A semiparametric Bayesian approach for estimating the gene expression distribution.
A semiparametric Bayesian approach for estimating the gene expression distribution.
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DOI：
10.1080/10543400903572746
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发表时间：
2010-03
影响因子：
1.1
通讯作者：
Ibrahim JG
中科院分区：
文献类型：
--
作者：
Zou F;Huang H;Ibrahim JG
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 mis-perception 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. The usefulness of the method is illustrated by simulations and a real murine lung study.
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影响因子：
1.9
作者：
Tang, Yongqiang;Ghosal, Subhashis;Roy, Anindya
通讯作者：
Roy, Anindya
影响因子：
2.7
作者：
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通讯作者：
MacEachern, SN
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
CHRISTENSEN, R
影响因子：
2.4
作者：
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Neal, RM