Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/100720
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Type: Journal article
Title: Feature extraction for proteomics imaging mass spectrometry data
Author: Winderbaum, L.
Koch, I.
Gustafsson, O.
Meding, S.
Hoffmann, P.
Citation: Annals of Applied Statistics, 2015; 9(4):1973-1996
Publisher: Institute of Mathematical Statistics
Issue Date: 2015
ISSN: 1932-6157
1941-7330
Statement of
Responsibility: 
Lyron J. Winderbaum, Inge Koch, Ove J. R. Gustafsson, Stephan Meding and Peter Hoffmann
Abstract: Imaging mass spectrometry (IMS) has transformed proteomics by providing an avenue for collecting spatially distributed molecular data. Mass spectrometry data acquired with matrix assisted laser desorption ionization (MALDI) IMS consist of tens of thousands of spectra, measured at regular grid points across the surface of a tissue section. Unlike the more standard liquid chromatography mass spectrometry, MALDI-IMS preserves the spatial information inherent in the tissue. Motivated by the need to differentiate cell populations and tissue types in MALDI-IMS data accurately and efficiently, we propose an integrated cluster and feature extraction approach for such data. We work with the derived binary data representing presence/absence of ions, as this is the essential information in the data. Our approach takes advantage of the spatial structure of the data in a noise removal and initial dimension reduction step and applies k -means clustering with the cosine distance to the high-dimensional binary data. The combined smoothing-clustering yields spatially localized clusters that clearly show the correspondence with cancer and various noncancerous tissue types. Feature extraction of the high-dimensional binary data is accomplished with our difference in proportions of occurrence (DIPPS) approach which ranks the variables and selects a set of variables in a data-driven manner. We summarize the best variables in a single image that has a natural interpretation. Application of our method to data from patients with ovarian cancer shows good separation of tissue types and close agreement of our results with tissue types identified by pathologists.
Rights: © Institute of Mathematical Statistics, 2015
DOI: 10.1214/15-AOAS870
Grant ID: http://purl.org/au-research/grants/arc/LP110100693
Published version: http://dx.doi.org/10.1214/15-AOAS870
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