Numerical experiments with MALDI Imaging data

Authors: Jan Hendrik Kobarg Peter Maass Janina Oetjen Oren Tropp Eyal Hirsch Chen Sagiv Mohammad Golbabaee Pierre Vandergheynst

Publish Date: 2013/11/06

Volume: 40, Issue: 3, Pages: 667-682

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Abstract

This article does not present new mathematical results it solely aims at discussing some numerical experiments with MALDI Imaging data However these experiments are based on and could not be done without the mathematical results obtained in the UNLocX project They tackle two obstacles which presently prevent clinical routine applications of MALDI Imaging technology In the last decade matrixassisted laser desorption/ionization imaging mass spectrometry MALDIIMS has developed into a powerful bioanalytical imaging modality MALDI imaging data consists of a set of mass spectra which are measured at different locations of a flat tissue sample Hence this technology is capable of revealing the full metabolic structure of the sample under investigation Sampling resolution as well as spectral resolution is constantly increasing presently a conventional 2D MALDI Imaging data requires up to 100 GB per dataset A major challenge towards routine applications of MALDI Imaging in pharmaceutical or medical workflows is the high computational cost for evaluating and visualizing the information content of MALDI imaging data This becomes even more critical in the near future when considering cohorts or 3D applications Due to its size and complexity MALDI Imaging constitutes a challenging test case for high performance signal processing In this article we will apply concepts and algorithms which were developed within the UNLocX project to MALDI Imaging data In particular we will discuss a suitable phase space model for such data and report on implementations of the resulting transform coders using GPU technology Within the MALDI Imaging workflow this leads to an efficient baseline removal and peak picking The final goal of data processing in MALDI Imaging is the discrimination of regions having different metabolic structures We introduce and discuss socalled softsegmentation maps which are obtained by nonnegative matrix factorization incorporating sparsity constraints

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