Spatially resolved transcriptomics (SRT) technologies measure gene expression at thousands of spatial locations in a tissue slice, enabling the identification of spatially varying genes and cell populations. In this talk, I will present two computational approaches for modeling spatial variation in SRT data. First, I will describe Belayer, an algorithm that models both discrete and continuous spatial variation in gene expression using techniques from complex analysis. Second, I will present STARCH, an algorithm for modeling spatial variation in copy number aberrations (CNAs), a common type of mutation present in cancer cells, and inferring the spatial localization of tumor clones. I will demonstrate the benefits of modeling spatial structure on a variety of different SRT datasets including data from the brain, skin, and tumors.

• https://doi.org/10.1016/j.cels.2022.09.002
• https://doi.org/10.1088/1478-3975/abbe99

Uthsav Chitra is a Ph.D. student at Princeton University. He is advised by Professor Ben Raphael and is supported by a National Science Foundation Graduate Research Fellowship and a Siebel Scholar award. He previously received bachelor’s degrees in math, applied math and computer science at Brown University. Uthsav’s research is broadly focused on developing statistical models and computational methods for biological problems, with a specific interest in problems involving graphs/networks or spatial structure.

All Welcome! Note this is a hybrid event in person at Wolf Conference Center 3rd floor of Hale BTM 60 Fenwood Road and on Zoom: https://partners.zoom.us/j/88978601145

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