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

Sidhant PuntambekarJay R HesselberthKent A RiemondyRui Fu
Published in: PLoS biology (2021)
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.
Keyphrases
  • single cell
  • rna seq
  • high throughput
  • electronic health record
  • healthcare
  • big data
  • machine learning
  • systematic review
  • gene expression
  • genome wide
  • dna methylation
  • artificial intelligence
  • data analysis