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

Scott R. Tyler (), Daniel Lozano-Ojalvo, Ernesto Guccione and Eric E. Schadt ()
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Scott R. Tyler: 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
Eric E. Schadt: Icahn School of Medicine at Mount Sinai

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-023-43406-9

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