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BY-NC-ND 4.0 license Open Access Published by De Gruyter October 18, 2016

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

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


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.

Published Online: 2016-10-18
Published in Print: 2012-3-1

© 2012 The Author(s). Published by Journal of Integrative Bioinformatics.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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