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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Mass spectrometry reviews Ročník 39; číslo 3; s. 245 - 291
Hlavní autoři:	Verbeeck, Nico, Caprioli, Richard M., Van de Plas, Raf
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Wiley Subscription Services, Inc 01.05.2020 John Wiley and Sons Inc
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23
ISSN:	0277-7037 1098-2787 1098-2787
DOI:	10.1002/mas.21602