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A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias

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  • Taavi Päll
  • Hannes Luidalepp
  • Tanel Tenson
  • Ülo Maiväli

Abstract

We assess inferential quality in the field of differential expression profiling by high-throughput sequencing (HT-seq) based on analysis of datasets submitted from 2008 to 2020 to the NCBI GEO data repository. We take advantage of the parallel differential expression testing over thousands of genes, whereby each experiment leads to a large set of p-values, the distribution of which can indicate the validity of assumptions behind the test. From a well-behaved p-value set π0, the fraction of genes that are not differentially expressed can be estimated. We found that only 25% of experiments resulted in theoretically expected p-value histogram shapes, although there is a marked improvement over time. Uniform p-value histogram shapes, indicative of

Suggested Citation

  • Taavi Päll & Hannes Luidalepp & Tanel Tenson & Ülo Maiväli, 2023. "A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias," PLOS Biology, Public Library of Science, vol. 21(3), pages 1-27, March.
    • Handle: RePEc:plo:pbio00:3002007
    DOI: 10.1371/journal.pbio.3002007
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    1. Scott Marek & Brenden Tervo-Clemmens & Finnegan J. Calabro & David F. Montez & Benjamin P. Kay & Alexander S. Hatoum & Meghan Rose Donohue & William Foran & Ryland L. Miller & Timothy J. Hendrickson &, 2022. "Reproducible brain-wide association studies require thousands of individuals," Nature, Nature, vol. 603(7902), pages 654-660, March.
    2. Monya Baker, 2016. "1,500 scientists lift the lid on reproducibility," Nature, Nature, vol. 533(7604), pages 452-454, May.
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