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On Bayesian modeling and design for ...

Ji, Yuan.

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On Bayesian modeling and design for microarray gene expression data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On Bayesian modeling and design for microarray gene expression data./
作者:	Ji, Yuan.
面頁冊數:	112 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3889.
Contained By:	Dissertation Abstracts International64-08B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3101396

On Bayesian modeling and design for microarray gene expression data.
Ji, Yuan.

On Bayesian modeling and design for microarray gene expression data. - 112 p.

Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3889.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2003.

Three problems related to microarray experiments are considered in this thesis. Two of them deal with the statistical modeling and inferences and the other is related to the design of experiments.Subjects--Topical Terms:

517247
Statistics.

On Bayesian modeling and design for microarray gene expression data.
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245 1 0 $a On Bayesian modeling and design for microarray gene expression data.
300 $a 112 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3889.
500 $a Supervisors: Kyungmann Kim; Kam-Wah Tsui.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2003.
520 $a Three problems related to microarray experiments are considered in this thesis. Two of them deal with the statistical modeling and inferences and the other is related to the design of experiments.
520 $a The first problem is about classification of experimental samples or tissues with gene expression profiles. Because the number of gene expression levels is much larger than the number of treatments to be classified, some traditional methods such as the Fisher discriminant analysis cannot be directly applied. We propose a two-stage empirical Bayes classification method for this problem. The first stage combines genes into clusters and the second stage constructs an empirical Bayes model incorporating the cluster information of genes. The performance of our method is evaluated by simulation studies. The method is implemented for two real data sets and misclassification errors are adjusted for the selection bias.
520 $a The second problem deals with identification of differentially expressed genes. We group genes into clusters again as a preliminary step to simplify the modeling process later. The differential expression level is modeled by a gene specific parameter and the posterior distribution for this parameter is obtained for each gene. Differential expression is declared based on the rank of posterior means. To assess the sensitivity and specificity of the set of genes declared to be differentially expressed, posterior selection probabilities are computed with a Monte Carlo procedure.
520 $a In the third problem, we address the pooling issue in the design of microarray experiments. Whether or not to pool mRNA samples from different subjects for subsequent analysis is an important design issue in microarray experiments. Motivated by Kendziorski <italic>et al</italic>. (2003), we extend their results for comparing pooled design and non-pooled design by relaxing several modeling assumptions. We further develop a general model that accounts for the variation in the pooling of mRNA samples. We employ a Monte Carlo method to compute the mean square error of the Bayesian estimate.
590 $a School code: 0262.
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