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dc.contributor.authorZhang, Zhongheng
dc.contributor.authorMurtagh, Fionn
dc.contributor.authorVan Poucke, Sven
dc.contributor.authorLin, Su
dc.contributor.authorLan, Peng
dc.date.accessioned2017-04-03T09:10:02Z
dc.date.available2017-04-03T09:10:02Z
dc.date.issued2017-02
dc.identifier.citationZhang Z, Murtagh F, Van Poucke S, Lin S, Lan P. Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R. Ann Transl Med 2017;5(4):75. doi: 10.21037/atm.2017.02.05en
dc.identifier.issn23055847
dc.identifier.doi10.21037/atm.2017.02.05
dc.identifier.urihttp://hdl.handle.net/10545/621536
dc.description.abstractBig data clinical research typically involves thousands of patients and there are numerous variables available. Conventionally, these variables can be handled by multivariable regression modeling. In this article, the hierarchical cluster analysis (HCA) is introduced. This method is used to explore similarity between observations and/or clusters. The result can be visualized using heat maps and dendrograms. Sometimes, it would be interesting to add scatter plot and smooth lines into the panels of the heat map. The inherent R heatmap package does not provide this function. A series of scatter plots can be created using lattice package, and then background color of each panel is mapped to the regression coefficient by using custom-made panel functions. This is the unique feature of the lattice package. Dendrograms and color keys can be added as the legend elements of the lattice system. The latticeExtra package provides some useful functions for the work.
dc.description.sponsorshipN/Aen
dc.language.isoenen
dc.publisherAME Publishing Companyen
dc.relation.urlhttp://atm.amegroups.com/article/view/13789/pdfen
dc.relation.urlhttp://atm.amegroups.com/article/view/13789/14063en
dc.subjectHierarchical cluster analysisen
dc.subjectDendrogramen
dc.subjectClinical dataen
dc.subjectHeat mapen
dc.titleHierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with Ren
dc.typeArticleen
dc.contributor.departmentZhejiang Universityen
dc.contributor.departmentUniversity of Derbyen
dc.contributor.departmentZiekenhuis Oost-Limburgen
dc.contributor.departmentFujian Medical Universityen
dc.identifier.journalAnnals of Translational Medicineen
dcterms.dateAccepted2017-01-18
refterms.dateFOA2019-02-28T15:41:18Z
html.description.abstractBig data clinical research typically involves thousands of patients and there are numerous variables available. Conventionally, these variables can be handled by multivariable regression modeling. In this article, the hierarchical cluster analysis (HCA) is introduced. This method is used to explore similarity between observations and/or clusters. The result can be visualized using heat maps and dendrograms. Sometimes, it would be interesting to add scatter plot and smooth lines into the panels of the heat map. The inherent R heatmap package does not provide this function. A series of scatter plots can be created using lattice package, and then background color of each panel is mapped to the regression coefficient by using custom-made panel functions. This is the unique feature of the lattice package. Dendrograms and color keys can be added as the legend elements of the lattice system. The latticeExtra package provides some useful functions for the work.


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