Abstract
Summary
The integration of metabolomics with other omics (“multi-omics”) offers complementary insights into disease biology. However, this integration remains challenging due to the fragmented landscape of current methodologies, which often require programming experience or bioinformatics expertise. Moreover, existing approaches are limited in their ability to accommodate unidentified metabolites, resulting in the exclusion of a significant portion of data from untargeted metabolomics experiments. Here, we introduce iModMix - Integrative Module Analysis for Multi-omics Data , a novel approach that uses a graphical lasso to construct network modules for integration and analysis of multi-omics data. iModMix uses a horizontal integration strategy, allowing metabolomics data to be analyzed alongside proteomics or transcriptomics to explore complex molecular associations within biological systems. Importantly, it can incorporate both identified and unidentified metabolites, addressing a key limitation of existing methodologies. iModMix is available as a user-friendly R Shiny application that requires no programming experience ( https://imodmix.moffitt.org ), and it includes example data from several publicly available multi-omic studies for exploration. An R package is available for advanced users ( https://github.com/biodatalab/iModMix ).
Availability and implementation
Shiny application: https://imodmix.moffitt.org . The R package and source code: https://github.com/biodatalab/iModMix .
Full Text
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