A 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. We applied our algorithm to two datasets and discovered several intriguing DE transcripts, including a transcript related to the modulation of neural stem/progenitor cell differentiation.