multiDEGGs: Single or Multiomic Differential Network Analysis for Biomarker Discovery and Feature Engineering for Predictive Modeling

: Motivation: Modern clinical trials increasingly leverage high-throughput omic data for patient stratification and biomarker discovery. While traditional differential gene expression analysis disregards the networked nature of molecular entities and produces extensive gene lists with limited interpretability, differential network analysis has emerged as a crucial complementary analysis for comparative studies. Here, we present multiDEGGs (multiomics differentially expressed gene-gene pairs), a CRAN R package that enables differential network analysis in single or multiomic scenarios. Methods: multiDEGGs uses a multiomic graph framework where, for each data type, differential networks are generated and the statistical significance of each link is evaluated. These networks are then integrated into a comprehensive visualization that allows interactive exploration of cross-omic patterns and interactions. The package facilitates seamless integration into cross-validation machine learning pipelines for feature selection and identification of biologically relevant interactions for feature engineering. Results: We validated multiDEGGs using two cohorts of patients with rheumatoid arthritis. For each treatment group, multiomic differential interactions were identified, and eight machine learning models were trained to predict treatment resistance using synovial RNA sequencing data. We systematically compared multiDEGGs against seven feature selection methods. On average, area under the receiver operating characteristic curve values obtained with multiDEGGs showed an improvement of 0.10 compared to conventional filters. Availability and Implementation: multiDEGGs is freely available on CRAN, and the source code is available on GitHub at https://github.com/elisabettasciacca/multiDEGGs under GPL-3.0 license. Source code used to generate figures and analyses conducted in this paper is available at https://github.com/EMR-bioinformatics/multiDEGGs_supplementary.