Abstract
Single-cell technologies have transformed biomedical research in the last few years. With singlecell sequencing, we can now simultaneously measure thousands of genomics features in a large number of cells, which provides an ultrahigh resolution phenotypic map for each individual. However, single-cell protocols are complex. Even with the most sensitive platforms, the data are often sparse and noisy. Recent development of single-cell multi-omics and spatial transcriptomics technologies further imposed additional challenges on data integration. In this talk, I will presentseveral machine learning methods that my group recently developed for single-cell and spatial transcriptomics data analysis. I will discuss methods for simultaneous denoising, clustering and batch effect correction, single-cell multi-omics data integration, identification of spatially variable genes, generation of super-resolution gene expression, and inference of cell type distribution in spatial transcriptomics. I will illustrate our methods by showing results from ongoing collaborations on cardiometabolic disease and applications to brain and cancer data.
Short Bio
Dr. Li’s research interests include statistical genetics and genomics, bioinformatics, and computational biology. The central theme of her current research is to use statistical and computational approaches to understand cellular heterogeneity in human-disease-relevant tissues, to characterize gene expression diversity across cell types, to study the patterns of cell state transition and crosstalk of various cells using data generated from single-cell and spatial transcriptomics studies, and to translate these findings to the clinics. In addition to methods development, Dr. Li is also interested in collaborating with researchers seeking to identify complex disease susceptibility genes and acting cell types. She is Director of Biostatistics for the Gene Therapy Program at Penn, where she advises biostatistics and bioinformatics analysis for various gene therapy studies. She is also Chair of the Graduate Program in Biostatistics. Dr. Li actively serves in the scientific community. She served as a regular member for the NIH Genomics, Computational Biology and Technology (GCAT) study section for 6 years, and the NHGRI Center for Inherited Disease Research (CIDR) for 3 years. She is an Associate Editor of Annals of Applied Statistics, Statistics in Biosciences, PLOS Computational Biology, and Human Genetics and Genomics Advances. She is an elected member of the International Statistical Institute and a Fellow of the American Statistical Association.