The Herbert and Florence Irving Institute for Cancer Dynamics will continue its seminar series on the topic of mathematical sciences underpinning cancer research.
On Wednesday, April 9th (2:00 PM ET), IICD welcomes Jean Fan (Johns Hopkins University). Seminar hosted by Jellert Gaublomme. The seminar will take place in person in Schermerhorn Hall 603. To attend the seminar remotely, please register here: https://columbiauniversity.zoom.us/meeting/register/tJcudumuqzgvEtO_Vu_qHiWbMWv_fc9Ma7B9
Title: Multi-Sample Comparative Spatial Omics Data Analysis
Abstract: Recent advances in high-throughput spatially resolved transcriptomics technologies now enable high-throughput molecular profiling of cells while maintaining their spatial organization within tissues. Application of these technologies provides the opportunity to contribute to a more complete understanding of how cellular spatial organization relates to tissue function and how cellular spatial organization is altered in disease. New statistical approaches and scalable computational tools are needed to connect these molecular states and spatial-contextual differences. In this talk, I will provide an overview of spatially resolved transcriptomics technologies and associated computational analysis methods developed by my lab. Specifically, to facilitate spatial molecular comparisons across structurally matched tissue sections from replicates, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. Likewise, to facilitate comparison cell-type spatial organizational patterns, we developed CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, to quantify pair-wise cell-type spatial relationships across length scales. We demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples to identify consistent as well as patient and sample-specific cell-type spatial relationships. We anticipate that such statistical approaches and computational methods for analyzing spatially resolved transcriptomic data will offer the potential to identify and characterize the heterogeneity of cells within their spatial contexts and contribute to important fundamental biological insights regarding how tissues are organized in both the healthy and diseased settings.
Bio: Jean Fan is a member of the faculty of Biomedical Engineering in the Center for Computational Biology at Johns Hopkins University. Her research team is interested in understanding the molecular and spatial-contextual factors shaping cellular identity and heterogeneity. She develops new open-source computational software for analyzing single-cell multi-omic and imaging data that can be tailored and applied to diverse cancer types and biological systems. Prof. Fan is the founder, director, and lead software developer for the non-profit organization CuSTEMized, which provides personalized STEM picture storybooks to encourage young girls to see themselves as scientists. She currently serves as an editor for PLoS Computational Biology. The impact of Prof. Fan's work has been recognized by several awards and honors, including the Forbes 30 Under 30, the Nature Research Award for Inspiring Science, the NSF CAREER Award, and the Presidential Early Career Award for Scientists and Engineers (PECASE).
If you would like to meet one-on-one (possibility via zoom) or attend the lunch or dinner with the speaker, please contact the event organizer.
