Cell-level metadata are indispensable for documenting single-cell sequencing datasets

PLoS Biol. 2021 May 4;19(5):e3001077. doi: 10.1371/journal.pbio.3001077. eCollection 2021 May.


Single-cell RNA sequencing (scRNA-seq) provides an unprecedented view of cellular diversity of biological systems. However, across the thousands of publications and datasets generated using this technology, we estimate that only a minority (<25%) of studies provide cell-level metadata information containing identified cell types and related findings of the published dataset. Metadata omission hinders reproduction, exploration, validation, and knowledge transfer and is a common problem across journals, data repositories, and publication dates. We encourage investigators, reviewers, journals, and data repositories to improve their standards and ensure proper documentation of these valuable datasets.

Publication types

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

MeSH terms

  • Animals
  • Computational Biology / methods*
  • Gene Expression Profiling / methods
  • Humans
  • Meta-Analysis as Topic
  • Metadata / trends
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / methods*
  • Software