RT Journal Article
SR Electronic
T1 Gene regulatory network inference from single-cell data using multivariate information measures
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 082099
DO 10.1101/082099
A1 Thalia E. Chan
A1 Michael P.H. Stumpf
A1 Ann C. Babtie
YR 2017
UL http://biorxiv.org/content/early/2017/09/26/082099.abstract
AB While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell data sets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available here: https://github.com/Tchanders/network_inference_tutorials. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.