Trajectory-based differential expression analysis for single-cell sequencing data.

ABSTRACT: Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-leve

SUBMITTER: Van den Berge K 

PROVIDER: S-EPMC7058077 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature