Hosted by the School of Medicine at Cardiff University
Tissues contain multiple cell types whose spatial arrangement facilitates specific biological functions. Recent spatial transcriptomics technologies measure RNA expression at thousands of locations in a 2D tissue slice quantifying the spatial distribution of cell types and spatial variation in gene expression. Due to limitations in technology and cost, these measurements are typically sparse with high rates of missing data. I will present algorithms that overcome these technical limitations by modeling the latent geometry of individual tissue slices and by integrating measurements from multiple slices. We use these algorithms to analyze variation of cell types and gene expression in normal tissues; derive gene expression gradients in the tumor microenvironment; reconstruct 3D tissues; and describe spatiotemporal changes in expression during development.
Ben Raphael is a Graduate Class of 1991 Professor of Computer Science at Princeton University, and an affiliate faculty member at the Columbia University Irving Institute for Cancer Dynamics, the New York Genome Center, and the Rutgers Cancer Institute of New Jersey. His research in computational biology and bioinformatics focuses on cancer evolution, single-cell and spatial DNA/RNA sequencing, network/pathway analysis of germline and somatic mutations, and structural variation in human and cancer genomes. His group’s algorithms have been used in The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). He is Fellow of the Association for Computing Machinery (ACM) and the International Society for Computational Biology (ISCB). He is the recipient of the 2021 Innovator Award from the ISCB, the Alfred P. Sloan Research Fellowship, the NSF CAREER award, and a Career Award at the Scientific Interface from the Burroughs Wellcome Fund.
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