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Whose sample is it anyway? Widespread misannotation of samples in transcriptomics studies.

Lilah Toker1, Min Feng2, Paul Pavlidis1

  • 1Department of Psychiatry, University of British Columbia, Vancouver, V6T 2A1, Canada; Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4, Canada.

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|October 26, 2016
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Summary
This summary is machine-generated.

Sample mislabeling is a significant issue in biomedical research, affecting 46% of human transcriptomics datasets studied. This impacts research reliability and necessitates better sample tracking to ensure accurate scientific findings.

Keywords:
Transcriptomicsdata qualitygene expressionmisannotationmislabelingreproducibility

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Area of Science:

  • Genomics
  • Bioinformatics
  • Biomedical Research

Background:

  • Reproducibility and reliability are critical concerns in biomedical research.
  • Sample mislabeling is an understudied issue that can lead to invalid comparisons.
  • Transcriptomics studies are valuable for understanding gene expression but susceptible to sample handling errors.

Purpose of the Study:

  • To investigate the prevalence of sample mislabeling in human transcriptomics studies.
  • To assess the impact of mislabeling on data integrity and research reproducibility.
  • To provide an estimate of the extent of sample mislabeling in published research.

Main Methods:

  • Analysis of a corpus of human transcriptomics datasets.
  • Comparison of provided sample sex annotations with expression levels of sex-specific genes.
  • Statistical analysis to estimate the prevalence and confidence intervals of mislabeled samples.

Main Results:

  • Apparent sample mislabeling was identified in 46% of the studied datasets.
  • A 99% confidence lower-bound estimate suggests at least 33% of all human transcriptomics studies may have mislabeled samples.
  • Analysis of a single cohort indicated laboratory mix-ups as a likely cause.

Conclusions:

  • Sample mislabeling is a widespread problem in human transcriptomics research.
  • The findings highlight the need for improved sample tracking and quality control measures.
  • Researchers using public datasets should be vigilant for potential annotation and labeling errors.