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Article: Detecting outlier samples in microarray data

TitleDetecting outlier samples in microarray data
Authors
Issue Date2009
PublisherBerkeley Electronic Press. The Journal's web site is located at http://www.bepress.com/sagmb
Citation
Statistical Applications In Genetics And Molecular Biology, 2009, v. 8 n. 1, article no. 13 How to Cite?
AbstractIn this paper, we address the problem of detecting outlier samples with highly different expression patterns in microarray data. Although outliers are not common, they appear even in widely used benchmark data sets and can negatively affect microarray data analysis. It is important to identify outliers in order to explore underlying experimental or biological problems and remove erroneous data. We propose an outlier detection method based on principal component analysis (PCA) and robust estimation of Mahalanobis distances that is fully automatic. We demonstrate that our outlier detection method identifies biologically significant outliers with high accuracy and that outlier removal improves the prediction accuracy of classifiers. Our outlier detection method is closely related to existing robust PCA methods, so we compare our outlier detection method to a prominent robust PCA method. Copyright ©2009 The Berkeley Electronic Press. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/58817
ISSN
2011 Impact Factor: 1.517
2015 SCImago Journal Rankings: 0.954
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorShieh, ADen_HK
dc.contributor.authorHung, YSen_HK
dc.date.accessioned2010-05-31T03:37:25Z-
dc.date.available2010-05-31T03:37:25Z-
dc.date.issued2009en_HK
dc.identifier.citationStatistical Applications In Genetics And Molecular Biology, 2009, v. 8 n. 1, article no. 13en_HK
dc.identifier.issn1544-6115en_HK
dc.identifier.urihttp://hdl.handle.net/10722/58817-
dc.description.abstractIn this paper, we address the problem of detecting outlier samples with highly different expression patterns in microarray data. Although outliers are not common, they appear even in widely used benchmark data sets and can negatively affect microarray data analysis. It is important to identify outliers in order to explore underlying experimental or biological problems and remove erroneous data. We propose an outlier detection method based on principal component analysis (PCA) and robust estimation of Mahalanobis distances that is fully automatic. We demonstrate that our outlier detection method identifies biologically significant outliers with high accuracy and that outlier removal improves the prediction accuracy of classifiers. Our outlier detection method is closely related to existing robust PCA methods, so we compare our outlier detection method to a prominent robust PCA method. Copyright ©2009 The Berkeley Electronic Press. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherBerkeley Electronic Press. The Journal's web site is located at http://www.bepress.com/sagmben_HK
dc.relation.ispartofStatistical Applications in Genetics and Molecular Biologyen_HK
dc.rightsStatistical Applications in Genetics and Molecular Biology. Copyright © Berkeley Electronic Press.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subject.meshColonic Neoplasms - diagnosis - geneticsen_HK
dc.subject.meshDatabases, Geneticen_HK
dc.subject.meshHumansen_HK
dc.subject.meshOligonucleotide Array Sequence Analysis - statistics & numerical dataen_HK
dc.subject.meshOutliers, DRG - statistics & numerical dataen_HK
dc.subject.meshPrincipal Component Analysisen_HK
dc.titleDetecting outlier samples in microarray dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1544-6115&volume=8, Issue 1&spage=Article 13&epage=&date=2009&atitle=Detecting+outlier+samples+in+microarray+dataen_HK
dc.identifier.emailHung, YS:yshung@eee.hku.hken_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.2202/1544-6115.1426en_HK
dc.identifier.pmid19222380en_HK
dc.identifier.scopuseid_2-s2.0-62449263773en_HK
dc.identifier.hkuros163899en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-62449263773&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume8en_HK
dc.identifier.issue1en_HK
dc.identifier.spagearticle no. 13-
dc.identifier.epagearticle no. 13-
dc.identifier.eissn1544-6115-
dc.identifier.isiWOS:000263440600016-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridShieh, AD=9237952100en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.citeulike6850077-

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