Data Science

Data science is essential in environmental exposomics research, used to analyze complex data to reveal patterns and trends in exposure and health effects. By processing large datasets, data science enables researchers to identify risk factors and predict outcomes, leading to more targeted interventions and improved public health strategies.

Projects

A central theme of our work is to develop analysis tools to enhance the ability of researchers to employ novel analysis techniques to their data. This includes the development of R packages that are designed to be user-friendly and robust, enabling researchers to efficiently analyze complex datasets. By providing these tools, we aim to facilitate cutting-edge research and promote the application of innovative methodologies in the field of environmental health and beyond.

EnvirOmix

The goal of EnvirOmix is to provide a suite of functions for the analysis of environmental and multiomics data.

Epiomics

Epiomics provides a collection of fast and flexible functions for the analysis of omics data in observational studies.

Developing data science tools to enable innovative scientific advances

Beyond simply helping to analyze data, we develop open-source software to reduce barriers to researchers examining the impact of environmental factors on the mechanisms of human disease. Our commitment to open-source software ensures that these tools, and others like them, are accessible to the broader scientific community, promoting transparency, collaboration, and reproducibility in research. By making these resources available, we empower researchers to advance our collective understanding of the impact of environmental factors on human health.

Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease

Goodrich, J. A., et al. (2024) Environment International, 108930
https://doi.org/10.1016/j.envint.2024.108930.

An Extension of Latent Unknown Clustering Integrating Multi-omics Data (LUCID) Incorporating Incomplete Omics Data

Zhao, Y., Jia, Q., Goodrich, J., Darst, B., & Conti, D. V. (2024) Bioinformatics Advances
https://doi.org/10.1093/bioadv/vbae123

Associations of dietary intake and longitudinal measures of per- and polyfluoroalkyl substances (PFAS) in predominantly Hispanic young Adults: A multicohort study

Hampson, H. E., et al. (2024). Environment international, 185, 108454.
https://doi.org/10.1016/j.envint.2024.108454.

Training

Multi-omics Boot Camp: Analysis of Omics Data for Research Studies

Dr. Goodrich participates as an instructor for the Multi-omics Boot Camp: Analysis of Omics Data for Research Studies, part of the Columbia Mailman School of Public Health SHARP Trainings. This is a three-day intensive workshop which provides an overview of multiple approaches to analyze multiple omic data types measured on the same individuals or via the use of summary statistic data. As an instructor, Dr. Goodrich has much experience in developing and applying methods for omic analysis in genetic and environmental epidemiology.

Multi-omics Boot Camp

Associate Director for Data Science

Jesse Goodrich, PhD

Dr. Jesse Goodrich is an assistant professor in the Department of Population and Public Health Sciences at the University of Southern California. His research focuses on integrating multiomic data to understand the molecular mechanisms linking endocrine and metabolism disrupting chemicals with complex diseases such as type 2 diabetes and non-alcoholic fatty liver disease. Jesse obtained his PhD in the Integrative Physiology department at the University of Colorado Boulder. During his PhD, he performed a variety of interventional and observational human research studies examining factors related to glucose metabolism and cardiorespiratory fitness. During his post-doc at USC, Jesse’s research was focused on using metabolomic measures to understand the biological mechanisms underlying the relationship between exposure to persistent organic chemicals and susceptibility to Type 2 diabetes in children and young adults.

jagoodri@usc.edu