Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experime...

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Bibliographic Details
Published in:Mass spectrometry reviews Vol. 39; no. 3; pp. 245 - 291
Main Authors: Verbeeck, Nico, Caprioli, Richard M., Van de Plas, Raf
Format: Journal Article
Language:English
Published: United States Wiley Subscription Services, Inc 01.05.2020
John Wiley and Sons Inc
Subjects:
Animals
clustering
Computer applications
Data Analysis
Data processing
DESI
Desorption
Humans
imaging mass spectrometry
Ions
LAESI
LAICP
Learning algorithms
Machine learning
MALDI
manifold learning
Mass spectrometry
Mass Spectrometry - methods
Mass spectroscopy
matrix factorization
Molecular Imaging - methods
Review
Scientific imaging
SIMS
Spatial distribution
unsupervised
Unsupervised Machine Learning
matrix factorization
imaging mass spectrometry
data analysis
MALDI
unsupervised
DESI
SIMS
clustering
LAESI
LAICP
machine learning
manifold learning
ISSN:0277-7037, 1098-2787, 1098-2787
Online Access:Get full text
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Summary:Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high‐dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data‐driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry‐based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1–47, 2019.
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ISSN:0277-7037
1098-2787
1098-2787
DOI:10.1002/mas.21602