title: switchde: inference of switch-like differential expression along single-cell trajectories.
abstract: 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.
  
  
  
  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.
  
  
  
  The R package switchde is available through the Bioconductor project at https://bioconductor.org/packages/switchde .
  
  
  
  kieran.campbell@sjc.ox.ac.uk.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Physiology, Anatomy and Genetics.|Wellcome Trust Centre for Human Genetics.
authors.forename: Kieran R
authors.initials: KR
authors.lastname: Campbell
authors.affiliation: Wellcome Trust Centre for Human Genetics.|Department of Statistics, University of Oxford, Oxford, UK.
authors.forename: Christopher
authors.initials: C
authors.lastname: Yau
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D017423:Sequence Analysis, RNA / Q000379:methods*; D059010:Single-Cell Analysis; D012984:Software