Package: CorNetto 0.99.1

CorNetto: Knowledge-Guided Multi-Omic Correlation Network Analysis

Builds knowledge-guided multi-omic correlation networks from normalized transcriptomic, proteomic, and metabolomic abundance data. Group-specific correlation networks are compared with the Fisher z-difference test, either across all within-omic pairs in one or every assay without generating cross-omic pairs, or across candidate edges supplied by a prior-knowledge network, and the resulting differential network is summarized into node-level rewiring scores that identify features whose interaction patterns change between groups. Rewiring scores can be compared with group-label permutation reference distributions, and networks can be restricted to pathway-focused neighbourhoods, converted to 'igraph' objects, or exported as Cytoscape-ready node and edge tables. The package is built on Bioconductor containers so that multi-omic assays and sample metadata are managed consistently throughout the workflow.

Authors:Bradley Ward [aut, cre], Jean-Luc Balligand [ctb], Laurence Bamps [ctb], Patrice D. Cani [ctb], Julien De Greef [ctb], Joseph P. Dewulf [ctb], Vincent Haufroid [ctb], Benoît Kabamba [ctb], Sébastien Pyr dit Ruys [ctb], Didier Vertommen [ctb], Jean Cyr Yombi [ctb], Leïla Belkhir [ths, fnd], Laure Elens [ths, fnd], Sofina COVID Solidarity Fund [fnd]