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Cassese, A.* ; Ellis, S.* ; Ogrinc Potočnik, N.* ; Burgermeister, E.* ; Ebert, M.* ; Walch, A.K. ; van den Maagdenberg, A.M.* ; McDonnell, L.A.* ; Heeren, R.M.* ; Balluff, B.*

Spatial autocorrelation in mass spectrometry imaging.

Anal. Chem. 88, 5871-5878 (2016)
Postprint DOI PMC
Open Access Green
Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. In microprobe MSI, images are created through a grid-wise interrogation of individual spots by mass spectrometry across a surface. Classical statistical tests for within-sample comparisons fail as close-by measurement spots violate the assumption of independence of these tests, which can lead to an increased false-discovery rate. For spatial data this effect is referred to as spatial autocorrelation. In this study we investigated spatial autocorrelation in three different matrix-assisted laser desorption/ionization MSI datasets. These datasets cover different molecular classes (metabolites/drugs, lipids, and proteins) and different spatial resolutions ranging from 20 µm to 100 µm. Significant spatial autocorrelation was detected in all three datasets and found to increase with decreasing pixel size. To enable statistical testing for differences in mass signal intensities between regions of interest within MSI datasets, we propose the use of Conditional Autoregressive (CAR) models. We show that by accounting for spatial autocorrelation, discovery rates (i.e. the ratio between the features identified and the total number of features) could be reduced between 21% and 69%. The reliability of this approach was validated by control mass signals based on prior knowledge. In light of the advent of larger MSI datasets based on either an increased spatial resolution or 3D datasets, accounting for effects due to spatial autocorrelation becomes even more indispensable. Here we propose a generic and easily applicable workflow to enable within-sample statistical comparisons.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Cortical Spreading Depression; Statistical-analysis; Mouse Model; Laser-beam; Migraine
ISSN (print) / ISBN 0003-2700
e-ISSN 1520-6882
Journal Analytical Chemistry
Quellenangaben Volume: 88, Issue: 11, Pages: 5871-5878 Article Number: , Supplement: ,
Publisher American Chemical Society (ACS)
Publishing Place Washington
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Research Unit Analytical Pathology (AAP)
Institute of Pathology (PATH)