The Ma Lab at Fred Hutchinson Cancer Center specializes in statistical and computational methods for microbiome data. We employ a variety of statistical learning methods, ranging from dimensionality reduction, graphical models, and high-dimensional inference, to address the analytical challenges faced with interpreting complex metagenomic data. The long-term goals of our research are to enhance biomarker discoveries through powerful and robust statistical inference, and to translate these findings to advance clinical research.
Keywords: network analysis, high-dimensional inference, data integration, microbiome, cancer prevention, aging
I was recently invited to write about careers in Biostatistics for high school students interested in careers in STEM and healthcare. If you are curious about careers in Biostatistics or cancer research, please check out this article. Huge thanks to Kristen Bergsman who helped write the article!
Jing received an R01 award from the National Institute of General Medical Sciences for the project on “Statistical Methods for Network-based Integrative Analysis of Microbiome Data”.
This project is in collaboration with Drs. Ali Shojaie, Yue Wang, and Robert Kaplan.
Jing received an award from The Translational Data Science Integrated Research Center for the pilot project on “Systems biology analysis of the immunomodulatory influence of circulating gut microbe-derived metabolites after transplantation”.
This project is in collaboration with Dr. Kate Markey, and will analyze a novel blood sample-derived data set. Collected from patients who underwent allogeneic stem cell transplantation, these samples have already been analyzed using state of the art flow cytometry and metabolomics methods. We now aim to a) develop new understanding of the links between microbial metabolites and immune function, and b) develop novel computational approaches to analyze these types of data sets.
Congratulations to Kun Yue, who has been named a winner of Best Student Paper for the 2021 WNAR Student Paper Competition!
Her paper “REHE: Fast Variance Components Estimation for Linear Mixed Models” proposes a new method for estimating the variance components in linear mixed models.
Jing received an award from The Pathogen Associated Malignancies Integrated Research Center for the pilot project on “Statistical Methods for Network-based Analysis of the Colorectal Cancer Microbiome”.
This project is in collaboration with Dr. Amanda Phipps (associate professor of epidemiology, University of Washington, and associate professor in the Public Health Sciences Division), Dr. Sam Minot (associate director of data science applications, Hutch Data Core) and Dr. Neelendu Dey (assistant professor in the Clinical Research Division).