Discrete Morse Graph Skeletonization and Application to Analaysis of Local Structures of scRNA-seq Data

Yusu Wang
University of California, San Diego (UCSD)

In recent years, topological and geometric data analysis (TGDA) has emerged as a new and promising field for processing, analyzing and understanding complex data. In this talk, I will show how the topological objects from discrete Morse theory and persistent homology can be used to extract graph skeletons of high-dimensional point cloud data. I will then describe how the resulting graph skeletonization method can be applied to study and quantify differences in (local) structures of scRNA-seq datasets across different brain regions. This is joint work with L. Magee, R. Gala, U. Sumbul, and M. Hawrylycz.

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