Environmental Exposomics in R: PFAS, Chemical Mixtures and Metabolomics

Environmental health researchers rarely face one chemical at a time. This three-day online workshop shows you how to analyze real-world chemical co-exposure, connect it to metabolomics data, and model the joint effect of a chemical mixture, using R. You will work through guided notebooks with prepared datasets covering PFAS, metals, pesticides, and plastic-associated chemicals. Each day ends with a finished output you can reuse in your own research: an exposure profile with a correlation heatmap, a ranked exposure–metabolite association table, and a joint mixture analysis with an exposure–response curve. The focus is on analysis you can defend: handling detection limits and missing values, correcting for multiple testing, judging annotation confidence, and separating statistical association from causation.

To equip participants with an integrated understanding of environmental exposomics by connecting chemical exposure profiling, exposure-associated metabolic features, and joint mixture analysis through reproducible, guided analytical workflows.

• Introduce environmental exposomics, biomonitoring, and the interpretation of chemical exposure measurements.
• Explain approaches for handling detection limits, missing values, skewed distributions, and correlated exposures.
• Develop familiarity with metabolomics feature tables, quality-control principles, preprocessing, and exploratory analysis.
• Demonstrate covariate-adjusted exposure–metabolite association analysis and false discovery rate correction.
• Introduce linear and nonlinear exposure–response modeling and major chemical mixture analysis methods.
• Apply quantile g-computation to estimate a joint mixture association and interpret component contributions.
• Strengthen scientific interpretation and reporting through consideration of confounding, uncertainty, annotation confidence, and study limitations.

📅 Day 1: Environmental Exposure Profiling & Chemical Mixture Characterization

• Focus: Understanding environmental exposures, biomonitoring data, and chemical co-exposure patterns through a reproducible analytical workflow. Introduction to environmental exposomics and its role in environmental health research. External exposures, internal chemical burden, and biomarkers of biological response. Priority contaminants: PFAS, metals, pesticides, and plastic-associated chemicals. Exposure routes, biological sampling matrices, and exposure timing. Principles of targeted biomonitoring, suspect screening, and non-targeted analysis. Understanding exposure-data tables, sample metadata, and relevant covariates. Handling detection limits, missing values, skewed distributions, and measurement units. Data transformation and standardization for exposure profiling. Correlation analysis, co-exposure patterns, and multicollinearity. Formulating research questions for single-chemical and mixture-based investigations.

📅 Day 2: Metabolomics Data Analysis & Exposure-Associated Metabolic Signatures

• Focus: Assessing metabolomics data quality and identifying metabolic features associated with environmental exposures. Principles of targeted and untargeted metabolomics in exposomics research. Distinguishing endogenous metabolites, xenobiotics, and xenobiotic metabolites. Understanding LC–MS feature tables, sample metadata, and metabolite annotations. Quality-control concepts: blanks, pooled QC samples, missingness, and analytical variability. Normalization, transformation, and scaling of metabolomics data. Recognizing batch effects, technical variation, and sample outliers. Principal component analysis for exploring metabolic variation. Covariate-adjusted exposure–metabolite association analysis. Effect-size interpretation, confidence intervals, and false discovery rate correction. Metabolite identification confidence, pathway context, and candidate biomarker interpretation.

📅 Day 3: Dose–Response Modeling & Joint Chemical Mixture Analysis

• Focus: Modeling exposure-related biological responses and interpreting joint mixture associations with appropriate statistical uncertainty. Distinguishing administered dose, measured concentration, and exposure biomarkers. Dose–response analysis in experiments versus exposure–response analysis in observational studies. Selecting appropriate metabolic or health-related response variables. Linear and nonlinear response relationships. Spline-based modeling and visualization of exposure–response curves. Interpreting confidence intervals, data coverage, and apparent response patterns. Overview of weighted quantile sum regression, quantile g-computation, and Bayesian kernel machine regression. Guided application of quantile g-computation to estimate a joint mixture association. Interpretation of positive and negative component contributions. Model assumptions, confounding, association versus causation, and reproducible reporting.

• Explain the relationships among external exposures, internal chemical burden, and biomarkers of biological response.
• Prepare biomonitoring data for guided analysis using appropriate cleaning and transformation steps.
• Generate exposure-distribution plots and correlation heatmaps to characterize co-exposure patterns.
• Assess prepared metabolomics data using quality-control information, missingness, and analytical variability.
• Apply predefined preprocessing steps and use PCA to explore metabolic variation.
• Run covariate-adjusted exposure–metabolite models and interpret effect estimates, confidence intervals, and FDR-adjusted significance values.
• Interpret selected metabolic features using available annotations and biological context.
• Fit a guided quantile g-computation model and interpret the joint mixture estimate and directional component contributions.
• Compare linear and spline-based exposure–response curves while considering uncertainty and data coverage.
• Prepare a concise scientific results statement that distinguishes statistical associations from causal conclusions.

Postgraduate students, Ph.D. scholars, and researchers in environmental science, toxicology, public health, epidemiology, and exposure science. Researchers studying PFAS, metals, pesticides, plastic-associated chemicals, or environmental chemical mixtures. Scientists working in metabolomics, analytical chemistry, biomonitoring, and environmental health. Bioinformaticians, biostatisticians, and data analysts interested in exposure-linked omics and mixture modeling. Academicians and research professionals seeking practical experience in reproducible environmental data analysis.