Journal
BIOINFORMATICS
Volume 33, Issue 8, Pages 1241-1242Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btw798
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Funding
- UK Medical Research Council
- UK Medical Research Council New Investigator Research Grant [MR/L001411/1]
- Wellcome Trust Core Award [090532/Z/09/Z]
- John Fell Oxford University Press (OUP) Research Fund
- Li Ka Shing Foundation via a Oxford-Stanford Big Data in Human Health Seed Grant
- MRC [MR/L001411/1] Funding Source: UKRI
- Medical Research Council [MR/L001411/1, 1523984] Funding Source: researchfish
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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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