Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq.

ABSTRACT: Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell's expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here, we benchmark and enhance the use of matrix factorization to solve this problem. We show with simulations that a method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including their relative contributions in each cell. To illustrate the insights this approach enables, we apply it to published brain organoi

SUBMITTER: Kotliar D 

PROVIDER: S-EPMC6639075 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature