Analysis and Figures from "Causal network inference from gene transcriptional time-series response to glucocorticoids"

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 This upload documents the analysis and figure files that support each numerical claim of the manuscript. Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript, assuming the home folder of port-from-della基因调控网络（Gene Regulatory Network）推断对于揭示基因通路间的复杂关联、指导下游实验并最终实现调控网络重构具有至关重要的意义。基于转录组时间序列数据的网络推断，需精准、可解释且高效地判定数千个基因间的因果关联。本研究开发了基于时间序列的自助弹性网回归（Bootstrap Elastic net regression from Time Series，简称BETS），这是一种基于格兰杰因果关系（Granger Causality）的统计框架，可从转录组时间序列数据中重构有向基因调控网络。BETS通过弹性网回归与自助抽样样本的稳定性选择，推断基因间的因果关联。该方法具备高度并行性，可高效分析大规模转录组数据集。在社区基准测试DREAM4 100基因网络推断挑战赛中，BETS展现出极具竞争力的准确率：其在同类性能方法中运算速度名列前茅，同时还可推断因果效应为激活型还是抑制型。我们将BETS应用于经糖皮质激素处理12小时的A549细胞的2768个差异表达基因的转录组时间序列数据，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）。我们通过两类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处理体系中的过表达实验，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数据。BETS已作为开源软件包发布，开源地址为https://github.com/lujonathanh/BETS。本次上传的文件包含支撑论文各项数值结论的分析代码与图片文件。Full Progeny.xlsx列出了以port-from-della作为主目录时，对应论文各项数值结论的相关代码与文件清单。