Multi-Omic Differentially Expressed Gene-Gene Pairs

Performs multi-omic differential network analysis by revealing differential interactions between molecular entities (genes, proteins, transcription factors, or other biomolecules) across the omic datasets provided. For each omic dataset, a differential network is constructed where links represent statistically significant differential interactions between entities. These networks are then integrated into a comprehensive visualization using distinct colors to distinguish interactions from different omic layers. This unified display allows interactive exploration of cross-omic patterns, such as differential interactions present at both transcript and protein levels. For each link, users can access differential statistical significance metrics (p values or adjusted p values, calculated via robust or traditional linear regression with interaction term) and differential regression plots. The methods implemented in this package are described in Sciacca et al. (2023) .

multiDEGGs

Differentially Expressed Gene-Gene pairs in multi omic data

multiDEGGs

The multiDEGGs package test for differential gene-gene correlations across different groups of samples in multi omic data.
Specific gene-gene interactions can be explored and gene-gene pair regression plots can be interactively shown.

Installation

Install from CRAN:
install.packages("multiDEGGs")

Install from Github:
devtools::install_github("elisabettasciacca/multiDEGGs")

Example

Load package and sample data

library(multiDEGGs)  
data("synthetic_metadata")  
data("synthetic_rnaseqData")  
data("synthetic_proteomicData")
data("synthetic_OlinkData")

Generate differential networks:

assayData_list <- list("RNAseq" = synthetic_rnaseqData,
                       "Proteomics" = synthetic_proteomicData,
                       "Olink" = synthetic_OlinkData)

deggs_object <- get_diffNetworks(assayData = assayData_list,
                                 metadata = synthetic_metadata,
                                 category_variable = "response",
                                 regression_method = "lm",
                                 padj_method = "bonferroni",
                                 verbose = FALSE,
                                 show_progressBar = FALSE,
                                 cores = 2)

Visualise interactively (will open a shiny interface)

View_diffNetworks(deggs_object)

Get a table listing all the significant interactions found in each category

get_multiOmics_diffNetworks(deggs_object, sig_threshold = 0.05)

Plot differential regression fits for a single interaction
plot_regressions(deggs_object, assayDataName = "RNAseq", gene_A = "MTOR", gene_B = "AKT2", legend_position = "bottomright")

Citation

To cite package ‘multiDEGGs’ in publications use:

Sciacca E, Wang S, Pitzalis C, Lewis M (2026). “multiDEGGs: Single or Multiomic Differential Network Analysis for Biomarker Discovery and Feature Engineering for Predictive Modeling.” Computational and Structural Biotechnology Journal, 35(1),

doi:10.34133/csbj.0001 https://doi.org/10.34133/csbj.0001.

A BibTeX entry for LaTeX users is

@Article{, title = {multiDEGGs: Single or Multiomic Differential Network Analysis for Biomarker Discovery and Feature Engineering for Predictive Modeling}, author = {Elisabetta Sciacca and Susan S. Wang and Costantino Pitzalis and Myles J. Lewis}, journal = {Computational and Structural Biotechnology Journal}, year = {2026}, volume = {35}, number = {1}, pages = {0001}, doi = {10.34133/csbj.0001}, }