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SEMgraph

Overview

SEMgraph estimates networks and causal relations in complex systems through Structural Equation Modeling (SEM). SEMgraph runs with a directed graph (adjacency matrix) that encodes the hypothesized or data-driven causal links among variables. It comes with the following functionalities:

  • Interchangeable model representation as either an igraph object or the corresponding SEM in lavaan syntax. Model management functions include graph-to-SEM conversion, automated covariance matrix regularization, graph conversion to DAG, and tree (arborescence) from correlation matrices.

  • Heuristic filtering, node and edge weighting, resampling and parallelization settings for fast fitting in case of very large models.

  • Automated data-driven model building and improvement, through causal structure learning and bow-free interaction search and latent variable confounding adjustment.

  • Perturbed paths finding, community searching and sample scoring, together with graph plotting utilities, tracing model architecture modifications and perturbation (i.e., activation or repression) routes.

Installation

The latest stable version can be installed from CRAN:

install.packages("SEMgraph")

The latest development version can be installed from GitHub:

# install.packages("devtools")
devtools::install_github("fernandoPalluzzi/SEMgraph")

References

Grassi M, Palluzzi F, Tarantino B. SEMgraph: an R package for causal network inference of high-throughput data with structural equation models. Bioinformatics, 2022 Aug 30; 38(20):btac567. https://doi.org/10.1093/bioinformatics/btac567

Grassi M, Tarantino B. SEMgsa: topology-based pathway enrichment analysis with structural equation models. BMC Bioinformatics, 2022 Aug 17; 23(1):344. https://doi.org/10.1186/s12859-022-04884-8

Grassi M, Tarantino B. SEMtree: tree-based structure learning methods with structural equation models. Bioinformatics, 2023 June 09; 39(6):btad377. https://doi.org/10.1093/bioinformatics/btad377

Grassi M, Tarantino B. SEMbap: Bow-free covariance search and data de-correlation. PLoS Comput Biol, 2024 Sep 11; 20(9):e1012448. https://doi.org/10.1371/journal.pcbi.1012448

Grassi M, Tarantino B. SEMdag: Fast learning of Directed Acyclic Graphs via node or layer ordering. PLoS ONE. 2025 Jan 08; 20(1): e0317283. https://doi.org/10.1371/journal.pone.0317283