Dimensionality reduction in spatial dimension of mass spectrometry imaging data

Mass spectrometry imaging (MSI, also called imaging MS) is an emerging technique in mass spectrometry. It integrates ion information gained from mass spectrum and spatial distribution across the tissue surface of interest. Matrix-assisted laser desorption ionization (MALDI) imaging is a common technology used for ionization in MSI. Many of the analysis pipelines for MALDI-MSI data involve investigation of spatial structure of the set of ion images contained in the single MSI data, such as peak picking, filtering, and clustering of ion images based on their spatial similarity. Data generated from an experiment can contain up to 50,000 mass spectra, each with thousands to tens of thousands of intensity values. Due to the high spatial and mass resolution, a major consideration in designing analytical methods for MALDI-MSI data is memory and runtime efficiency. Therefore, developing appropriate methods for data reduction is necessary in the field of MALDI-MSI analysis. This project explores various techniques of dimensionality reduction in the spatial dimension, as well as how they affect downstream data analysis.U of I Onlyundergraduate senior thesis not recommended for general acces