ScAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data.

ABSTRACT: Although single-cell sequencing has provided a powerful tool to deconvolute cellular heterogeneity of diseases like cancer, extrapolating clinical significance or identifying clinically-relevant cells remains challenging. Here, we propose a novel computational method scAB, which integrates single-cell genomics data with clinically annotated bulk sequencing data via a knowledge- and graph-guided matrix factorization model. Once combined, scAB provides a coarse- and fine-grain multiresolution perspective of phenotype-associated cell states and prognostic signatures previously not visible by single-cell genomics. We use scAB to enhance live cancer single-cell RNA-seq data, identifying clinically-relevant previously unrecognized cancer and stromal cell subsets whose signatures show a stronger

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC9757078 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature