Graphical Exploration of Gene Expression Data: A Comparative Study of Three Multivariate Methods
This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the...
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| Veröffentlicht in: | Biometrics 2003-12, Vol.59 (4), p.1131-1139 |
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| container_title | Biometrics |
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| creator | Wouters, Luc Göhlmann, Hinrich W. Bijnens, Luc Kass, Stefan U. Molenberghs, Geert Lewi, Paul J. |
| description | This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. Proper weighting is important, since it enables less reliable data to be down-weighted and more reliable information to be emphasized. |
| doi_str_mv | 10.1111/j.0006-341X.2003.00130.x |
| format | Article |
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It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. 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Box 1354, 9600 Garsington Road , Oxford OX4 2DQ , U.K: Blackwell Publishing</publisher><subject>Bioinformatics ; Biometry - methods ; Biplot ; Consultant's Forum ; Correspondence factor analysis ; Data mining ; Data visualization ; Datasets ; Gene Expression ; Gene expression data ; Genes ; Genetic mapping ; Leukemia ; Microarray data ; Models, Genetic ; Models, Statistical ; Multivariate Analysis ; Multivariate exploratory data analysis ; Oligonucleotide Array Sequence Analysis - methods ; Principal component analysis ; Principal components analysis ; Reproducibility of Results ; Spectral map analysis ; Spectroscopic analysis ; Statistical variance ; T lymphocytes ; Term weighting</subject><ispartof>Biometrics, 2003-12, Vol.59 (4), p.1131-1139</ispartof><rights>Copyright 2003 The International Biometric Society</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5290-c953447efa0f54ca4c7b6f384382f688efbb7abe95cea7ea587b2e5f773ab4dc3</citedby><cites>FETCH-LOGICAL-c5290-c953447efa0f54ca4c7b6f384382f688efbb7abe95cea7ea587b2e5f773ab4dc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/3695355$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/3695355$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,832,1417,27924,27925,45574,45575,58017,58021,58250,58254</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/14969494$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wouters, Luc</creatorcontrib><creatorcontrib>Göhlmann, Hinrich W.</creatorcontrib><creatorcontrib>Bijnens, Luc</creatorcontrib><creatorcontrib>Kass, Stefan U.</creatorcontrib><creatorcontrib>Molenberghs, Geert</creatorcontrib><creatorcontrib>Lewi, Paul J.</creatorcontrib><title>Graphical Exploration of Gene Expression Data: A Comparative Study of Three Multivariate Methods</title><title>Biometrics</title><addtitle>Biometrics</addtitle><description>This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. Proper weighting is important, since it enables less reliable data to be down-weighted and more reliable information to be emphasized.</description><subject>Bioinformatics</subject><subject>Biometry - methods</subject><subject>Biplot</subject><subject>Consultant's Forum</subject><subject>Correspondence factor analysis</subject><subject>Data mining</subject><subject>Data visualization</subject><subject>Datasets</subject><subject>Gene Expression</subject><subject>Gene expression data</subject><subject>Genes</subject><subject>Genetic mapping</subject><subject>Leukemia</subject><subject>Microarray data</subject><subject>Models, Genetic</subject><subject>Models, Statistical</subject><subject>Multivariate Analysis</subject><subject>Multivariate exploratory data analysis</subject><subject>Oligonucleotide Array Sequence Analysis - methods</subject><subject>Principal component analysis</subject><subject>Principal components analysis</subject><subject>Reproducibility of Results</subject><subject>Spectral map analysis</subject><subject>Spectroscopic analysis</subject><subject>Statistical variance</subject><subject>T lymphocytes</subject><subject>Term weighting</subject><issn>0006-341X</issn><issn>1541-0420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkM9v0zAUxy0EYmXsP0AoJ24pdmzHCRKHrYwyaWWH_YCbeXGf1ZS0zuwE2v8eh1Tddb7Y773P91n6EJIwOmXxfFxPKaV5ygX7Oc0o5bFknE53L8iEScFSKjL6kkyO0Al5E8I6lqWk2WtywkSZl6IUE_Jr7qFd1Qaa5HLXNs5DV7tt4mwyxy0OPY8hDK0v0MGn5DyZuU0LA_YHk9uuX-4H-G7lEZNF38Q2-Bq6WGC3csvwlryy0AQ8O9yn5P7r5d3sW3p9M7-anV-nRmYlTU0puRAKLVArhQFhVJVbXgheZDYvCrRVpaDCUhoEhSALVWUorVIcKrE0_JR8GPe23j32GDq9qYPBpoEtuj5oxWSRUUEjWIyg8S4Ej1a3vt6A32tG9WBXr_UgTg_i9GBX_7erdzH6_vBHX21w-RQ86IzA5xH4Wze4f_ZifXF1s4ivmH835tehc_6Y53m0I2Ucp-O4Dh3ujmPwv3WuuJL6x_e5vlXigV08cL3g_wApxaJZ</recordid><startdate>200312</startdate><enddate>200312</enddate><creator>Wouters, Luc</creator><creator>Göhlmann, Hinrich W.</creator><creator>Bijnens, Luc</creator><creator>Kass, Stefan U.</creator><creator>Molenberghs, Geert</creator><creator>Lewi, Paul J.</creator><general>Blackwell Publishing</general><general>International Biometric Society</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>200312</creationdate><title>Graphical Exploration of Gene Expression Data: A Comparative Study of Three Multivariate Methods</title><author>Wouters, Luc ; Göhlmann, Hinrich W. ; Bijnens, Luc ; Kass, Stefan U. ; Molenberghs, Geert ; Lewi, Paul J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5290-c953447efa0f54ca4c7b6f384382f688efbb7abe95cea7ea587b2e5f773ab4dc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Bioinformatics</topic><topic>Biometry - methods</topic><topic>Biplot</topic><topic>Consultant's Forum</topic><topic>Correspondence factor analysis</topic><topic>Data mining</topic><topic>Data visualization</topic><topic>Datasets</topic><topic>Gene Expression</topic><topic>Gene expression data</topic><topic>Genes</topic><topic>Genetic mapping</topic><topic>Leukemia</topic><topic>Microarray data</topic><topic>Models, Genetic</topic><topic>Models, Statistical</topic><topic>Multivariate Analysis</topic><topic>Multivariate exploratory data analysis</topic><topic>Oligonucleotide Array Sequence Analysis - methods</topic><topic>Principal component analysis</topic><topic>Principal components analysis</topic><topic>Reproducibility of Results</topic><topic>Spectral map analysis</topic><topic>Spectroscopic analysis</topic><topic>Statistical variance</topic><topic>T lymphocytes</topic><topic>Term weighting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wouters, Luc</creatorcontrib><creatorcontrib>Göhlmann, Hinrich W.</creatorcontrib><creatorcontrib>Bijnens, Luc</creatorcontrib><creatorcontrib>Kass, Stefan U.</creatorcontrib><creatorcontrib>Molenberghs, Geert</creatorcontrib><creatorcontrib>Lewi, Paul J.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Biometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wouters, Luc</au><au>Göhlmann, Hinrich W.</au><au>Bijnens, Luc</au><au>Kass, Stefan U.</au><au>Molenberghs, Geert</au><au>Lewi, Paul J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Graphical Exploration of Gene Expression Data: A Comparative Study of Three Multivariate Methods</atitle><jtitle>Biometrics</jtitle><addtitle>Biometrics</addtitle><date>2003-12</date><risdate>2003</risdate><volume>59</volume><issue>4</issue><spage>1131</spage><epage>1139</epage><pages>1131-1139</pages><issn>0006-341X</issn><eissn>1541-0420</eissn><abstract>This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. 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| source | MEDLINE; JSTOR Mathematics & Statistics; Access via Wiley Online Library; JSTOR Archive Collection A-Z Listing; Oxford University Press Journals All Titles (1996-Current) |
| subjects | Bioinformatics Biometry - methods Biplot Consultant's Forum Correspondence factor analysis Data mining Data visualization Datasets Gene Expression Gene expression data Genes Genetic mapping Leukemia Microarray data Models, Genetic Models, Statistical Multivariate Analysis Multivariate exploratory data analysis Oligonucleotide Array Sequence Analysis - methods Principal component analysis Principal components analysis Reproducibility of Results Spectral map analysis Spectroscopic analysis Statistical variance T lymphocytes Term weighting |
| title | Graphical Exploration of Gene Expression Data: A Comparative Study of Three Multivariate Methods |
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