Abstract: In the era of big data, integrative genomics has emerged as a powerful approach to unravel the complexities of biological systems. However, the choice of analytical methods ranging from traditional statistical techniques to advanced deep learning models plays a critical role in the success of these efforts. Through a few case studies, Dr. Li will demonstrate how genome-wide co-localization analysis, a relatively straightforward statistical method, can effectively identify shared genetic architecture of complex diseases. Conversely, he will highlight the application of deep learning methods in predicting pan-cancer copy number aberrations from RNA-seq data, showcasing its superior performance in handling high-dimensional, non-linear relationships inherent in genomic data. Finally, he will briefly discuss Wasserstein regression for analysis of population-scale single cell data.
About the speaker: Dr. Hongzhe Li is Perelman Professor of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine at the University of Pennsylvania. He is Vice Chair for Research Integration, Director of the Center of Statistics in Big Data, and former Chair of the Graduate Program in Biostatistic at Penn. He is also a Professor of Statistics and Data Science at the Wharton School and a Professor of Applied Mathematics and Computational Science at Penn. Dr. Li has been elected as a Fellow of the American Statistical Association (ASA), a Fellow of the Institute of Mathematical Statistics (IMS) and a Fellow of American Association for the Advancement of Science (AAAS). Dr. Li served on the Board of Scientific Counselors of the National Cancer Institute of NIH and regularly serves on various NIH study sections. He served as Chair of the Section on Statistics in Genomics and Genetics of the ASA and Co-Editor-in-Chief of Statistics in Biosciences. Dr. Li’s research focuses on developing statistical and computational methods for analysis of large-scale genetic, genomics and metagenomics data and theory on high dimensional statistics. He has trained over 50 PhD students and postdoctoral fellows.
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