ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes.

ABSTRACT:

Motivation

Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret biological implications of computational results. Hence, a theory for efficiently annotating individual cells remains warranted.

Results

We present ASURAT, a computational tool for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process and signaling pathway activity for single-cell transcriptomic data, using a correlation graph decomposition for genes in database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear ce

SUBMITTER: Iida K 

PROVIDER: S-EPMC9477531 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature