  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.
