On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data

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doi doi:10.2390/biecoll-jib-2012-189
submission November 21, 2011
published March 21, 2012
NCBI PubMed PubMed ID 22433312

Dennis Trede, Jan Hendrik Kobarg, Janina Oetjen, Herbert Thiele, Peter Maass and Theodore Alexandrov

Correspondence should be addressed to:
Dennis Trede
Steinbeis Innovation Center SCiLS, Richard-Dehmel-Str. 69, 28211 Bremen, Germany
ed.slics@nulledert


Abstract

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.

Reference

Dennis Trede, Jan Hendrik Kobarg, Janina Oetjen, Herbert Thiele, Peter Maass and Theodore Alexandrov. On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data. Journal of Integrative Bioinformatics, 9(1):189, 2012. Online Journal: http://journal.imbio.de/index.php?paper_id=189
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