HSPH Biostatistics and DFCI Data Science Colloquium
Monday March 10th at 4:00pm
HSPH Kresge G2

Eric Sun
PhD Candidate, Department of Biomedical Informatics
Stanford University

Aging is a highly complex process and the greatest risk factor for many chronic diseases including cardiovascular disease, dementia, stroke, diabetes, and cancer. Recent spatial and single-cell omics technologies have enabled the high-dimensional profiling of complex biology including that underlying aging. As such, new machine learning and computational methods are needed to unlock important insights from spatial and single-cell omics datasets. First, I present the development of high-resolution machine learning models (‘spatial aging clocks’) that can measure the aging of individual cells in the brain. Using these spatial aging clocks, I discovered that some cell types can dramatically influence the aging of nearby cells. Next, I present new computational and statistical methods for overcoming the gene coverage limitations of existing spatially resolved single-cell omics technologies, which have enabled the discovery of gene pathways underlying the spatial effects of brain aging.