This article is part of the supplement: Statistical mass spectrometry-based proteomics
MALDI imaging mass spectrometry: statistical data analysis and current computational challenges
Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 1, 28359 Bremen, Germany
Steinbeis Innovation Center for Scientific Computing in Life Sciences, Richard-Dehmel-Str. 69, 28211 Bremen, Germany
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
BMC Bioinformatics 2012, 13(Suppl 16):S11 doi:10.1186/1471-2105-13-S16-S11Published: 5 November 2012
Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry, also called MALDI-imaging, is a label-free bioanalytical technique used for spatially-resolved chemical analysis of a sample. Usually, MALDI-imaging is exploited for analysis of a specially prepared tissue section thaw mounted onto glass slide. A tremendous development of the MALDI-imaging technique has been observed during the last decade. Currently, it is one of the most promising innovative measurement techniques in biochemistry and a powerful and versatile tool for spatially-resolved chemical analysis of diverse sample types ranging from biological and plant tissues to bio and polymer thin films. In this paper, we outline computational methods for analyzing MALDI-imaging data with the emphasis on multivariate statistical methods, discuss their pros and cons, and give recommendations on their application. The methods of unsupervised data mining as well as supervised classification methods for biomarker discovery are elucidated. We also present a high-throughput computational pipeline for interpretation of MALDI-imaging data using spatial segmentation. Finally, we discuss current challenges associated with the statistical analysis of MALDI-imaging data.