Title:
Assessing gene significance from cDNA microarray expression data via mixed models
Assessing gene significance from cDNA microarray expression data via mixed models
| dc.contributor.author | Wolfinger, Russell D. | en_US |
| dc.contributor.author | Gibson, Greg | en_US |
| dc.contributor.author | Wolfinger, Elizabeth D. | en_US |
| dc.contributor.author | Bennett, Lee | en_US |
| dc.contributor.author | Hamadeh, Hisham | en_US |
| dc.contributor.author | Ashari, Cynthia | en_US |
| dc.contributor.author | Paules, Richard S. | en_US |
| dc.contributor.corporatename | SAS Institute | en_US |
| dc.contributor.corporatename | North Carolina State University. Dept. of Genetics | en_US |
| dc.contributor.corporatename | Meredith College (Raleigh, N.C.). Dept. of Biology | en_US |
| dc.contributor.corporatename | National Institute of Environmental Health Sciences | en_US |
| dc.date.accessioned | 2011-10-21T20:21:21Z | |
| dc.date.available | 2011-10-21T20:21:21Z | |
| dc.date.issued | 2001 | |
| dc.description | This is a copy of an article published in the Journal of Computational Biology © 2001 Mary Ann Liebert, Inc.; Journal of Computational Biology is available online at: http://www.liebertonline.com. | en_US |
| dc.description.abstract | The determination of a list of differentially expressed genes is a basic objective in many cDNA microarray experiments. We present a statistical approach that allows direct control over the percentage of false positives in such a list and, under certain reasonable assumptions, improves on existing methods with respect to the percentage of false negatives. The method accommodates a wide variety of experimental designs and can simultaneously assess signi cant differences between multiple types of biological samples. Two interconnected mixed linear models are central to the method and provide a exible means to properly account for variability both across and within genes. The mixed model also provides a convenient framework for evaluating the statistical power of any particular experimental design and thus enables a researcher to a priori select an appropriate number of replicates. We also suggest some basic graphics for visualizing lists of signi cant genes. Analyses of published experiments studying human cancer and yeast cells illustrate the results. | en_US |
| dc.identifier.citation | Wolfinger, R.D., G. Gibson, Wolfinger E.D., Bennett, L., Hamadeh, H., Bushel, P., Afshari, C., and Paules, R.S. (2001) Assessing gene significance from cDNA microarray expression data via mixed models. J. Comput. Biol. 8: 625-637. | en |
| dc.identifier.issn | 1066-5277 | |
| dc.identifier.uri | http://hdl.handle.net/1853/41850 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Mary Ann Liebert, Inc. Publishers | |
| dc.subject | ANOVA | en_US |
| dc.subject | cDNA microarray | en_US |
| dc.subject | Gene expression | en_US |
| dc.subject | Mixed models | en_US |
| dc.subject | Statistical significance | en_US |
| dc.title | Assessing gene significance from cDNA microarray expression data via mixed models | en_US |
| dc.type | Text | |
| dc.type.genre | Article | |
| dspace.entity.type | Publication | |
| local.contributor.author | Gibson, Greg | |
| local.contributor.corporatename | College of Sciences | |
| local.contributor.corporatename | School of Biological Sciences | |
| relation.isAuthorOfPublication | 5606ef18-bd5a-4b7b-b3fc-05821bf66602 | |
| relation.isOrgUnitOfPublication | 85042be6-2d68-4e07-b384-e1f908fae48a | |
| relation.isOrgUnitOfPublication | c8b3bd08-9989-40d3-afe3-e0ad8d5c72b5 |
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