Transcriptome data are insufficient to control false discoveries in regulatory network inference.

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  • Additional Information
    • Source:
      Publisher: Cell Press Country of Publication: United States NLM ID: 101656080 Publication Model: Print Cited Medium: Internet ISSN: 2405-4720 (Electronic) Linking ISSN: 24054712 NLM ISO Abbreviation: Cell Syst Subsets: MEDLINE
    • Publication Information:
      Original Publication: [Cambridge, MA] : Cell Press, [2015]-
    • Subject Terms:
      Gene Regulatory Networks*/genetics ; Transcriptome*/genetics; Humans ; Chromatin Immunoprecipitation/methods ; Gene Expression Profiling/methods
    • Abstract:
      Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.
      Competing Interests: Declaration of interests A.B. is a stockholder for Alphabet, Inc.; has consulted for Third Rock Ventures; and is a founder of CellCipher, Inc.
      (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
    • Contributed Indexing:
      Keywords: Markov random field; false discovery rate; gene regulatory network; knockoff filter; network inference; structure learning; transcription factor; transcriptional regulation
    • Publication Date:
      Date Created: 20240822 Date Completed: 20240822 Latest Revision: 20240822
    • Publication Date:
      20240823
    • Accession Number:
      10.1016/j.cels.2024.07.006
    • Accession Number:
      39173585