GPseudoRank: a permutation sampler for single cell orderings
Oxford University Press
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Strauss, M., Reid, J., & Wernisch, L. (2018). GPseudoRank: a permutation sampler for single cell orderings. Bioinformatics https://doi.org/10.1093/bioinformatics/bty664
Abstract Motivation: A number of pseudotime methods have provided point estimates of the ordering of cells for scRNA-seq data. A still limited number of methods also model the uncertainty of the pseudotime estimate. However, there is still a need for a method to sample from complicated and multi-modal distributions of orders, and to estimate changes in the amount of the uncertainty of the order during the course of a biological development, as this can support the selection of suitable cells for the clustering of genes or for network inference. Results: In applications to scRNA-seq data we demonstrate the potential of GPseudoRank to sample from complex and multi-modal posterior distributions and to identify phases of lower and higher pseudotime uncertainty during a biological process. GPseudoRank also correctly identifies cells precocious in their antiviral response and links uncertainty in the ordering to metastable states. A variant of the method extends the advantages of Bayesian modelling and MCMC to large droplet-based scRNA-seq data sets. Availability and implementation: Our method is available on github: https://github.com/magStra/GPseudoRank.
MS, JR and LW are funded by the UK Medical Research Council (Grant Ref MC_UU_00002/1).
External DOI: https://doi.org/10.1093/bioinformatics/bty664
This record's URL: https://www.repository.cam.ac.uk/handle/1810/279864
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/