Optimized cell type signatures revealed from single-cell data by combining principal feature analysis, mutual information, and machine learning.

ABSTRACT: Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal

SUBMITTER: Caliskan A 

PROVIDER: S-EPMC10276237 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature