Zoom: https://illinois.zoom.us/j/81155290075?pwd=SuS6UScQEmyjMDX6k2GT3sNxzZQhJo.1
Abstract:
Many factors governing cell development - like gene expression and 3D DNA organization - have been identified, and researchers have collected various related genomics datasets. Measurements at single-cell resolution, in particular, reveal heterogeneity in tissues like the brain and tumor, allowing us to study cell-type-specific differences. However, how the different factors function together to regulate cells remains unclear. This knowledge gap is partly due to the technical constraints in simultaneously measuring different genomic signals in single cells. Moreover, measuring factors like the 3D organization of the DNA in single cells is resource-intensive, leading to missing information.
In this talk, I will discuss our research to develop state-of-the-art machine learning methods to integrate genomics datasets and fill this knowledge gap. First, I will present AGWOT, an optimal transport-based method to align different single-cell measurements. Our method's ability to simultaneously map cells and features across datasets generates new biological hypotheses. Next, I will talk about scGraphHiC, a deep learning framework that performs graph deconvolution to extract single-cell DNA organization from bulk data using single-cell gene expression as a guiding signal. Our integrative modeling produces mappings and data predictions for single cells, leading to interesting biological insights for heterogeneous tissues.
Bio:
Ritambhara Singh is the John E. Savage Assistant Professor of Computer Science and Data Science and a member of the Center for Computational Molecular Biology at Brown University. Her research lab develops machine learning methods with the goals of data integration and model interpretation for biological and biomedical applications. Prior to joining Brown, she was a post-doctoral researcher in the Noble Lab at the University of Washington. She completed her Ph.D. in 2018 from the University of Virginia with Dr. Yanjun Qi as her advisor. Ritambhara has received the NHGRI Genomic Innovator Award and Brown University’s Richard B. Salomon Faculty Research Award for developing deep learning methods to integrate and model genomics datasets. She also received the Dean’s Award for Excellence in Teaching at Brown.
Faculty Host: Tandy Warnow
Meeting ID: 811 5529 0075; Password: csillinois