Learning regulatory models for cell development from single cell transcriptomic data

Abstract

Single cell transcriptomic data allow us to probe the transcriptional changes occurring during cell development in unprecedented detail. These complex datasets are driving the development of new computational and statistical tools that are revolutionizing our understanding of differentiation processes. Many clustering and dimensionality reduction methods exist to aid visualization and exploration of structure in these datasets. Increasingly, pseudotemporal ordering and network inference algorithms are emerging that aim to elucidate the regulatory mechanisms that drive and control changes in gene expression state. Combining multiple analytical approaches enables us to make best use of the complementary information they offer, and provides the detail needed to infer mathematical models describing the structure and dynamics of gene regulatory networks.