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Statistical methods in the design an...

Yang, Yee Hwa.

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Statistical methods in the design and analysis of gene expression data from cDNA microarray experiments.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical methods in the design and analysis of gene expression data from cDNA microarray experiments./
Author:	Yang, Yee Hwa.
Description:	100 p.
Notes:	Source: Dissertation Abstracts International, Volume: 63-09, Section: B, page: 4239.
Contained By:	Dissertation Abstracts International63-09B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3063607
ISBN:	0493825495

Statistical methods in the design and analysis of gene expression data from cDNA microarray experiments.
Yang, Yee Hwa.

Statistical methods in the design and analysis of gene expression data from cDNA microarray experiments. - 100 p.

Source: Dissertation Abstracts International, Volume: 63-09, Section: B, page: 4239.

Thesis (Ph.D.)--University of California, Berkeley, 2002.

Microarray experiments performed in many areas of biological sciences generate large and complex multivariate datasets. This thesis addresses statistical design and analysis issues, which are essential to improve the efficiency and reliability of cDNA microarray experiments. Firstly, we discuss various considerations unique to the design of cDNA microarrays, and examine how different types of replication affect the design decisions. We show in theory and with experimental data that the widely used common reference approach is inherently more variable than direct comparisons using the same number of experiments. Secondly, we examine the impact and reliability of various normalization procedures, which are required to ensure that observed differences in intensity indeed reflect the differential gene expression and not artefactual bias inherent to the experiment. To this end, we develop various nonlinear normalization procedures based on robust local regression to account for intensity and spatial biases. Finally, we present a microarray experiment that aims to identify spatially differentially expressed genes within the mouse olfactory bulb. Using this experiment, we illustrate various stages involved in the design and analysis of gene expression data. A number of differentially expressed genes identified by our statistical analysis are verified by an independent biological procedure.

ISBN: 0493825495Subjects--Topical Terms:

517247
Statistics.

Statistical methods in the design and analysis of gene expression data from cDNA microarray experiments.
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100 1 $a Yang, Yee Hwa. $3 1940981
245 1 0 $a Statistical methods in the design and analysis of gene expression data from cDNA microarray experiments.
300 $a 100 p.
500 $a Source: Dissertation Abstracts International, Volume: 63-09, Section: B, page: 4239.
500 $a Chair: Terence P. Speed.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2002.
520 $a Microarray experiments performed in many areas of biological sciences generate large and complex multivariate datasets. This thesis addresses statistical design and analysis issues, which are essential to improve the efficiency and reliability of cDNA microarray experiments. Firstly, we discuss various considerations unique to the design of cDNA microarrays, and examine how different types of replication affect the design decisions. We show in theory and with experimental data that the widely used common reference approach is inherently more variable than direct comparisons using the same number of experiments. Secondly, we examine the impact and reliability of various normalization procedures, which are required to ensure that observed differences in intensity indeed reflect the differential gene expression and not artefactual bias inherent to the experiment. To this end, we develop various nonlinear normalization procedures based on robust local regression to account for intensity and spatial biases. Finally, we present a microarray experiment that aims to identify spatially differentially expressed genes within the mouse olfactory bulb. Using this experiment, we illustrate various stages involved in the design and analysis of gene expression data. A number of differentially expressed genes identified by our statistical analysis are verified by an independent biological procedure.
590 $a School code: 0028.
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 University of California, Berkeley. $3 687832
773 0 $t Dissertation Abstracts International $g 63-09B.
790 1 0 $a Speed, Terence P., $e advisor
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791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3063607