Estimates Pseudotimes for Single Cell Expression Data

Implements the DeLorean model (Reid & Wernisch (2016) ) to estimate pseudotimes for single cell expression data. The DeLorean model uses a Gaussian process latent variable model to model uncertainty in the capture time of cross-sectional data.

Travis-CI Build Status

R package to model time series accounting for noise in the temporal dimension. Specifically designed for single cell transcriptome experiments.


To render the vignettes you will need a working version (> 1.12.3) of pandoc on your machine with the pandoc-citeproc filter. On Ubuntu do:

sudo apt-get install pandoc pandoc-citeproc

Installation from CRAN

Just run the following in an R session:


Installation from source

If you prefer to have the very latest version you can install DeLorean from source. If you do not already have devtools installed, then install it by running:


Now you can install the development version of DeLorean with:



Read the vignette:



Version 1.3.0

  • Remove filter.genes and filter.cells, (renamed to use underscores) in order to pass CRAN checks.

Version 1.2.6

  • Deprecate filter.genes and filter.cells, (renamed to use underscores).

Version 1.2.5

Version 1.2.4

Version 1.2.3

  • Update for new package versions: rstan (2.10.1) and dplyr (0.5.0).

Version 1.2.2

  • Updated code for revision of manuscript. Changed cell size normalisation strategy.

Version 1.2.1

  • Fix for sqrt(integer) bug on Solaris x86.

Version 1.2.0

  • First version submitted to CRAN.

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.3.0 by John Reid, 6 months ago

Browse source code at

Authors: John Reid <[email protected]>

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports rstan, dplyr, reshape2, stringr, ggplot2, MASS, broom, coda, parallel, functional, kernlab, fastICA, seriation, memoise

Depends on Rcpp

Suggests knitr, knitcitations, rmarkdown, formatR, extrafont, testthat, svglite, VGAM

See at CRAN