LAUSR

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. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.