Bayesian Gaussian Process Latent Variable Models for pseudotime inference in single-cell RNA-seq data

Abstract

Single-cell genomics has revolutionised modern biology while requiring the development of advanced computational and statistical methods. Advances have been made in uncovering gene expression heterogeneity, discovering new cell types and novel identification of genes and transcription factors involved in cellular processes. One such approach to the analysis is to construct pseudotime orderings of cells as they progress through a particular biological process, such as cell-cycle or differentiation. These methods assign a score - known as the pseudotime - to each cell as a surrogate measure of progression. However, all published methods to date are purely algorithmic and lack any way to give uncertainty to the pseudotime assigned to a cell. Here we present a method that combines Gaussian Process Latent Variable Models (GP-LVM) with a recently published electroGP prior to perform Bayesian inference on the pseudotimes. We go on to show that the posterior variability in these pseudotimes leads to nontrivial uncertainty in the pseudo-temporal ordering of the cells and that pseudotimes should not be thought of as point estimates.