TY - JOUR T1 - Network inference from single-cell data using multivariate information measures JF - bioRxiv DO - 10.1101/082099 SP - 082099 AU - Thalia E. Chan AU - Michael P.H. Stumpf AU - Ann C. Babtie Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/04/26/082099.abstract N2 - Gene expression is robustly controlled in space and time by networks of transcriptional regulators. Single cell data now allow us to probe and map these networks and cellular transcriptional states in unprecedented detail, but the heterogeneity and large sample sizes that characterise these data pose new analytical challenges. Here we develop an algorithm that exploits these features to infer gene (co-)regulatory networks from single cell data using multivariate information measures. Information theory allows us to detect complex non-linear relationships and explore statistical dependencies between multiple genes in detail. We thoroughly evaluate the performance of our algorithm in comparison to existing methods, demonstrate the benefits of considering multivariate information measures, and illustrate its application to several datasets. We provide guidelines for the use of information theoretical based algorithms, and software implementing our method to enable the identification of putative functional relationships and mechanistic hypotheses from single cell transcriptomic data. ER -