Demonstrates streamlined exposure-omics analysis using an open-source package, implying efficient data integration.
Motivation Environmental exposures shape health across the life course, influencing molecular pathways, disease susceptibility, and therapeutic response. However, integrating exposome and multi-omics data poses several challenges, ranging from extensive analytical steps to ensuring trends are consistent across studies. Results To address this challenge, we developed tidyexposomics, an open-source R package that delivers an end-to-end, tidyverse-native workflow including ontology-based exposure annotation, quality control, association testing, stability assessment, multi-omics integration, network analysis, and functional enrichment. The package offers intuitive Application Programming Interface (API), modular functions, and extensive visualization tools to facilitate flexible analyses. Comprehensive analytical step tracking and result export functions enable reproducibility and consistent reporting of downstream results. By combining systematic preprocessing, statistical modeling and biologically informed interpretation in one package, tidyexposomics streamlines exposure-omics analysis. Availability and implementation The GitHub repository for tidyexposomics is available at: https://github.com/BioNomad/tidyexposomics Pre-processed example data are available at: https://doi.org/10.5281/zenodo.17049350
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Laird et al. (2026) studied this question.
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