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Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq

Author

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  • Scott R. Tyler

    (Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai)

  • Daniel Lozano-Ojalvo

    (Icahn School of Medicine at Mount Sinai)

  • Ernesto Guccione

    (Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai)

  • Eric E. Schadt

    (Icahn School of Medicine at Mount Sinai)

Abstract

While sub-clustering cell-populations has become popular in single cell-omics, negative controls for this process are lacking. Popular feature-selection/clustering algorithms fail the null-dataset problem, allowing erroneous subdivisions of homogenous clusters until nearly each cell is called its own cluster. Using real and synthetic datasets, we find that anti-correlated gene selection reduces or eliminates erroneous subdivisions, increases marker-gene selection efficacy, and efficiently scales to millions of cells.

Suggested Citation

  • Scott R. Tyler & Daniel Lozano-Ojalvo & Ernesto Guccione & Eric E. Schadt, 2024. "Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-43406-9
    DOI: 10.1038/s41467-023-43406-9
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    References listed on IDEAS

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    3. Masahiro Yoshida & Kaylee B. Worlock & Ni Huang & Rik G. H. Lindeboom & Colin R. Butler & Natsuhiko Kumasaka & Cecilia Dominguez Conde & Lira Mamanova & Liam Bolt & Laura Richardson & Krzysztof Polans, 2022. "Local and systemic responses to SARS-CoV-2 infection in children and adults," Nature, Nature, vol. 602(7896), pages 321-327, February.
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