PuSH - Publikationsserver des Helmholtz Zentrums München: Spatial autocorrelation in mass spectrometry imaging.

Informationen

Hinweise zu Qualitätskriterien von Zeitschriften

Open-Access-Richtlinie der Helmholtz-Gemeinschaft 2016

CC Licencences

Metriken für Publikationen

Navigation

Startseite

English

EVA: Veröffentlichen

Neuer EVA Antrag

Recherche

Erweiterte Suche

Durchblättern nach ...

... HMGU-Autoren/Konsortien

... Organisationsstruktur

... Zeitschriften

... Publikationstypen

... Forschungsdaten

... Arbeitsgruppen

... Erscheinungsjahr

Publikationen im Überblick

Statistik

HGF Fortschrittsbericht

OA Publikationen

Eintragen

Neue Publikation eintragen

Neue Publikation holen aus...

...EVA

Fehlende Publikation melden

Highlights

Suche

Hilfe & Kontakt

Ansprechpartner

Hilfe

Datenschutz

Helmholtz Open Science

Bibliometrische Indikatoren

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

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

Abstract
Metriken
Zusatzinfos

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.

Impact Factor

Scopus SNIP

Web of Science
Times Cited

Scopus
Cited By

Altmetric

5.886

1.520