ArticleF1000Research2016
Whose sample is it anyway? Widespread misannotation of samples in transcriptomics studies.
Article in F1000Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A Meta-Analysis of the Effects of Acute Sleep Deprivation on the Cortical Transcriptome in Rodent Models.Journal of sleep research · 2026Pooled it
- Transcriptomic Evidence for Alterations in Astrocytes and Parvalbumin Interneurons in Subjects With Bipolar Disorder and Schizophrenia.Biological psychiatry · 2018Pooled it
- Pooled it
- CanID: A Robust and Accurate RNA-seq Expression-based Diagnostic Classification Scheme for Pediatric Malignancies.Genomics, proteomics & bioinformatics · 2025Article
- Making genomic data FAIR through effective Data Portals.Scientific data · 2025Article
- Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies.Cell genomics · 2025Review
- Metadata integrity in bioinformatics: Bridging the gap between data and knowledge.Computational and structural biotechnology journal · 2023Review
- Navigating the hydroxymethylome: experimental biases and quality control tools for the tandem bisulfite and oxidative bisulfite Illumina microarrays.Epigenomics · 2022Article
- Transposable element sequence fragments incorporated into coding and noncoding transcripts modulate the transcriptome of human pluripotent stem cells.Nucleic acids research · 2021Article
- Tissue heterogeneity is prevalent in gene expression studies.NAR genomics and bioinformatics · 2021Article
- DNA methylation-based sex classifier to predict sex and identify sex chromosome aneuploidy.BMC genomics · 2021Article
- A community effort to identify and correct mislabeled samples in proteogenomic studies.Patterns (New York, N.Y.) · 2021Article
- Large-scale labeling and assessment of sex bias in publicly available expression data.BMC bioinformatics · 2021Article
- Curation of over 10 000 transcriptomic studies to enable data reuse.Database : the journal of biological databases and curation · 2021Article
- Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study.Bioinformatics (Oxford, England) · 2020Article
- Genome-Wide Association Study in Two Cohorts from a Multi-generational Mouse Advanced Intercross Line Highlights the Difficulty of Replication Due to Study-Specific Heterogeneity.G3 (Bethesda, Md.) · 2020Article
- Assessment of kinship detection using RNA-seq data.Nucleic acids research · 2019Article
- Sex Differences in the Blood Transcriptome Identify Robust Changes in Immune Cell Proportions with Aging and Influenza Infection.Cell reports · 2019Article
- Transcriptomic Characterization of the Human Insular Cortex and Claustrum.Frontiers in neuroanatomy · 2019Article
- Functional Transcriptomics for Bacterial Gene Detectives.Microbiology spectrum · 2018Review
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3 authors.
Funding
Abstract
Concern about the reproducibility and reliability of biomedical research has been rising. An understudied issue is the prevalence of sample mislabeling, one impact of which would be invalid comparisons. We studied this issue in a corpus of human transcriptomics studies by comparing the provided annotations of sex to the expression levels of sex-specific genes. We identified apparent mislabeled samples in 46% of the datasets studied, yielding a 99% confidence lower-bound estimate for all studies of 33%. In a separate analysis of a set of datasets concerning a single cohort of subjects, 2/4 had mislabeled samples, indicating laboratory mix-ups rather than data recording errors. While the number of mixed-up samples per study was generally small, because our method can only identify a subset of potential mix-ups, our estimate is conservative for the breadth of the problem. Our findings emphasize the need for more stringent sample tracking, and that re-users of published data must be alert to the possibility of annotation and labelling errors.
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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.