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dc.contributor.authorLubbock, Alexander L. R.
dc.contributor.authorKatz, Elad
dc.contributor.authorHarrison, David J.
dc.contributor.authorOverton, Ian M.
dc.date.accessioned2014-05-06T15:01:02Z
dc.date.available2014-05-06T15:01:02Z
dc.date.issued2013-07
dc.identifier116329739
dc.identifier670cdaa7-bb66-4ea6-a627-6ff2142c8d44
dc.identifier000323603200089
dc.identifier84883561578
dc.identifier.citationLubbock , A L R , Katz , E , Harrison , D J & Overton , I M 2013 , ' TMA Navigator: network inference, patient stratification and survival analysis with tissue microarray data ' , Nucleic Acids Research , vol. 41 , no. W1 , pp. W562-W568 . https://doi.org/10.1093/nar/gkt529en
dc.identifier.issn0305-1048
dc.identifier.otherORCID: /0000-0001-9041-9988/work/64034304
dc.identifier.urihttps://hdl.handle.net/10023/4713
dc.descriptionScottish Funding Council (SFC) and the Chief Scientist’s Office (CSO) (to D.H.); Royal Society of Edinburgh Scottish Government Fellowship co-funded by Marie Curie Actions and the UK Medical Research Council (MRC) (to I.O.). Funding for open access charge: Royal Society of Edinburgh.en
dc.description.abstractTissue microarrays (TMAs) allow multiplexed analysis of tissue samples and are frequently used to estimate biomarker protein expression in tumour biopsies. TMA Navigator (www.tmanavigator.org) is an open access web application for analysis of TMA data and related information, accommodating categorical, semi-continuous and continuous expression scores. Non-biological variation, or batch effects, can hinder data analysis and may be mitigated using the ComBat algorithm, which is incorporated with enhancements for automated application to TMA data. Unsupervised grouping of samples (patients) is provided according to Gaussian mixture modelling of marker scores, with cardinality selected by Bayesian information criterion regularization. Kaplan-Meier survival analysis is available, including comparison of groups identified by mixture modelling using the Mantel-Cox log-rank test. TMA Navigator also supports network inference approaches useful for TMA datasets, which often constitute comparatively few markers. Tissue and cell-type specific networks derived from TMA expression data offer insights into the molecular logic underlying pathophenotypes, towards more effective and personalized medicine. Output is interactive, and results may be exported for use with external programs. Private anonymous access is available, and user accounts may be generated for easier data management.
dc.format.extent7
dc.format.extent4594595
dc.language.isoeng
dc.relation.ispartofNucleic Acids Researchen
dc.subjectFalse discovery rateen
dc.subjectBreast-canceren
dc.subjectProtein expressionen
dc.subjectQuantitative-analysisen
dc.subjectGene networksen
dc.subjectHuman-diseaseen
dc.subjectPathwaysen
dc.subjectValidationen
dc.subjectOptimizationen
dc.subjectTechnologyen
dc.subjectQH426 Geneticsen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subject.lccQH426en
dc.titleTMA Navigator: network inference, patient stratification and survival analysis with tissue microarray dataen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.identifier.doi10.1093/nar/gkt529
dc.description.statusPeer revieweden


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