Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data

Abstract

Computational techniques have arisen from single-cell ‘omics and cancer modelling where pseudotime can be used to learn about cellular differentiation or tumour progression. However, methods to date typically implicitly assume homogeneous genetic, phenotypic or environmental backgrounds, which becomes limiting as data sets grow in size and complexity. We describe a novel statistical framework that learns how pseudotime trajectories can be modulated through covariates that encode such factors.

Publication
In: Nature Communications, (9), 1, pp. 2442