omicRexposome

omicRexposome evaluates associations between environmental exposures and high-dimensional omic data to support exposome research.

Key Features:

• R/Bioconductor architecture: Implements data structures and functions within the R/Bioconductor ecosystem.
• Exposome data management (rexposome): Uses the rexposome project framework to define and manage chemical, outdoor, social, and lifestyle exposure data across life stages.
• Omic layer integration: Supports integration of methylomes, transcriptomes, proteomes, and metabolomes.
• Association testing (limma): Applies limma for statistical association testing between exposome variables and health outcomes or omic features.
• Data integration techniques: Implements multi co-inertia analysis via omicade4 and multi-canonical correlation analysis via PMA to integrate exposome and omic datasets.
• High-dimensional data handling: Manages high-dimensional datasets and supports large-scale studies such as HELIX involving over 1000 mother-child pairs.
• Object-oriented framework: Provides object-oriented classes and methods tailored for high-dimensional exposome data.

Scientific Applications:

• Disease etiology investigation: Investigates environmental contributions to disease etiology, including respiratory diseases and outcomes influenced by early-life exposures.
• Life-course exposure studies: Studies cumulative exposures across the life course to assess how prenatal and childhood environments shape biological responses.
• Exposure–omics discovery: Facilitates discovery of associations between specific exposures such as maternal smoking and cadmium and molecular changes including DNA methylation, transcriptomic alterations, and metabolomic signatures.

Methodology:

Implements a generic object-oriented framework for high-dimensional exposome data and applies limma for association testing, with omicade4 (multi co-inertia analysis) and PMA (multi-canonical correlation analysis) for exposome–omic data integration.