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dc.contributor.authorLai, Yinglei
dc.contributor.authorEckenrode, Sarah E
dc.contributor.authorShe, Jin-Xiong
dc.date.accessioned2010-09-24T20:59:22Z
dc.date.available2010-09-24T20:59:22Z
dc.date.issued2009-02-11en_US
dc.identifier.citationBMC Bioinformatics. 2009 Jan 30; 10(Suppl 1):S23en_US
dc.identifier.issn1471-2105en_US
dc.identifier.pmid19208123en_US
dc.identifier.doi10.1186/1471-2105-10-S1-S23en_US
dc.identifier.urihttp://hdl.handle.net/10675.2/25
dc.description.abstractBACKGROUND: Different microarray data sets can be collected for studying the same or similar diseases. We expect to achieve a more efficient analysis of differential expression if an efficient statistical method can be developed for integrating different microarray data sets. Although many statistical methods have been proposed for data integration, the genome-wide concordance of different data sets has not been well considered in the analysis. RESULTS: Before considering data integration, it is necessary to evaluate the genome-wide concordance so that misleading results can be avoided. Based on the test results, different subsequent actions are suggested. The evaluation of genome-wide concordance and the data integration can be achieved based on the normal distribution based mixture models. CONCLUSION: The results from our simulation study suggest that misleading results can be generated if the genome-wide concordance issue is not appropriately considered. Our method provides a rigorous parametric solution. The results also show that our method is robust to certain model misspecification and is practically useful for the integrative analysis of differential expression.
dc.rightsThe PMC Open Access Subset is a relatively small part of the total collection of articles in PMC. Articles in the PMC Open Access Subset are still protected by copyright, but are made available under a Creative Commons or similar license that generally allows more liberal redistribution and reuse than a traditional copyrighted work. Please refer to the license statement in each article for specific terms of use. The license terms are not identical for all articles in this subset.en_US
dc.subject.meshComputational Biology / methodsen_US
dc.subject.meshGene Expressionen_US
dc.subject.meshGene Expression Profiling / methodsen_US
dc.subject.meshGenomeen_US
dc.subject.meshModels, Statisticalen_US
dc.subject.meshOligonucleotide Array Sequence Analysisen_US
dc.titleA statistical framework for integrating two microarray data sets in differential expression analysis.en_US
dc.typeJournal Articleen_US
dc.typeResearch Support, N.I.H., Extramuralen_US
dc.identifier.pmcidPMC2648727en_US
dc.contributor.corporatenameCenter for Biotechnology and Genomic Medicineen_US
refterms.dateFOA2019-04-09T16:42:59Z
html.description.abstractBACKGROUND: Different microarray data sets can be collected for studying the same or similar diseases. We expect to achieve a more efficient analysis of differential expression if an efficient statistical method can be developed for integrating different microarray data sets. Although many statistical methods have been proposed for data integration, the genome-wide concordance of different data sets has not been well considered in the analysis. RESULTS: Before considering data integration, it is necessary to evaluate the genome-wide concordance so that misleading results can be avoided. Based on the test results, different subsequent actions are suggested. The evaluation of genome-wide concordance and the data integration can be achieved based on the normal distribution based mixture models. CONCLUSION: The results from our simulation study suggest that misleading results can be generated if the genome-wide concordance issue is not appropriately considered. Our method provides a rigorous parametric solution. The results also show that our method is robust to certain model misspecification and is practically useful for the integrative analysis of differential expression.


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