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

Kieran R Campbell, Christopher Yau

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1241-1242
Number of pages2
Issue number8
Early online date30 Dec 2016
Publication statusPublished - 15 Apr 2017


  • Gene Expression Profiling
  • Models, Genetic
  • Models, Statistical
  • Sequence Analysis, RNA
  • Single-Cell Analysis
  • Software
  • Journal Article
  • Research Support, Non-U.S. Gov't


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