Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq.
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ABSTRACT:
SUBMITTER: Tyler SR
PROVIDER: S-EPMC10808220 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Publications
Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq.
Tyler Scott R SR Lozano-Ojalvo Daniel D Guccione Ernesto E Schadt Eric E EE
Nature communications 20240124 1
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 ...[more]
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