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• W2884656343 abstract "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.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 datasets.Our method is available on github: https://github.com/magStra/GPseudoRank.Supplementary data are available at Bioinformatics online." @default.
• W2884656343 created "2018-08-03" @default.
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• W2884656343 date "2018-07-25" @default.
• W2884656343 modified "2023-10-13" @default.
• W2884656343 title "GPseudoRank: a permutation sampler for single cell orderings" @default.
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• W2884656343 doi "https://doi.org/10.1093/bioinformatics/bty664" @default.
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