Multivariate analysis of complex gene expression and clinical phenotypes with genetic marker data
Identifieur interne : 002555 ( Main/Exploration ); précédent : 002554; suivant : 002556Multivariate analysis of complex gene expression and clinical phenotypes with genetic marker data
Auteurs : Joseph Beyene [Canada] ; David Tritchler [Canada]Source :
- Genetic Epidemiology [ 0741-0395 ] ; 2007.
English descriptors
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Abstract
This paper summarizes contributions to group 12 of the 15th Genetic Analysis Workshop. The papers in this group focused on multivariate methods and applications for the analysis of molecular data including genotypic data as well as gene expression microarray measurements and clinical phenotypes. A range of multivariate techniques have been employed to extract signals from the multi‐feature data sets that were provided by the workshop organizers. The methods included data reduction techniques such as principal component analysis and cluster analysis; latent variable models including structural equations and item response modeling; joint multivariate modeling techniques as well as multivariate visualization tools. This summary paper categorizes and discusses individual contributions with regard to multiple classifications of multivariate methods. Given the wide variety in the data considered, the objectives of the analysis and the methods applied, direct comparison of the results of the various papers is difficult. However, the group was able to make many interesting comparisons and parallels between the various approaches. In summary, there was a consensus among authors in group 12 that the genetic research community should continue to draw experiences from other fields such as statistics, econometrics, chemometrics, computer science and linear systems theory. Genet. Epidemiol. 31(Suppl. 1):S103–S109, 2007. © 2007 Wiley‐Liss, Inc.
Url:
DOI: 10.1002/gepi.20286
Affiliations:
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Epidemiol.</title><idno type="ISSN">0741-0395</idno><idno type="eISSN">1098-2272</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2007">2007</date><biblScope unit="volume">31</biblScope><biblScope unit="issue">S1</biblScope><biblScope unit="page" from="S103">S103</biblScope><biblScope unit="page" to="S109">S109</biblScope></imprint><idno type="ISSN">0741-0395</idno></series><idno type="istex">25EB2A9C3D922F522B36FFA91EA6D2EEFF502D1D</idno><idno type="DOI">10.1002/gepi.20286</idno><idno type="ArticleID">GEPI20286</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0741-0395</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>SNPs</term><term>gene expression</term><term>microarrays</term><term>multivariate analysis</term><term>multivariate phenotypes</term><term>simulated data</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper summarizes contributions to group 12 of the 15th Genetic Analysis Workshop. The papers in this group focused on multivariate methods and applications for the analysis of molecular data including genotypic data as well as gene expression microarray measurements and clinical phenotypes. A range of multivariate techniques have been employed to extract signals from the multi‐feature data sets that were provided by the workshop organizers. The methods included data reduction techniques such as principal component analysis and cluster analysis; latent variable models including structural equations and item response modeling; joint multivariate modeling techniques as well as multivariate visualization tools. This summary paper categorizes and discusses individual contributions with regard to multiple classifications of multivariate methods. Given the wide variety in the data considered, the objectives of the analysis and the methods applied, direct comparison of the results of the various papers is difficult. However, the group was able to make many interesting comparisons and parallels between the various approaches. In summary, there was a consensus among authors in group 12 that the genetic research community should continue to draw experiences from other fields such as statistics, econometrics, chemometrics, computer science and linear systems theory. Genet. 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