Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R

Hdl Handle:
http://hdl.handle.net/10545/621536
Title:
Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R
Authors:
Zhang, Zhongheng; Murtagh, Fionn ( 0000-0002-0589-6892 ) ; Van Poucke, Sven; Lin, Su; Lan, Peng
Abstract:
Big 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.
Affiliation:
Zhejiang University; University of Derby; Ziekenhuis Oost-Limburg; Fujian Medical University
Citation:
Zhang 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.2017.02.05
Publisher:
AME Publishing Company
Journal:
Annals of Translational Medicine
Issue Date:
Feb-2017
URI:
http://hdl.handle.net/10545/621536
DOI:
10.21037/atm.2017.02.05
Additional Links:
http://atm.amegroups.com/article/view/13789/pdf; http://atm.amegroups.com/article/view/13789/14063
Type:
Article
Language:
en
ISSN:
23055847
Sponsors:
N/A
Appears in Collections:
Department of Electronics, Computing & Maths

Full metadata record

DC FieldValue Language
dc.contributor.authorZhang, Zhonghengen
dc.contributor.authorMurtagh, Fionnen
dc.contributor.authorVan Poucke, Svenen
dc.contributor.authorLin, Suen
dc.contributor.authorLan, Pengen
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.en
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
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