biostudies-literatureMonroy Kuhn JMEuropean Research Councilvbac042https://www.ebi.ac.uk/biostudies/studies/S-EPMC9710706biostudies-literatureUnknown2(1)<h4>Summary</h4>Today's immense growth in complex biological data demands effective and flexible tools for integration, analysis and extraction of valuable insights. Here, we present CoNI, a practical R package for the unsupervised integration of numerical omics datasets. Our tool is based on partial correlations to identify putative confounding variables for a set of paired dependent variables. CoNI combines two omics datasets in an integrated, complex hypergraph-like network, represented as a weighted undirected graph, a bipartite graph, or a hypergraph structure. These network representations form a basis for multiple further analyses, such as identifying priority candidates of biological importance or comparing network structures dependent on different conditions.<h4>Availability and implementation</h4>The R package CoNI is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/CoNI/) and GitLab (https://gitlab.com/computational-discovery-research/coni). It is distributed under the GNU General Public License (version 3).<h4>Supplementary information</h4>Supplementary data are available at <i>Bioinformatics Advances</i> online.Bioinformatics advancesCorrelation-guided Network Integration (CoNI), an R package for integrating numerical omics data that allows multiform graph representations to study molecular interaction networks.PMC9710706695054Lutter DMonroy Kuhn JMMiok VfalseCorrelation-guided Network Integration (CoNI), an R package for integrating numerical omics data that allows multiform graph representations to study molecular interaction networks.<h4>Summary</h4>Today's immense growth in complex biological data demands effective and flexible tools for integration, analysis and extraction of valuable insights. Here, we present CoNI, a practical R package for the unsupervised integration of numerical omics datasets. Our tool is based on partial correlations to identify putative confounding variables for a set of paired dependent variables. CoNI combines two omics datasets in an integrated, complex hypergraph-like network, represented as a weighted undirected graph, a bipartite graph, or a hypergraph structure. These network representations form a basis for multiple further analyses, such as identifying priority candidates of biological importance or comparing network structures dependent on different conditions.<h4>Availability and implementation</h4>The R package CoNI is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/CoNI/) and GitLab (https://gitlab.com/computational-discovery-research/coni). It is distributed under the GNU General Public License (version 3).<h4>Supplementary information</h4>Supplementary data are available at <i>Bioinformatics Advances</i> online.2022-01-01T00:00:00Z20222025-04-21T23:08:17.252Z2025-04-05T19:06:18.674ZS-EPMC97107063669935210.1093/bioadv/vbac042