Supervised learning of high-confidence phenotypic subpopulations from single-cell data.

ABSTRACT: Accurately identifying phenotype-relevant cell subsets from heterogeneous cell populations is crucial for delineating the underlying mechanisms driving biological or clinical phenotypes. Here, by deploying a learning with rejection strategy, we developed a novel supervised learning framework called PENCIL to identify subpopulations associated with categorical or continuous phenotypes from single-cell data. By embedding a feature selection function into this flexible framework, for the first time, we were able to select informative features and identify cell subpopulations simultaneously, which enables the accurate identification of phenotypic subpopulations otherwise missed by methods incapable of concurrent gene selection. Furthermore, the regression mode of PENCIL presents a novel abilit

SUBMITTER: Ren T 

PROVIDER: S-EPMC10055361 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature