Causal network inference from gene transcriptional time-series response to glucocorticoids.

ABSTRACT: Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show compet

SUBMITTER: Lu J 

PROVIDER: S-EPMC7875426 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature