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Community assessment of methods to deconvolve cellular composition from bulk gene expression

Author

Listed:
  • Brian S. White

    (Sage Bionetworks
    The Jackson Laboratory for Genomic Medicine)

  • Aurélien Reyniès

    (INSERM U1138, Université Paris Cité)

  • Aaron M. Newman

    (Stanford University
    Stanford University)

  • Joshua J. Waterfall

    (PSL Research University)

  • Andrew Lamb

    (Sage Bionetworks)

  • Florent Petitprez

    (Ligue Nationale Contre le Cancer
    University of Edinburgh)

  • Yating Lin

    (Xiamen University)

  • Rongshan Yu

    (Xiamen University)

  • Martin E. Guerrero-Gimenez

    (National University of Cuyo)

  • Sergii Domanskyi

    (Michigan State University)

  • Gianni Monaco

    (BIOGEM Institute of Molecular Biology and Genetics)

  • Verena Chung

    (Sage Bionetworks)

  • Jineta Banerjee

    (Sage Bionetworks)

  • Daniel Derrick

    (Oregon Health & Science University)

  • Alberto Valdeolivas

    (Institute for Computational Biomedicine)

  • Haojun Li

    (Xiamen University)

  • Xu Xiao

    (Xiamen University)

  • Shun Wang

    (Cancer Hospital, Chinese Aacdemy of Medical Science)

  • Frank Zheng

    (AmoyDx)

  • Wenxian Yang

    (Aginome Scientific)

  • Carlos A. Catania

    (National University of Cuyo)

  • Benjamin J. Lang

    (Harvard Medical School)

  • Thomas J. Bertus

    (Michigan State University)

  • Carlo Piermarocchi

    (Michigan State University)

  • Francesca P. Caruso

    (BIOGEM Institute of Molecular Biology and Genetics)

  • Michele Ceccarelli

    (BIOGEM Institute of Molecular Biology and Genetics
    University of Miami Miller School of Medicine)

  • Thomas Yu

    (Sage Bionetworks)

  • Xindi Guo

    (Sage Bionetworks)

  • Julie Bletz

    (Sage Bionetworks)

  • John Coller

    (Stanford University School of Medicine)

  • Holden Maecker

    (Stanford University School of Medicine)

  • Caroline Duault

    (Stanford University School of Medicine)

  • Vida Shokoohi

    (Stanford University School of Medicine)

  • Shailja Patel

    (Stanford University School of Medicine)

  • Joanna E. Liliental

    (Stanford University School of Medicine)

  • Stockard Simon

    (Sage Bionetworks)

  • Julio Saez-Rodriguez

    (Institute for Computational Biomedicine)

  • Laura M. Heiser

    (Oregon Health & Science University)

  • Justin Guinney

    (Sage Bionetworks)

  • Andrew J. Gentles

    (Stanford University
    Stanford University School of Medicine
    Stanford University)

Abstract

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

Suggested Citation

  • Brian S. White & Aurélien Reyniès & Aaron M. Newman & Joshua J. Waterfall & Andrew Lamb & Florent Petitprez & Yating Lin & Rongshan Yu & Martin E. Guerrero-Gimenez & Sergii Domanskyi & Gianni Monaco &, 2024. "Community assessment of methods to deconvolve cellular composition from bulk gene expression," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50618-0
    DOI: 10.1038/s41467-024-50618-0
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    References listed on IDEAS

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