Anti-correlated Feature Selection Prevents False Discovery of Subpopulations in scRNAseq

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 45,348 scRNAseq analyses of real and synthetic datasets, we found that anti-correlated gene selection reduces or eliminates erroneous subdivisions, increases marker-gene selection efficacy, and efficiently scales to 245k cells without the need for high-performance computing.