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dc.contributor.authorKamb, Alexander
dc.contributor.authorRamaswami, Mani
dc.date.accessioned2016-05-20T09:04:38Z
dc.date.available2016-05-20T09:04:38Z
dc.date.issued2001en
dc.identifier.citationBMC Biotechnology 2001, 1:8 http://www.biomedcentral.com/1472-6750/1/8en
dc.identifier.doi10.1186/1472-6750-1-8en
dc.identifier.urihttp://hdl.handle.net/10150/610339
dc.description.abstractBACKGROUND:Microarray experiments offer a potent solution to the problem of making and comparing large numbers of gene expression measurements either in different cell types or in the same cell type under different conditions. Inferences about the biological relevance of observed changes in expression depend on the statistical significance of the changes. In lieu of many replicates with which to determine accurate intensity means and variances, reliable estimates of statistical significance remain problematic. Without such estimates, overly conservative choices for significance must be enforced.RESULTS:A simple statistical method for estimating variances from microarray control data which does not require multiple replicates is presented. Comparison of datasets from two commercial entities using this difference-averaging method demonstrates that the standard deviation of the signal scales at a level intermediate between the signal intensity and its square root. Application of the method to a dataset related to the beta-catenin pathway yields a larger number of biologically reasonable genes whose expression is altered than the ratio method.CONCLUSIONS:The difference-averaging method enables determination of variances as a function of signal intensities by averaging over the entire dataset. The method also provides a platform-independent view of important statistical properties of microarray data.
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urlhttp://www.biomedcentral.com/1472-6750/1/8en
dc.rights© 2001 Kamb and Ramaswami; licensee BioMed Central Ltd. Verbatim copying and redistribution of this article are permitted in any medium for any non-commercial purpose, provided this notice is preserved along with the article's original URL.en
dc.titleA simple method for statistical analysis of intensity differences in microarray-derived gene expression dataen
dc.typeArticleen
dc.identifier.eissn1472-6750en
dc.contributor.departmentArcaris, Inc. (Currently Deltagen Proteomics, Inc.) Salt Lake City, UT USAen
dc.contributor.departmentDept of Molecular and Cell Biology and ARL Division of Neurobiology University of Arizona Tucson, AZ USAen
dc.identifier.journalBMC Biotechnologyen
dc.description.collectioninformationThis item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-09-11T11:01:28Z
html.description.abstractBACKGROUND:Microarray experiments offer a potent solution to the problem of making and comparing large numbers of gene expression measurements either in different cell types or in the same cell type under different conditions. Inferences about the biological relevance of observed changes in expression depend on the statistical significance of the changes. In lieu of many replicates with which to determine accurate intensity means and variances, reliable estimates of statistical significance remain problematic. Without such estimates, overly conservative choices for significance must be enforced.RESULTS:A simple statistical method for estimating variances from microarray control data which does not require multiple replicates is presented. Comparison of datasets from two commercial entities using this difference-averaging method demonstrates that the standard deviation of the signal scales at a level intermediate between the signal intensity and its square root. Application of the method to a dataset related to the beta-catenin pathway yields a larger number of biologically reasonable genes whose expression is altered than the ratio method.CONCLUSIONS:The difference-averaging method enables determination of variances as a function of signal intensities by averaging over the entire dataset. The method also provides a platform-independent view of important statistical properties of microarray data.


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