exposomeShiny

exposomeShiny performs comprehensive computational analyses of exposome data to identify associations and biological pathways linking environmental exposures to health outcomes.

Key Features:

• Data pre-processing: Implements normalization, missing value imputation, and procedures to handle limits of detection (LOD).
• Descriptive analysis: Provides functions to summarize and visualize exposome data distributions and summaries.
• Principal Component Analysis and Hierarchical Clustering: Performs PCA and hierarchical clustering to identify exposure patterns and sample relationships.
• Exposome-Wide Association Studies (ExWAS) and Variable Selection: Conducts ExWAS and variable selection methods to detect associations between exposures and health outcomes.
• Omic Data Integration: Supports integration of exposome data with omic layers through single association analyses and multi-omic approaches.
• Post-exposome Analyses: Enables gene–environment interaction studies, pathway analysis, and queries to the Comparative Toxicogenomics Database (CTD) for biological contextualization.

Scientific Applications:

• Association discovery: Detects exposure–health outcome associations using ExWAS and variable selection methods.
• Exposure pattern characterization: Identifies and interprets exposure profiles via PCA and hierarchical clustering.
• Multi-omic mechanistic inference: Integrates exposome and omic data with pathway analysis and CTD queries to elucidate biological mechanisms.
• Gene–environment interaction analysis: Investigates interactions between genetic factors and environmental exposures.

Methodology:

Normalization, missing value imputation, limit of detection (LOD) handling, principal component analysis, hierarchical clustering, ExWAS, variable selection, single association analyses, multi-omic approaches, gene–environment interaction analyses, and pathway analysis with Comparative Toxicogenomics Database (CTD) queries.