ScapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data.

ABSTRACT: Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene-cell association network for inferring single-cell pathway activity scores and identifying cell phenotype-associated gene modules from single-cell multi-omics data. Systematic benchmarking demonstrated that scapGNN was more accurate, robust, and scalable than state-of-the-art methods in various downstream single-cell analyses such as cell denoising, batch effect removal, cell clustering, cell trajectory inference, and pat

SUBMITTER: Han X 

PROVIDER: S-EPMC10681325 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature