Data-Driven Insights into Disease Prognosis Through Spatially Resolved Transcriptomics
Statistics Seminars: Fall 2025
Department of Mathematical Sciences, IU Indianapolis
Organizer: Honglang Wang (hlwang at iu dot edu)
Talk time: 12:15-1:15pm (EST), 09/02/2025, Tuesday
Zoom Meetings: We host our seminars via zoom meetings: Join from computer or mobile by clicking: Zoom to Join or use Meeting ID: 845 0989 4694 with Password: 113959 to join.
Title: Data-Driven Insights into Disease Prognosis Through Spatially Resolved Transcriptomics
Abstract: Recent advancements in RNA sequencing technologies, such as bulk RNA sequencing, single-cell RNA sequencing, and spatially resolved transcriptomics, provide high-dimensional gene expression data that offer insights into genetic behavior. Analyzing these data presents significant statistical challenges, particularly in modeling spatial dependencies, handling sparsity, and integrating multi-modal information. In this talk, I will present a novel statistical framework for identifying high-risk cells and tissue regions from single-cell spatially resolved transcriptomics data. Traditional clustering methods associate cell types with disease attributes but fail to assign risk at the individual cell or tissue location level. We propose a latent representation approach with domain adaptation techniques to transfer disease attributes from patients to single cells. These statistical approaches provide rigorous frameworks for modeling complex biological systems, enhancing our understanding of disease mechanisms, and identifying potential therapeutic targets.
Bio: Dr. Debolina Chatterjee is a Postdoctoral Fellow in the Department of Biostatistics and Health Data Science at the Indiana University School of Medicine. She earned her Ph.D. in Mathematical Sciences (Statistics) from Indiana University–Purdue University Indianapolis. Her research integrates advanced statistical methodologies with genomics, focusing on developing novel techniques for modeling and analyzing high-dimensional biological data. She applies these methods to analyze data from next generation sequencing technologies to uncover disease associated signatures. Additionally, her work extends to reliability theory and applied stochastic processes, where she explores innovative approaches to modeling uncertainty and system reliability.
Welcome to join us to learn more about Dr. Chatterjee’s research work via Zoom!