On including nonlinearity in multivariate analysis of imaging SIMS data

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 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.

Femtosecond laser desorption ionization mass spectrometry imaging and multivariate analysis of lipids in pancreatic tissue

Femtosecond laser desorption ionization mass spectrometry was used to obtain mass spectrometric (MS) images of lipids in human pancreatic tissue. The resulting MS images were analyzed using multivariate analysis, specifically principal component analysis and maximum a posteriori (MAP) reconstruction. Both analysis methods showed that the MS images can be separated into lipid and non-lipid areas. MAP analysis further indicated that the lipid areas are composed of phosphatidylcholines and fatty acids. However, definitive identification of the lipids cannot be made because none of the intact parent ions of phosphatidylcholine, sphingomyelins, and/or other lipids were observed. The MAP analysis also revealed that the non-lipid areas could be separated into components that are due to the sample chemical treatment and topography.