Dimensionality reduction in spatial dimension of mass spectrometry imaging data
Chen, Xi
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https://hdl.handle.net/2142/104001
Description
Title
Dimensionality reduction in spatial dimension of mass spectrometry imaging data
Author(s)
Chen, Xi
Contributor(s)
Ochoa, Idoia
Issue Date
2019-05
Keyword(s)
bioinformatics
dimensionality reduction
mass spectroscopy, biomedical image processing
pattern clustering
Abstract
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.
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