Biostatistics Dept Seminar: Modeling the geometry of spatial gene expression

Biostatistics Department Seminar 

Title: Modeling the geometry of spatial gene expression

Abstract: Recent spatial transcriptomics (ST) technologies make high-throughput measurements of gene expression at thousands of locations in a 2-D tissue slice. However, due to current ST technological limitations, these measurements are highly sparse—thus complicating the identification and analysis of spatial gene expression patterns. In this talk, I will present computational and machine learning approaches that overcome these technological limitations by modeling the latent geometry of a 2-D tissue slice. First, I will present Belayer, an algorithm for learning both discrete and continuous variation in layered tissues using tools from complex analysis (conformal mapping, harmonic functions). Second, I will present GASTON and GASTON-Mix, unsupervised and interpretable deep learning algorithms to learn a "topographic map" of a 2-D tissue slice. I will show how our algorithms uncover subtle gene expression patterns across a diverse range of biological systems including the brain, skin, liver, and tumor microenvironment. 

Speakers

Uthsav Chitra is an Assistant Professor of Computer Science and faculty in the Data Science and AI Institute at the Johns Hopkins University. His research broadly develops statistical/machine learning methods for analyzing high-dimensional and multi-modal biological data.

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2025-2026 Monday Seminar Series

All seminars are held at 12:05 PM via Zoom and onsite. View all seminar information here.