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Optimal design of single factorcDNA ...

Yang, Xiao.

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Optimal design of single factorcDNA microarray experiments and mixed models for gene expression data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimal design of single factorcDNA microarray experiments and mixed models for gene expression data./
Author:	Yang, Xiao.
Description:	98 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3529.
Contained By:	Dissertation Abstracts International65-07B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3141112
ISBN:	0496882466

Optimal design of single factorcDNA microarray experiments and mixed models for gene expression data.
Yang, Xiao.

Optimal design of single factorcDNA microarray experiments and mixed models for gene expression data. - 98 p.

Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3529.

Thesis (Ph.D.)--Virginia Polytechnic Institute and State University, 2003.

Microarray experiments are used to perform gene expression profiling on a large scale. E- and A-optimality of mixed design was established for experiments with up to 26 different varieties and with the restriction that the number of arrays available is equal to the number of varieties. Because the IBD setting only allows for a single blocking factor (arrays), the search for optimal designs was extended to the Row-Column Design (RCD) setting with blocking factors dye (row) and array (column). Relative efficiencies of these designs were further compared under analysis of variance (ANOVA) models. We also compared the performance of classification analysis for the interwoven loop and the replicated reference designs under four scenarios. The replicated reference design was favored when gene-specific sample variation was large, but the interwoven loop design was preferred for large variation among biological replicates.

ISBN: 0496882466Subjects--Topical Terms:

517247
Statistics.

Optimal design of single factorcDNA microarray experiments and mixed models for gene expression data.
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100 1 $a Yang, Xiao. $3 1927968
245 1 0 $a Optimal design of single factorcDNA microarray experiments and mixed models for gene expression data.
300 $a 98 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3529.
500 $a Chairs: Ina Hoeschele; Keying Ye.
502 $a Thesis (Ph.D.)--Virginia Polytechnic Institute and State University, 2003.
520 $a Microarray experiments are used to perform gene expression profiling on a large scale. E- and A-optimality of mixed design was established for experiments with up to 26 different varieties and with the restriction that the number of arrays available is equal to the number of varieties. Because the IBD setting only allows for a single blocking factor (arrays), the search for optimal designs was extended to the Row-Column Design (RCD) setting with blocking factors dye (row) and array (column). Relative efficiencies of these designs were further compared under analysis of variance (ANOVA) models. We also compared the performance of classification analysis for the interwoven loop and the replicated reference designs under four scenarios. The replicated reference design was favored when gene-specific sample variation was large, but the interwoven loop design was preferred for large variation among biological replicates.
520 $a We applied mixed model methodology to detection and estimation of gene differential expression. For identification of differential gene expression, we favor contrasts which include both variety main effects and variety by gene interactions. In terms of t-statistics for these contrasts, we examined the equivalence between the one- and two-step analyses under both fixed and mixed effects models. We analytically established conditions for equivalence under fixed and mixed models. We investigated the difference of approximation with the two-step analysis in situations where equivalence does not hold. The significant difference between the one- and two-step mixed effects model was further illustrated through Monte Carlo simulation and three case studies. We implemented the one-step analysis for mixed models with the ASREML software.
590 $a School code: 0247.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Molecular. $3 1017719
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710 2 0 $a Virginia Polytechnic Institute and State University. $3 1017496
773 0 $t Dissertation Abstracts International $g 65-07B.
790 1 0 $a Hoeschele, Ina, $e advisor
790 1 0 $a Ye, Keying, $e advisor
790 $a 0247
791 $a Ph.D.
792 $a 2003
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