SEMgraph：用结构方程模型进行高吞吐量数据因果网络分析的R软件包
SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models
摘要：
随着分子生物学和医学中高通量测序（HTS）的出现，对用于建模复杂生物系统的可扩展统计解决方案的需求变得至关重要。越来越多的平台和可能的实验场景提出了整合大量新的异构数据和现有知识的问题，以测试新的假设并提高我们对生理过程和疾病的理解。虽然网络理论提供了一个框架来表示生物系统并研究其隐藏的属性，但不同的算法仍然提供低再现性和鲁棒性，依赖于用户定义的设置，并且可解释性差。在这里，我们讨论R包SEMgraph，结合网络分析和结构方程模型（SEM）的框架内的因果推理。它提供了一个完全自动化的工具包，将复杂的生物系统作为多变量网络进行管理，通过对模型架构和扰动的数据驱动评估来确保鲁棒性和可重复性，这很容易解释系统组件之间的因果关系。此外，SEMgraph还提供了几个函数，用于扰动路径查找，模型简化和并行化选项，用于分析大型交互网络。
英文摘要：
With the advent of high-throughput sequencing (HTS) in molecular biology and medicine, the need for scalable statistical solutions for modeling complex biological systems has become of critical importance. The increasing number of platforms and possible experimental scenarios raised the problem of integrating large amounts of new heterogeneous data and current knowledge, to test novel hypotheses and improve our comprehension of physiological processes and diseases. Although network theory provided a framework to represent biological systems and study their hidden properties, different algorithms still offer low reproducibility and robustness, dependence on user-defined setup, and poor interpretability. Here we discuss the R package SEMgraph, combining network analysis and causal inference within the framework of structural equation modeling (SEM). It provides a fully automated toolkit, managing complex biological systems as multivariate networks, ensuring robustness and reproducibility through data-driven evaluation of model architecture and perturbation, that is readily interpretable in terms of causal effects among system components. In addition, SEMgraph offers several functions for perturbed path finding, model reduction, and parallelization options for the analysis of large interaction networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
