switchde: inference of switch-like differential expression along single-cell trajectories
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
- Wellcome Trust Centre for Human Genetics
Motivation: Pseudotime analyses of single-cell RNA-seq data have become increasingly common. Typically, a latent trajectory corresponding to a biological process of interest-such as differentiation or cell cycle-is discovered. However, relatively little attention has been paid to modelling the differential expression of genes along such trajectories.
Results: We present switchde , a statistical framework and accompanying R package for identifying switch-like differential expression of genes along pseudotemporal trajectories. Our method includes fast model fitting that provides interpretable parameter estimates corresponding to how quickly a gene is up or down regulated as well as where in the trajectory such regulation occurs. It also reports a P -value in favour of rejecting a constant-expression model for switch-like differential expression and optionally models the zero-inflation prevalent in single-cell data.
Availability and Implementation: The R package switchde is available through the Bioconductor project at https://bioconductor.org/packages/switchde .
Supplementary information: Supplementary data are available at Bioinformatics online.
|Number of pages||2|
|Early online date||30 Dec 2016|
|Publication status||Published - 15 Apr 2017|
- Gene Expression Profiling, Models, Genetic, Models, Statistical, Sequence Analysis, RNA, Single-Cell Analysis, Software, Journal Article, Research Support, Non-U.S. Gov't