Package: multiDEGGs 1.2.1.9000

multiDEGGs: 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) <doi:10.1093/bioinformatics/btad192>.

Authors:Elisabetta Sciacca [aut, cre, cph], Myles Lewis [ctb]

multiDEGGs_1.2.1.9000.tar.gz
multiDEGGs_1.2.1.9000.zip(r-4.7)multiDEGGs_1.2.1.9000.zip(r-4.6)multiDEGGs_1.2.1.9000.zip(r-4.5)
multiDEGGs_1.2.1.9000.tgz(r-4.6-any)multiDEGGs_1.2.1.9000.tgz(r-4.5-any)
multiDEGGs_1.2.1.9000.tar.gz(r-4.7-any)multiDEGGs_1.2.1.9000.tar.gz(r-4.6-any)
multiDEGGs_1.2.1.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
multiDEGGs/json (API)

# Install 'multiDEGGs' in R:
install.packages('multiDEGGs', repos = c('https://elisabettasciacca.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/elisabettasciacca/multideggs/issues

Pkgdown/docs site:https://elisabettasciacca.github.io

Datasets:

On CRAN:

Conda:

bioinformaticsdifferential-expressioninteractive-visualizationsmultiomicsmultiomics-datanetwork-analysisomicsproteomicstranscriptomics

6.49 score 8 stars 9 scripts 571 downloads 8 exports 47 dependencies

Last updated from:6fa7a3d95f. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR211
source / vignettesOK275
linux-release-x86_64ERROR214
macos-release-arm64ERROR123
macos-oldrel-arm64ERROR111
windows-develERROR106
windows-releaseERROR125
windows-oldrelERROR108
wasm-releaseOK129

Exports:.predict_multiDEGGsget_diffNetworksget_multiOmics_diffNetworksget_sig_deggsmultiDEGGs_combined_filtermultiDEGGs_filterplot_regressionsView_diffNetworks

Dependencies:base64encbslibcachemclicommonmarkcrosstalkdigestDTevaluatefastmapfontawesomefsgluehighrhtmltoolshtmlwidgetshttpuvjquerylibjsonliteknitrlaterlazyevallifecyclemagrittrMASSmemoisemimeotelpbapplypbmcapplypromisesR6rappdirsRcpprlangrmarkdownsasssfsmiscshinyshinydashboardsourcetoolstinytexvisNetworkwithrxfunxtableyaml

Differential Network Analysis with multiDEGGs
Introduction | Installation | Quick start - Generate Differential Networks | Key Parameters of get_diffNetworks | Visualization | List All Differential Interactions | Differential Regression Plots | Differential Network Analysis with More Than Two Groups | Feature Selection and Engineering with multiDEGGs in Nested Cross-Validation | Why Nested Cross-Validation for Feature Engineering? | multiDEGGs_filter(): Pure Differential Network-Based Selection | Key Parameters | Usage Examples | Basic Usage: Pairs Only | Including Individual Genes (keep_single_genes = TRUE) | How nfilter works with keep_single_genes | multiDEGGs_combined_filter(): Hybrid Statistical and Network-Based Selection | Dynamic vs. Balanced Selection Modes | Dynamic Selection (dynamic_nfilter = TRUE) | Balanced Selection (dynamic_nfilter = FALSE) | Available Statistical Methods | Practical considerations | Feature Engineering Details | Citation

Last update: 2026-06-16
Started: 2025-05-13

Feature Selection and Feature Engineering with multiDEGGs
Feature Selection and Feature Engineering with multiDEGGs in Nested Cross-Validation | Why Nested Cross-Validation for Feature Engineering? | multiDEGGs_filter(): Pure Differential Network-Based Selection | Key Parameters | Usage Examples | Basic Usage: Pairs Only | Including Individual Genes (keep_single_genes = TRUE) | How nfilter works with keep_single_genes | multiDEGGs_combined_filter(): Hybrid Statistical and Network-Based Selection | Dynamic vs. Balanced Selection Modes | Dynamic Selection (dynamic_nfilter = TRUE) | Balanced Selection (dynamic_nfilter = FALSE) | Available Statistical Methods | Practical considerations | Feature Engineering Details | Citation

Last update: 2026-04-01
Started: 2026-02-02

Differential Network Analysis with multiDEGGs
Introduction | Installation | Quick start - Generate Differential Networks | Key Parameters of get_diffNetworks | Visualization | List All Differential Interactions | Differential Regression Plots | Differential Network Analysis with More Than Two Groups | Custom Reference Network | Citation

Last update: 2026-03-24
Started: 2026-03-23