Jingyi Jessica LiProfessor of Statistics
University of California, Los Angeles
Helen Putnam Fellow, 2022-23
Radcliffe Institute for Advanced Study at Harvard University
Introduced by: Martin Vingron, Chair, ISCB Awards Committee
Time: Tuesday, July 25, 8:45 AM - 9:45 AM
Room: Lumière Auditorium
Using Synthetic Controls to Enhance the Statistical Rigor in Genomics Data Science
The rapid development of genomics technologies has propelled fast advances in genomics data science. Although numerous computational algorithms have been developed to analyze genomics data, the statistical rigor of data analyses has been largely overlooked. Motivated by the use of negative and positive controls in experiments, I propose to enhance the reliability of data analysis by using synthetic controls generated from real data under specified hypotheses. I will demonstrate this strategy in identifying differentially expressed genes from bulk and single-cell RNA-seq data. Specifically, to support this strategy in single-cell and spatial omics data analysis, I will introduce our simulator scDesign3 for synthetic control generation. Moreover, to ensure computational efficiency, I will introduce a p-value-free strategy for high-throughput feature screening without requiring multiple rounds of synthetic control generation. Overall, using synthetic controls is an effective strategy to increase the statistical rigor of genomics data analysis and improve the reliability of analysis results.
Jingyi Jessica Li is a Professor in the Department of Statistics and Data Science at the University of California, Los Angeles, where she holds secondary appointments in the Departments of Biostatistics, Computational Medicine, and Human Genetics. She leads a research group called the Junction of Statistics and Biology, which focuses on developing interpretable statistical methods for biomedical data analysis.
Jessica received her Ph.D. from the University of California, Berkeley, and her B.S. from Tsinghua University. Her research interests include quantifying the central dogma, extracting hidden information from bulk, single-cell, and spatial multi-omics data, and ensuring statistical rigor in biomedical data analysis. She emphasizes using in silico negative controls to avoid false discoveries.
Jessica has received multiple awards in recognition of her work, including the NSF CAREER Award, Sloan Research Fellowship, Johnson & Johnson WiSTEM2D Math Scholar Award, MIT Technology Review 35 Innovators Under 35 China, Harvard Radcliffe Fellowship, and COPSS Emerging Leader Award.