Spatiochemical Characterization of the Pancreas Using Mass Spectrometry Imaging and Topological Data Analysis

Mass SpectrometryImaging (MSI) is a technique used to identifythe spatial distribution of molecules in tissues. An MSI experimentresults in large amounts of high dimensional data, so efficient computationalmethods are needed to analyze the output. Topological Data Analysis(TDA) has proven to be effective in all kinds of applications. TDAfocuses on the topology of the data in high dimensional space. Lookingat the shape in a high dimensional data set can lead to new or differentinsights. In this work, we investigate the use of Mapper, a form ofTDA, applied on MSI data. Mapper is used to find data clusters insidetwo healthy mouse pancreas data sets. The results are compared toprevious work using UMAP for MSI data analysis on the same data sets.This work finds that the proposed technique discovers the same clustersin the data as UMAP and is also able to uncover new clusters, suchas an additional ring structure inside the pancreatic islets and abetter defined cluster containing blood vessels. The technique canbe used for a large variety of data types and sizes and can be optimizedfor specific applications. It is also computationally similar to UMAPfor clustering. Mapper is a very interesting method, especially itsuse in biomedical applications.