ArticlePLoS biology2021
Cell-level metadata are indispensable for documenting single-cell sequencing datasets.
Article in PLoS biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Persistent hindrances to data re-use in single-cell genomics.Scientific data · 2026Article
- Making genomic data FAIR through effective Data Portals.Scientific data · 2025Article
- Galaxy single-cell & spatial omics community update: Navigating new frontiers in 2025.Cell genomics · 2025Review
- MAMS: matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data.Genome biology · 2024Article
- CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.Nucleic acids research · 2024Article
- PlaqView 2.0: A comprehensive web portal for cardiovascular single-cell genomics.Frontiers in cardiovascular medicine · 2022Article
- The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing.F1000Research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.