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

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Abstract

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:

kieran.campbell@sjc.ox.ac.uk

Supplementary information:

Supplementary data are available at Bioinformatics online.

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