An NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data.

ABSTRACT: Single-cell RNA sequencing has enabled researchers to quantify the transcriptomes of individual cells, infer cell types and investigate differential expression among cell types, which will lead to a better understanding of the regulatory mechanisms of cell states. Transcript diversity caused by phenomena such as aberrant splicing events have been revealed, and differential expression of previously unannotated transcripts might be overlooked by annotation-based analyses. Accordingly, we have developed an approach to discover overlooked differentially expressed (DE) gene regions that complements annotation-based methods. Our algorithm decomposes mapped count data matrix for a gene region using non-negative matrix factorization, quantifies the differential expression level based on the decomp

SUBMITTER: Matsumoto H 

PROVIDER: S-EPMC8499053 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature