Effective and Scalable Causal Inference from Gene Expression Time Series

Abstract

Gene regulatory network inference holds great potential to uncover the biological mechanisms of disease and inform downstream experiments. However, effective inference of such causal relations is not straightforward. Methods for network inference must demonstrate accuracy to handle molecular interactions, statistical signifcance to decide on thresholds, and scalability to handle high-throughput sequencing assays. We introduce BETS (Bootstrap Elastic net regression from Time Series) to address these issues. BETS makes three major contributions: 1) it uses the elastic net regression penalty to handle correlated genes, 2) it ranks edges based on a new measure of their stability, the "bootstrap frequency", and 3) it is highly parallelized, allowing analysis of datasets of thousands of genes in only a few days. Through these three innovations, our method has ranked 3rd in AUROC (out of 17) and 6th in AUPR (out of 22) in the DREAM4 100-gene community benchmark for gene regulatory network inference. Importantly, our method is one of the fastest methods compared with methods of similar performance . We next run on the GR project data, consisting of 2768 differentially-expressed genes across 12 timepoints. We infer two causal networks, which we analyze for their biological relevance. Finally, we evaluate on multiple sources of held-out data, including overexpression data from the same experimental system and literature-curated regulatory relationships.