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Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq
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Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq
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Journal Article
Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq
2024
Overview
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.
Typical single-cell RNAseq pipelines will subcluster homogeneous cells. Here, authors present a computational algorithm for accurately identifying cell-type marker genes in single-cell data analysis with a low false discovery rate.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
