Statistically principled feature selection for single cell transcriptomics.

ABSTRACT:

Background

The high dimensionality of data in single cell transcriptomics (scRNAseq) requires investigators to choose subsets of genes ("feature selection") for downstream analysis (e.g., unsupervised cell clustering). The evaluation of different approaches to feature selection is hampered by the fact that, as we show here, the difficulty of feature selection can vary greatly, depending on the dataset being analyzed.

Results

For routine cell type identification, even randomly chosen features can perform well, but for cell type differences that are subtle, both number of features and selection strategy matter strongly. We present a simple feature selection method grounded in an analytical model that allows for interpretable delineation of how many and which features to choose

SUBMITTER: Dollinger EP 

PROVIDER: S-EPMC12490061 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature