scmTE: multivariate transfer entropy builds interpretable compact gene regulatory networks by reducing false predictions

Abstract

Gene regulatory network inference from single-cell RNA sequencing (scRNAseq) datasets has an incredible potential to discover new regulatory rules. However, current computational inference methods often suffer from excessive predictions as existing strategies fail to remove indirect or false predictions. Here, we report a new algorithm single-cell multivariate Transfer Entropy, "scmTE", that generates interpretable regulatory networks with reduced indirect and false predictions. By utilizing multivariate transfer entropy, scmTE accounts for gene-to-gene interdependence when quantifying regulatory relationships. Benchmarking against other methods using synthetic data manifested that scmTE is the unique algorithm that did not produce a hair-ball structure (due to too many predictions) and recapitulated known ground-truth relationships with high accuracy. In silico knockdown experiments shows that scmTE assigns higher scores for specific interactions important for differentiation processes. We apply scmTE to T-cell differentiation, myelopoiesis and pancreatic development and identified known and novel regulatory interactions. scmTE provides a robust approach to infer interpretable networks by effectively removing unwanted indirect relationships.