Causal network inference from gene transcriptional time-series response to glucocorticoids [A549_OE]

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 competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of 2,768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2,768 genes and 31,945 directed edges (FDR <= 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS Overall design: overexpression samples: mRNA profiles from A549 cells stimulated with dexamethasone and overexpressing one transcription factor, over 12 hours

基因调控网络推断（Gene regulatory network inference）对于揭示基因通路间的复杂关联、为下游实验提供理论参考至关重要，最终可实现调控网络的重构。基于转录组时间序列数据的网络推断，需要对数千个基因间的因果关联进行精准、可解释且高效的判定。在此，我们开发了面向时间序列数据的Bootstrap弹性网回归（Bootstrap Elastic Net Regression from Time Series, BETS）——一种基于格兰杰因果（Granger causality）的统计框架，用于从转录组时间序列数据中恢复有向基因网络。
BETS采用弹性网回归与Bootstrap抽样得到的稳定性选择方法来推断基因间的因果关联。该方法具备高度并行化特性，可高效分析大规模转录组数据集。我们在社区基准测试DREAM4 100基因网络推断挑战赛中验证了其具有竞争力的准确率：BETS是性能相近的方法中运行速度最快的方法之一，同时还可推断因果效应为激活型还是抑制型。
我们将BETS应用于暴露于糖皮质激素的A549细胞中2768个差异表达基因的转录组时间序列数据，分析时长为12小时。最终构建了包含2768个基因与31945条有向边的调控网络（错误发现率（False Discovery Rate, FDR）≤ 0.2）。我们通过两个外部数据源对推断得到的因果网络边进行验证：一是同一糖皮质激素系统下的过表达实验，二是基因型-组织表达（Genotype-Tissue Expression, GTEx）v6项目中原代肺组织中与推断边相关的遗传变异。
BETS已作为开源软件包发布，访问地址为https://github.com/lujonathanh/BETS。
总体实验设计：过表达样本组：经地塞米松刺激且过表达单个转录因子的A549细胞的mRNA表达谱，采样周期为12小时。