Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Nico Verbeeck, Richard M. Caprioli, Raf Van de Plas

Research output: Contribution to journalReview articleScientificpeer-review

6 Citations (Scopus)
26 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)245-291
JournalMass Spectrometry Reviews
Volume39
Issue number3
DOIs
Publication statusPublished - 2019

Keywords

  • clustering
  • data analysis
  • DESI
  • imaging mass spectrometry
  • LAESI
  • LAICP
  • machine learning
  • MALDI
  • manifold learning
  • matrix factorization
  • SIMS
  • unsupervised

Fingerprint Dive into the research topics of 'Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry'. Together they form a unique fingerprint.

  • Cite this