An orchestra of machine learning methods reveals landmarks in single-cell data exemplified with aging fibroblasts.

ABSTRACT: In this work, a Python framework for characteristic feature extraction is developed and applied to gene expression data of human fibroblasts. Unlabeled feature selection objectively determines groups and minimal gene sets separating groups. ML explainability methods transform the features correlating with phenotypic differences into causal reasoning, supported by further pipeline and visualization tools, allowing user knowledge to boost causal reasoning. The purpose of the framework is to identify characteristic features that are causally related to phenotypic differences of single cells. The pipeline consists of several data science methods enriched with purposeful visualization of the intermediate results in order to check them systematically and infuse the domain knowledge about the inv

SUBMITTER: Rasbach L 

PROVIDER: S-EPMC11023401 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature