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dc.contributor.authorInglese, Paoloen
dc.contributor.authorCorreia, Gonçaloen
dc.contributor.authorPruski, Pamelaen
dc.contributor.authorGlen, Roberten
dc.contributor.authorTakats, Zoltanen
dc.date.accessioned2019-05-03T23:30:37Z
dc.date.available2019-05-03T23:30:37Z
dc.date.issued2019-05en
dc.identifier.issn0003-2700
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/292332
dc.description.abstractSupervised 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 co-expression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.
dc.format.mediumPrint-Electronicen
dc.languageengen
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHumansen
dc.subjectNeoplasmsen
dc.subjectSpectrometry, Mass, Electrospray Ionizationen
dc.subjectSpectrometry, Mass, Matrix-Assisted Laser Desorption-Ionizationen
dc.subjectProtein Transporten
dc.subjectMolecular Imagingen
dc.titleColocalization Features for Classification of Tumors Using Desorption Electrospray Ionization Mass Spectrometry Imaging.en
dc.typeArticle
prism.endingPage6540
prism.issueIdentifier10en
prism.publicationDate2019en
prism.publicationNameAnalytical chemistryen
prism.startingPage6530
prism.volume91en
dc.identifier.doi10.17863/CAM.39158
dcterms.dateAccepted2019-04-23en
rioxxterms.versionofrecord10.1021/acs.analchem.8b05598en
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-05en
dc.contributor.orcidInglese, Paolo [0000-0001-6179-9643]
dc.contributor.orcidPruski, Pamela [0000-0001-8012-7922]
dc.contributor.orcidGlen, Robert [0000-0003-1759-2914]
dc.contributor.orcidTakats, Zoltan [0000-0002-0795-3467]
dc.identifier.eissn1520-6882
rioxxterms.typeJournal Article/Reviewen


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International