switchde: inference of switch-like differential expression along single-cell trajectories.

Abstract

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. 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. The R package switchde is available through the Bioconductor project at https://bioconductor.org/packages/switchde . kieran.campbell@sjc.ox.ac.uk. Supplementary data are available at Bioinformatics online.