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Bayesian Identifcation of Differential Gene Expression Induced by Metals in Human Bronchial Epithelial Cells
被引:1
|作者:
House, Leanna L.
[1
]
Clyde, Merlise A.
[1
]
Huang, Yuh-Chin T.
[2
]
机构:
[1] Duke Univ, Inst Stat & Decis Sci, Durham, NC 27706 USA
[2] US EPA, Natl Hlth & Environm Effects Res Lab, Res Triangle Pk, NC 27711 USA
来源:
BAYESIAN ANALYSIS
|
2006年
/
1卷
/
01期
基金:
美国国家科学基金会;
关键词:
Bayesian;
latent variables;
MCMC;
differential expression;
hierarchical model;
microarray;
macroarray;
toxicology;
model selection;
D O I:
10.1214/06-BA103
中图分类号:
O1 [数学];
学科分类号:
0701 ;
070101 ;
摘要:
The study of genetics continues to advance dramatically with the development of microarray technology. Inlight of the advancements, interesting statistical challenges have arisen. Given that only one observation can be made from each gene on a single array, statisticians are faced with three issues: analysis with more genes than arrays, separating true differential expression from noise, and multiple hypothesis testing fo rregulation.Within this study, we model the expression of 1185 genes simultaneously in response to five chemical constituents of particulate matter; arsenic, iron, nickel, vanadium, and zinc.Taking advantage of a hierarchical Bayesian mixture model with latent variables, we compare multiple treatments to a control and estimate noise across arrays without assuming equal treatment means for housekeeping genes.To account for model uncertainty and hyperparameter specification, model averaging, MCMC, and Rao-Blackwell estimation are utilized
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页码:105 / 120
页数:16
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