Biologically informed deep learning to query gene programs in single-cell atlases.

ABSTRACT: The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known 'gene programs'. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to exi

SUBMITTER: Lotfollahi M 

PROVIDER: S-EPMC9928587 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature