Repository logo
 

Colocalization Features for Classification of Tumors Using Desorption Electrospray Ionization Mass Spectrometry Imaging.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Correia, Gonçalo 
Glen, Robert C 

Abstract

Supervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection of the distinct molecular characteristics of cancerous tissues. Currently, two types of supervised analyses are mainly used on MSI data: pixel-wise segmentation of sample images and whole-sample-based classification. A large number of mass spectra associated with each MSI sample can represent a challenge for designing models that simultaneously preserve the overall molecular content while capturing valuable information contained in the MSI data. Furthermore, intensity-related batch effects can introduce biases in the statistical models. Here we introduce a method based on ion colocalization features that allows the classification of whole tissue specimens using MSI data, which naturally preserves the spatial information associated the with the mass spectra and is less sensitive to possible batch effects. Finally, we propose data visualization strategies for the inspection of the derived networks, which can be used to assess whether the correlation differences are related to coexpression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.

Description

Keywords

Humans, Molecular Imaging, Neoplasms, Protein Transport, Spectrometry, Mass, Electrospray Ionization, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization

Journal Title

Anal Chem

Conference Name

Journal ISSN

0003-2700
1520-6882

Volume Title

91

Publisher

American Chemical Society (ACS)