Ouija is a probabilistic pseudotime framework. Ouija

• infers pseudotimes from a small number of marker genes letting you understand why the pseudotimes have been learned in terms of those genes (A)
• provides parameter estimates (with uncertainty) for interpretable gene regulation behaviour (such as the peak time or the upregulation time) (B)
• has a Bayesian hypothesis test to find genes regulated before others along the trajectory (C)
• identifies metastable states, ie discrete cell types along the continuous trajectory (D)

# install.packages("devtools")
devtools::install_github("kieranrcampbell/ouija")

To build the Ouija vignette install using

devtools::install_github("kieranrcampbell/ouija", local = FALSE, 
                          args = "--preclean", build_vignettes = TRUE)

Input is a cell-by-gene expression matrices that is non-negative and represents logged gene expression values. We recommend using log2(TPM + 1). This can either take the form of a matrix or a SingleCellExperiment (use of the SingleCellExperiment infrastructure is highly encouraged for single-cell analyses). By default the logcounts assay of a SingleCellExperiment will be used.

To fit the pseudotimes, pass the input data to the ouija function:

library(ouija)
data(example_gex) # synthetic gene expression data bundled
oui <- ouija(example_gex)
pseudotimes <- map_pseudotime(oui)

The map_pseudotimes function extracts the maximum-a-posteriori (MAP) estimates of the pseudotimes.

For further usage options see the vignette. A prebuilt vignette can be found here.

Kieran Campbell & Christopher Yau
Wellcome Trust Centre for Human Genetics, University of Oxford

Artwork by cwcyau, the mysterious banksy-esque artist of the statistical genomics world.