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Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516)

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 10^8 to 10^9 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.

DOI: 10.2390/biecoll-jib-2012-189

On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data. Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516), 9(1), 2012

Online-Journal: http://journal.imbio.de/article.php?aid=189
URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2012/5206

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