Scholars' Mine
Missouri S&T
Research Repository
Curtis Laws Wilson Library
400 W. 14th Street
Rolla, MO 65409-0060
scholarsmine@mst.edu
| Title: | Empirical Bayes estimation of gene-specific effects in micro-array research |
| Author (s): | Edwards, Jode W. Page, Grier P. Gadbury, Gary L. Heo, Moonseong Kayo, Tsuyoshi Weindruch, Richard Allison, David B. |
| Department/Lab Affiliations: | Mathematics & Statistics |
| Keywords: | Empirical bayes Estimation Micro-array Shrinkage |
| Issue Date: | 2005 |
| Publisher: | Springer Berlin / Heidelberg |
| Citation: | Edwards, Jode W., Page, Grier P., Gadbury, Gary, Heo, Moonseong, Kayo, Tsuyoshi, Weindruch, Richard, and Allison, David B. (2005). Empirical Bayes Estimation of Gene-Specific Effects In Micro-array Research. Functional & Integrative Genomics, 5, 32 – 39. |
| Abstract: | Micro-array technology allows investigators the opportunity to measure expression levels of thousands of genes simultaneously. However, investigators are also faced with the challenge of simultaneous estimation of gene expression differences for thousands of genes with very small sample sizes. Traditional estimators of differences between treatment means (ordinary least squares estimators or OLS) are not the best estimators if interest is in estimation of gene expression differences for an ensemble of genes. In the case that gene expression differences are regarded as exchangeable samples from a common population, estimators are available that result in much smaller average mean-square error across the population of gene expression difference estimates. We have simulated the application of such an estimator, namely an empirical Bayes (EB) estimator of random effects in a hierarchical linear model (normal-normal). Simulation results revealed mean-square error as low as 0.05 times the mean-square error of OLS estimators (i.e., the difference between treatment means). We applied the analysis to an example dataset as a demonstration of the shrinkage of EB estimators and of the reduction in mean-square error, i.e., increase in precision, associated with EB estimators in this analysis. The method described here is available in software that is available at http://www.soph.uab.edu/ssg.asp?id=1087. |
| Type: | Article - Journal text |
| In Title: | Functional & Integrative Genomics |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: |
| Publisher URL: | |
| Link to this page: |
| title | Empirical Bayes estimation of gene-specific effects in micro-array research |
| contributor.author | Edwards, Jode W. |
| contributor.author | Page, Grier P. |
| contributor.author | Gadbury, Gary L. |
| contributor.author | Heo, Moonseong |
| contributor.author | Kayo, Tsuyoshi |
| contributor.author | Weindruch, Richard |
| contributor.author | Allison, David B. |
| contributor.deptlab | Mathematics & Statistics |
| contributor.sponsor | Frederick Gardner Cottrell Foundation |
| contributor.sponsor | National Institute of Health |
| contributor.sponsor | National Science Foundation |
| contributor.sponsor | University of Alkabama Health Services Foundation |
| subject | Empirical bayes |
| subject | Estimation |
| subject | Micro-array |
| subject | Shrinkage |
| date.issued | 2005 |
| publisher | Springer Berlin / Heidelberg |
| identifier.citation | Edwards, Jode W., Page, Grier P., Gadbury, Gary, Heo, Moonseong, Kayo, Tsuyoshi, Weindruch, Richard, and Allison, David B. (2005). Empirical Bayes Estimation of Gene-Specific Effects In Micro-array Research. Functional & Integrative Genomics, 5, 32 – 39. |
| identifier.pub.URI | |
| description.abstract | Micro-array technology allows investigators the opportunity to measure expression levels of thousands of genes simultaneously. However, investigators are also faced with the challenge of simultaneous estimation of gene expression differences for thousands of genes with very small sample sizes. Traditional estimators of differences between treatment means (ordinary least squares estimators or OLS) are not the best estimators if interest is in estimation of gene expression differences for an ensemble of genes. In the case that gene expression differences are regarded as exchangeable samples from a common population, estimators are available that result in much smaller average mean-square error across the population of gene expression difference estimates. We have simulated the application of such an estimator, namely an empirical Bayes (EB) estimator of random effects in a hierarchical linear model (normal-normal). Simulation results revealed mean-square error as low as 0.05 times the mean-square error of OLS estimators (i.e., the difference between treatment means). We applied the analysis to an example dataset as a demonstration of the shrinkage of EB estimators and of the reduction in mean-square error, i.e., increase in precision, associated with EB estimators in this analysis. The method described here is available in software that is available at http://www.soph.uab.edu/ssg.asp?id=1087. |
| type | Article - Journal |
| type.DCMIType | text |
| type.status | Postprint |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| rights.URI | |
| relation.isPartOf | Functional & Integrative Genomics |
| date.accessioned | 2007-04-11T17:00:48Z |
| date.available | 2008-05-09T19:15:29Z |
| identifier.persist.URI |