| pubmed-article:20309758 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:20309758 | lifeskim:mentions | umls-concept:C0017262 | lld:lifeskim |
| pubmed-article:20309758 | lifeskim:mentions | umls-concept:C1704711 | lld:lifeskim |
| pubmed-article:20309758 | lifeskim:mentions | umls-concept:C0449445 | lld:lifeskim |
| pubmed-article:20309758 | pubmed:issue | 2 | lld:pubmed |
| pubmed-article:20309758 | pubmed:dateCreated | 2010-3-23 | lld:pubmed |
| pubmed-article:20309758 | pubmed:abstractText | Gene expression microarrays are powerful tools for global comparison and estimation of gene expression. Many microarray studies have demonstrated biologically plausible results with only a few arrays, leading to a misperception that a handful of hybridized arrays can always find something meaningful. From a statistical point of view, it is important to prospectively estimate required sample sizes prior to undertaking a microarray experiment. However, all sample size calculations need to directly or indirectly estimate the unknown distribution of the effect sizes of gene expression intensities. A parametric mixture model has been developed for relating the sample size directly to the false discovery rate (FDR), the most popular multiple-comparison control criteria. In this paper, we extend the parametric mixture model and propose a robust semiparametric Dirichlet process mixture model, where the parametric distribution of gene expressions is no longer specified. This analysis is performed in a Bayesian inference framework using Markov-chain Monte Carlo steps. The usefulness of the method is illustrated by simulations and a real murine lung study. | lld:pubmed |
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| pubmed-article:20309758 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20309758 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20309758 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
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| pubmed-article:20309758 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20309758 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20309758 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20309758 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20309758 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20309758 | pubmed:language | eng | lld:pubmed |
| pubmed-article:20309758 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20309758 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:20309758 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:20309758 | pubmed:month | Mar | lld:pubmed |
| pubmed-article:20309758 | pubmed:issn | 1520-5711 | lld:pubmed |
| pubmed-article:20309758 | pubmed:author | pubmed-author:IbrahimJoseph... | lld:pubmed |
| pubmed-article:20309758 | pubmed:author | pubmed-author:ZouFeiF | lld:pubmed |
| pubmed-article:20309758 | pubmed:author | pubmed-author:HuangHanwenH | lld:pubmed |
| pubmed-article:20309758 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:20309758 | pubmed:volume | 20 | lld:pubmed |
| pubmed-article:20309758 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:20309758 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:20309758 | pubmed:pagination | 267-80 | lld:pubmed |
| pubmed-article:20309758 | pubmed:dateRevised | 2011-10-28 | lld:pubmed |
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| pubmed-article:20309758 | pubmed:meshHeading | pubmed-meshheading:20309758... | lld:pubmed |
| pubmed-article:20309758 | pubmed:year | 2010 | lld:pubmed |
| pubmed-article:20309758 | pubmed:articleTitle | A semiparametric Bayesian approach for estimating the gene expression distribution. | lld:pubmed |
| pubmed-article:20309758 | pubmed:affiliation | Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. fzou@bios.unc.edu | lld:pubmed |
| pubmed-article:20309758 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:20309758 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
