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

Original publication

DOI

10.1093/bioinformatics/btw798

Type

Journal article

Journal

Bioinformatics (Oxford, England)

Publication Date

04/2017

Volume

33

Pages

1241 - 1242

Addresses

Department of Physiology, Anatomy and Genetics.

Keywords

Models, Statistical, Gene Expression Profiling, Sequence Analysis, RNA, Models, Genetic, Software, Single-Cell Analysis