DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data.

ABSTRACT: Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even resulting in poorer clustering accuracy than without feature selection. Moreover, existing methods ignore information contained in gene-gene correlations. Here, we introduce DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diver

SUBMITTER: Ranjan B 

PROVIDER: S-EPMC8494900 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature