Environmental Exposomics in R: PFAS, Chemical Mixtures and Metabolomics
From Chemical Co-Exposure Patterns to Metabolic Signatures and Joint Mixture Analysis
Skills you will gain:
About Workshop:
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.
Aim:
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.
Workshop Objectives:
- 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.
What you will learn?
📅 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.
🛠️ Hands-on: Profile Environmental Chemical Exposure Patterns
- Explore a prepared biomonitoring dataset in a guided notebook.
- Apply selected data-cleaning and transformation steps.
- Generate exposure-distribution plots and a correlation heatmap.
- Identify correlated chemicals and define a mixture-analysis question.
🧰 Tools Covered: R, tidyverse, ggplot2, corrplot, EPA CompTox Chemicals Dashboard.
📌 Output: An environmental exposure profile with distribution plots, a correlation heatmap, and identified co-exposure patterns.
📅 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.
🛠️ Hands-on: Identify Exposure-Associated Metabolic Features
- Analyze a prepared metabolomics dataset linked to exposure metadata.
- Apply predefined quality-filtering and preprocessing steps.
- Generate a PCA visualization of sample patterns.
- Run adjusted association models and correct for multiple testing.
- Interpret selected findings using a supplied metabolite-annotation table.
🧰 Tools Covered: R, limma, tidyverse, ggplot2, MetaboAnalyst 6.0.
📌 Output: A PCA plot and a ranked exposure–metabolite association table with effect estimates, adjusted significance values, and available annotations.
📅 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.
🛠️ Hands-on: Model Chemical Mixtures & Exposure–Response Relationships
- Fit a quantile g-computation model using a prepared teaching dataset.
- Visualize the joint mixture estimate and component contributions.
- Compare linear and spline-based response curves for one selected exposure.
- Prepare a concise interpretation of the findings and their limitations.
🧰 Tools Covered: R, qgcomp, splines, ggplot2.
📌 Output: A joint mixture-association plot, an exposure–response curve, and a concise scientific results statement.
Mentor Profile
Fee Plan
Important Dates
12 Oct 2026 Indian Standard Timing 4:30 PM
12 Oct 2026 to 14 Oct 2026 Indian Standard Timing 5:30 PM
Get an e-Certificate of Participation!
Intended For :
- 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.
Career Supporting Skills
Workshop Outcomes
By the end of the workshop, participants will be able to:
- 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.