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On the importance of mathematical methods for analysis of MALDI-imaging mass spectrometry data

: Trede, D.; Kobarg, J.H.; Oetjen, J.; Thiele, H.; Maass, P.; Alexandrov, T.

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Journal of integrative bioinformatics : JIB 9 (2012), N.1, Art.189
ISSN: 1613-4516
ISSN: 1432-4385
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer MEVIS ()

In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies. A typical MALDI-imaging data set contains 108 to 109 intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.