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.
University of Oxford researchers present switchde, a statistical framework and accompanying R package for identifying switch-like differential expression of genes along pseudotemporal trajectories. This 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 – The R package switchde is available at http://www.github.com/kieranrcampbell/switchde