Bayesian inference of haematopoietic stem/progenitor cell differentiation phenotypic manifolds and their bifurcation points using Gaussian process and Gibbs sampling.

Abstract

Cell differentiation is a fundamental process in biology by which cells progress through different stages of maturation to become specialised cell types. Owing to the importance of understanding the process of cell differentiation various mathematical models have been developed to represent cell behaviour during its developmental process. Advancements in these models are owed to researchers being able to obtain single-cell gene expression data with high throughput genome-scale sequencing methods. Here we present BAGEL: Bayesian Analysis of Gene Expression Lineages, which is a novel statistical model. It allows researchers to gain new insights into the process of cell differentiation based on (i) a sound Bayesian inference approach to model cell differentiation as a continuous process; and (ii) an effective projection method which opens the door to visualise and investigate the similarities and differences between intra- and inter-species single-cell gene expression datasets. Although the main focus of this manuscript is on haematopoiesis, BAGEL should hold for various single-cell gene expression datasets.