence Analysis, RNA / methods*
  • Sequence Analysis, RNA / statistics & numerical data
  • Single-Cell Analysis / methods*
  • Single-Cell Analysis / statistics & numerical data
  • Transcription, Genetic