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Statistical issues in the analysis o...

Xiao, Yuanyuan.

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Statistical issues in the analysis of the DNA microarray data: Normalization and differential expression.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical issues in the analysis of the DNA microarray data: Normalization and differential expression./
Author:	Xiao, Yuanyuan.
Description:	104 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-06, Section: B, page: 2729.
Contained By:	Dissertation Abstracts International65-06B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3136083
ISBN:	0496832829

Statistical issues in the analysis of the DNA microarray data: Normalization and differential expression.
Xiao, Yuanyuan.

Statistical issues in the analysis of the DNA microarray data: Normalization and differential expression. - 104 p.

Source: Dissertation Abstracts International, Volume: 65-06, Section: B, page: 2729.

Thesis (Ph.D.)--University of California, San Francisco, 2004.

DNA microarray technology, being one of the most powerful tools in functional genomics, profiles gene expression of any organism on a genomic scale and produces large and complex data. Analysis of such data has profound influence on the accurate extraction and deciphering of the underlying biological processes. This thesis addresses pertinent statistical issues from two aspects, the normalization of cDNA microarrays and the assessment of differential expression. The first half of this thesis focuses on normalization, which is the process that adjusts variations inherent in microarray technology rather than from biological differences. Although a number of normalization models have been proposed, it has not been well researched on how to select the most appropriate model with respect to the observed data. To this end, we propose a new within-slide normalization method, which integrates various normalization models of different complexities in the same framework and assesses their effectiveness via a quantitative criterion. Such a process is applied sequentially for the adjustments of the intensity and spatial biases. The latter half of this thesis examines the important issue of identifying differentially expressed genes amongst the many being investigated. We show that by using a distance synthesizing scheme that combines varying statistics, our proposed algorithm enjoys robust properties that are lacking in individual statistics. Both algorithms in normalization and differential expression are tested using several microarray datasets that feature varying properties. Emphasis of the analysis of real microarray datasets is given to a novel splicing array experiment, for which we develop and illustrate appropriate methodologies in normalization and differential expression in the context of a complex experimental design.

ISBN: 0496832829Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Statistical issues in the analysis of the DNA microarray data: Normalization and differential expression.
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245 1 0 $a Statistical issues in the analysis of the DNA microarray data: Normalization and differential expression.
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500 $a Source: Dissertation Abstracts International, Volume: 65-06, Section: B, page: 2729.
500 $a Chair: C. Anthony Hunt.
502 $a Thesis (Ph.D.)--University of California, San Francisco, 2004.
520 $a DNA microarray technology, being one of the most powerful tools in functional genomics, profiles gene expression of any organism on a genomic scale and produces large and complex data. Analysis of such data has profound influence on the accurate extraction and deciphering of the underlying biological processes. This thesis addresses pertinent statistical issues from two aspects, the normalization of cDNA microarrays and the assessment of differential expression. The first half of this thesis focuses on normalization, which is the process that adjusts variations inherent in microarray technology rather than from biological differences. Although a number of normalization models have been proposed, it has not been well researched on how to select the most appropriate model with respect to the observed data. To this end, we propose a new within-slide normalization method, which integrates various normalization models of different complexities in the same framework and assesses their effectiveness via a quantitative criterion. Such a process is applied sequentially for the adjustments of the intensity and spatial biases. The latter half of this thesis examines the important issue of identifying differentially expressed genes amongst the many being investigated. We show that by using a distance synthesizing scheme that combines varying statistics, our proposed algorithm enjoys robust properties that are lacking in individual statistics. Both algorithms in normalization and differential expression are tested using several microarray datasets that feature varying properties. Emphasis of the analysis of real microarray datasets is given to a novel splicing array experiment, for which we develop and illustrate appropriate methodologies in normalization and differential expression in the context of a complex experimental design.
590 $a School code: 0034.
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