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Signal preprocessing, multivariate analysis and software tools for MA(LDI)‐TOF mass spectrometry imaging for biological applications
Author(s) -
Ràfols Pere,
Vilalta Dídac,
Brezmes Jesús,
Cañellas Nicolau,
del Castillo Esteban,
Yanes Oscar,
Ramírez Noelia,
Correig Xavier
Publication year - 2016
Publication title -
mass spectrometry reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.035
H-Index - 126
eISSN - 1098-2787
pISSN - 0277-7037
DOI - 10.1002/mas.21527
Subject(s) - preprocessor , visualization , mass spectrometry imaging , computer science , segmentation , data pre processing , identification (biology) , software , pattern recognition (psychology) , data mining , artificial intelligence , pixel , chemistry , mass spectrometry , botany , chromatography , biology , programming language
Mass spectrometry imaging (MSI) is a label‐free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach. MSI data processing is challenging due to several factors. The large volume of mass spectra involved in a MSI experiment makes choosing the correct computational strategies critical. Furthermore, pixel to pixel variation inherent in the technique makes choosing the correct preprocessing steps critical. The primary aim of this review was to provide an overview of the data‐processing steps and tools that can be applied to an MSI experiment, from preprocessing the raw data to the more advanced strategies for image visualization and segmentation. This review is particularly aimed at researchers performing MSI experiments and who are interested in incorporating new data‐processing features, improving their computational strategy, and/or desire access to data‐processing tools currently available. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:281–306, 2018