Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle
In functional genomic laboratories, it is common to use the same microarray slide across studies, each investigating a unique biological question, and each analyzed separately due to computational limitations and/or because there is no hybridization of samples from different studies on one slide. Ho...
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Veröffentlicht in: | Journal of animal science 2004-12, Vol.82 (12), p.3430-3439 |
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container_title | Journal of animal science |
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creator | Reverter, A Wang, Y.H Byrne, K.A Tan, S.H Harper, G.S Lehnert, S.A |
description | In functional genomic laboratories, it is common to use the same microarray slide across studies, each investigating a unique biological question, and each analyzed separately due to computational limitations and/or because there is no hybridization of samples from different studies on one slide. However, the question of analyzing data from multiple studies is a major current issue in microarray data analysis because there are gains to be made in the accuracy of estimated effects by exploiting a covariance structure between gene expression data across studies. We propose an approach for combining multiple studies using multivariate mixed models, with the assumption of a nonzero correlation among genes across experiments, while imposing a null residual covariance. We applied this method to jointly analyze three experiments in genetics of cattle with a total of 54 arrays, each with 19,200 spots and 7,638 elements. The resulting seven-variate model contains 752,476 equations and 56 covariances. To identify differentially expressed genes, we applied model-based clustering to a linear combination of the random gene x variety interaction effect. We enhanced the biological interpretation of the results by applying an iterative algorithm to identify the gene ontology classes that significantly changed in each experiment. We found 118 elements with coordinate expression that clustered into distinct biological functions such as adipogenesis and protein turnover. These results contribute to our understanding of the mechanistic processes involved in adipogenesis and nutrient partitioning. |
doi_str_mv | 10.2527/2004.82123430x |
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However, the question of analyzing data from multiple studies is a major current issue in microarray data analysis because there are gains to be made in the accuracy of estimated effects by exploiting a covariance structure between gene expression data across studies. We propose an approach for combining multiple studies using multivariate mixed models, with the assumption of a nonzero correlation among genes across experiments, while imposing a null residual covariance. We applied this method to jointly analyze three experiments in genetics of cattle with a total of 54 arrays, each with 19,200 spots and 7,638 elements. The resulting seven-variate model contains 752,476 equations and 56 covariances. To identify differentially expressed genes, we applied model-based clustering to a linear combination of the random gene x variety interaction effect. We enhanced the biological interpretation of the results by applying an iterative algorithm to identify the gene ontology classes that significantly changed in each experiment. We found 118 elements with coordinate expression that clustered into distinct biological functions such as adipogenesis and protein turnover. These results contribute to our understanding of the mechanistic processes involved in adipogenesis and nutrient partitioning.</description><identifier>ISSN: 0021-8812</identifier><identifier>EISSN: 1525-3163</identifier><identifier>DOI: 10.2527/2004.82123430x</identifier><identifier>PMID: 15537761</identifier><language>eng</language><publisher>Savoy, IL: Am Soc Animal Sci</publisher><subject>algorithms ; Animal productions ; Animals ; beef cattle ; Biological and medical sciences ; Breeding ; Cattle - genetics ; complementary DNA ; DNA, Complementary ; Food industries ; Fundamental and applied biological sciences. Psychology ; gene expression ; Gene Expression Profiling - methods ; Gene Expression Profiling - veterinary ; Gene Expression Regulation ; genetic improvement ; genetic variance ; lipogenesis ; Meat and meat product industries ; microarray technology ; Multivariate Analysis ; Oligonucleotide Array Sequence Analysis - methods ; Oligonucleotide Array Sequence Analysis - veterinary ; protein metabolism ; statistical models ; Terrestrial animal productions ; Vertebrates</subject><ispartof>Journal of animal science, 2004-12, Vol.82 (12), p.3430-3439</ispartof><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16302484$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15537761$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Reverter, A</creatorcontrib><creatorcontrib>Wang, Y.H</creatorcontrib><creatorcontrib>Byrne, K.A</creatorcontrib><creatorcontrib>Tan, S.H</creatorcontrib><creatorcontrib>Harper, G.S</creatorcontrib><creatorcontrib>Lehnert, S.A</creatorcontrib><title>Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle</title><title>Journal of animal science</title><addtitle>J Anim Sci</addtitle><description>In functional genomic laboratories, it is common to use the same microarray slide across studies, each investigating a unique biological question, and each analyzed separately due to computational limitations and/or because there is no hybridization of samples from different studies on one slide. However, the question of analyzing data from multiple studies is a major current issue in microarray data analysis because there are gains to be made in the accuracy of estimated effects by exploiting a covariance structure between gene expression data across studies. We propose an approach for combining multiple studies using multivariate mixed models, with the assumption of a nonzero correlation among genes across experiments, while imposing a null residual covariance. We applied this method to jointly analyze three experiments in genetics of cattle with a total of 54 arrays, each with 19,200 spots and 7,638 elements. The resulting seven-variate model contains 752,476 equations and 56 covariances. To identify differentially expressed genes, we applied model-based clustering to a linear combination of the random gene x variety interaction effect. We enhanced the biological interpretation of the results by applying an iterative algorithm to identify the gene ontology classes that significantly changed in each experiment. We found 118 elements with coordinate expression that clustered into distinct biological functions such as adipogenesis and protein turnover. These results contribute to our understanding of the mechanistic processes involved in adipogenesis and nutrient partitioning.</description><subject>algorithms</subject><subject>Animal productions</subject><subject>Animals</subject><subject>beef cattle</subject><subject>Biological and medical sciences</subject><subject>Breeding</subject><subject>Cattle - genetics</subject><subject>complementary DNA</subject><subject>DNA, Complementary</subject><subject>Food industries</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>gene expression</subject><subject>Gene Expression Profiling - methods</subject><subject>Gene Expression Profiling - veterinary</subject><subject>Gene Expression Regulation</subject><subject>genetic improvement</subject><subject>genetic variance</subject><subject>lipogenesis</subject><subject>Meat and meat product industries</subject><subject>microarray technology</subject><subject>Multivariate Analysis</subject><subject>Oligonucleotide Array Sequence Analysis - methods</subject><subject>Oligonucleotide Array Sequence Analysis - veterinary</subject><subject>protein metabolism</subject><subject>statistical models</subject><subject>Terrestrial animal productions</subject><subject>Vertebrates</subject><issn>0021-8812</issn><issn>1525-3163</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqF0btvFDEQB2ALgcgl0FKCG6Da4Nd6vWUUCA9FUEDq1ax3fHHkfWB7j1zD346jO0RJNbL06TcPE_KCs3NRi-adYEydG8GFVJLdPyIbXou6klzLx2TDmOCVMVyckNOU7hjjom7rp-SE17VsGs035PeX2U-ZwgRhn3yis6PjGrJfAlL7_usFHb2NM8QIe5ryOnhMdOfhgHYQPWQs5h4HOs4DhkRhWYIvzzzTLU6YvaV-XOK8wxFLp9KgR3TUQs4Bn5EnDkLC58d6Rm6uPvy4_FRdf_v4-fLiunJSqFwNZVczOGxUKyw3TA8Ke-WMQUClVV8z1FaaVkhbVm5617tW6sFqJtqeqVaekTeH3DLIzxVT7kafLIYAE85r6nTDtNRG_hfyRretaR7gyyNc-xGHbol-hLjv_p62gNdHAMlCcBEm69M_pyUTyqji3h7crd_e_vIRuzRCCCWWd3eQjOi46B5-t8hXB-lg7mAbS9rNd8G4ZKzVSvNW_gFdwZ_C</recordid><startdate>20041201</startdate><enddate>20041201</enddate><creator>Reverter, A</creator><creator>Wang, Y.H</creator><creator>Byrne, K.A</creator><creator>Tan, S.H</creator><creator>Harper, G.S</creator><creator>Lehnert, S.A</creator><general>Am Soc Animal Sci</general><general>American Society of Animal Science</general><scope>FBQ</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20041201</creationdate><title>Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle</title><author>Reverter, A ; Wang, Y.H ; Byrne, K.A ; Tan, S.H ; Harper, G.S ; Lehnert, S.A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-f324t-d2528dfe7492c1806d4eb4f88eae464b50e6c38923c0217bfbf936dc6029b0493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>algorithms</topic><topic>Animal productions</topic><topic>Animals</topic><topic>beef cattle</topic><topic>Biological and medical sciences</topic><topic>Breeding</topic><topic>Cattle - genetics</topic><topic>complementary DNA</topic><topic>DNA, Complementary</topic><topic>Food industries</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>gene expression</topic><topic>Gene Expression Profiling - methods</topic><topic>Gene Expression Profiling - veterinary</topic><topic>Gene Expression Regulation</topic><topic>genetic improvement</topic><topic>genetic variance</topic><topic>lipogenesis</topic><topic>Meat and meat product industries</topic><topic>microarray technology</topic><topic>Multivariate Analysis</topic><topic>Oligonucleotide Array Sequence Analysis - methods</topic><topic>Oligonucleotide Array Sequence Analysis - veterinary</topic><topic>protein metabolism</topic><topic>statistical models</topic><topic>Terrestrial animal productions</topic><topic>Vertebrates</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Reverter, A</creatorcontrib><creatorcontrib>Wang, Y.H</creatorcontrib><creatorcontrib>Byrne, K.A</creatorcontrib><creatorcontrib>Tan, S.H</creatorcontrib><creatorcontrib>Harper, G.S</creatorcontrib><creatorcontrib>Lehnert, S.A</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of animal science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Reverter, A</au><au>Wang, Y.H</au><au>Byrne, K.A</au><au>Tan, S.H</au><au>Harper, G.S</au><au>Lehnert, S.A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle</atitle><jtitle>Journal of animal science</jtitle><addtitle>J Anim Sci</addtitle><date>2004-12-01</date><risdate>2004</risdate><volume>82</volume><issue>12</issue><spage>3430</spage><epage>3439</epage><pages>3430-3439</pages><issn>0021-8812</issn><eissn>1525-3163</eissn><abstract>In functional genomic laboratories, it is common to use the same microarray slide across studies, each investigating a unique biological question, and each analyzed separately due to computational limitations and/or because there is no hybridization of samples from different studies on one slide. However, the question of analyzing data from multiple studies is a major current issue in microarray data analysis because there are gains to be made in the accuracy of estimated effects by exploiting a covariance structure between gene expression data across studies. We propose an approach for combining multiple studies using multivariate mixed models, with the assumption of a nonzero correlation among genes across experiments, while imposing a null residual covariance. We applied this method to jointly analyze three experiments in genetics of cattle with a total of 54 arrays, each with 19,200 spots and 7,638 elements. The resulting seven-variate model contains 752,476 equations and 56 covariances. To identify differentially expressed genes, we applied model-based clustering to a linear combination of the random gene x variety interaction effect. We enhanced the biological interpretation of the results by applying an iterative algorithm to identify the gene ontology classes that significantly changed in each experiment. We found 118 elements with coordinate expression that clustered into distinct biological functions such as adipogenesis and protein turnover. These results contribute to our understanding of the mechanistic processes involved in adipogenesis and nutrient partitioning.</abstract><cop>Savoy, IL</cop><pub>Am Soc Animal Sci</pub><pmid>15537761</pmid><doi>10.2527/2004.82123430x</doi><tpages>10</tpages></addata></record> |
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subjects | algorithms Animal productions Animals beef cattle Biological and medical sciences Breeding Cattle - genetics complementary DNA DNA, Complementary Food industries Fundamental and applied biological sciences. Psychology gene expression Gene Expression Profiling - methods Gene Expression Profiling - veterinary Gene Expression Regulation genetic improvement genetic variance lipogenesis Meat and meat product industries microarray technology Multivariate Analysis Oligonucleotide Array Sequence Analysis - methods Oligonucleotide Array Sequence Analysis - veterinary protein metabolism statistical models Terrestrial animal productions Vertebrates |
title | Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle |
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