Speaker:
Abstract: Advances in single cell (scRNA-seq) and spatial transcriptomics (ST) have revolutionized the study of complex tissues, with assays now able to economically capture gene expression for hundreds of thousands of cells or tissue locations. Unfortunately, the efficiency and scalability of standard analysis pipelines has not kept pace with the rapid growth in dataset size and development of scRNA-seq/ST collections. One promising approach for addressing this computational challenge is to leverage approximate matrix factorization methods from the field of randomized numerical linear algebra (RNLA). This talk will provide a brief overview of RNLA (with a specific focus on randomized SVD) and then describe two RNLA-based methods we recently developed for gene set analysis of scRNA-seq data (RESET) and spatially-informed dimensionality reduction of ST data (RASP).
Bio: Rob is an Associate Professor of Biomedical Data Science at Dartmouth College and Director of the Graduate Program in Quantitative Biomedical Sciences. Rob's research spans the domains of bioinformatics, biostatistics and applied mathematics with a current focus on methods for single cell and spatial transcriptomics data.
Zoom Meeting: https://duke.zoom.us/j/93095727432
Meeting ID: 930 9572 7432