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Statistical analyses of time-course ...

Eckel, Jeanette Elaine.

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Statistical analyses of time-course and dose-response microarray experiments.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical analyses of time-course and dose-response microarray experiments./
Author:	Eckel, Jeanette Elaine.
Description:	190 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3609.
Contained By:	Dissertation Abstracts International64-08B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3101578

Statistical analyses of time-course and dose-response microarray experiments.
Eckel, Jeanette Elaine.

Statistical analyses of time-course and dose-response microarray experiments. - 190 p.

Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3609.

Thesis (Ph.D.)--Virginia Commonwealth University, 2003.

With the use of microarrays, the expression of tens of thousands of genes can be examined simultaneously to study the effects following exposure to a single chemical or following exposure to a mixture of chemicals. Measuring gene expression over a range of dose-concentrations (or similarly, over time) can expose similarities across genes and thus provide relationships in gene behavior, aid in determining gene function based on gene expression profiles, and reveal relationships between chemical treatments. We propose an extension to a recently developed gene-screening tool to reduce the dimensionality of a dose-response (or time-course) microarray dataset from tens of thousands of genes down to a subset of the most differentially expressed genes, which takes into account the continuous effect of dose (or time). To explore relationships among the subset of differentially expressed genes, we propose a multivariate model that allows for inter-gene as well as intra-gene correlated measurements. Rao's score test, a goodness-of-fit test for covariance matrices, is developed to test the goodness-of-fit of a parsimonious covariance (correlation) structure, which allows the number of genes in the corresponding covariance matrix to be larger than the number of independent tissue samples. Although, the development of Rao's score test for covariance matrices was motivated by microarray data, it is applicable to non-microarray data as well (e.g., a small clinical trial in which numerous repeated measurements are recorded for each subject).Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Statistical analyses of time-course and dose-response microarray experiments.
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035 $a (UnM)AAI3101578
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100 1 $a Eckel, Jeanette Elaine. $3 1943954
245 1 0 $a Statistical analyses of time-course and dose-response microarray experiments.
300 $a 190 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3609.
500 $a Directors: Chris Gennings; Vernon Chinchilli.
502 $a Thesis (Ph.D.)--Virginia Commonwealth University, 2003.
520 $a With the use of microarrays, the expression of tens of thousands of genes can be examined simultaneously to study the effects following exposure to a single chemical or following exposure to a mixture of chemicals. Measuring gene expression over a range of dose-concentrations (or similarly, over time) can expose similarities across genes and thus provide relationships in gene behavior, aid in determining gene function based on gene expression profiles, and reveal relationships between chemical treatments. We propose an extension to a recently developed gene-screening tool to reduce the dimensionality of a dose-response (or time-course) microarray dataset from tens of thousands of genes down to a subset of the most differentially expressed genes, which takes into account the continuous effect of dose (or time). To explore relationships among the subset of differentially expressed genes, we propose a multivariate model that allows for inter-gene as well as intra-gene correlated measurements. Rao's score test, a goodness-of-fit test for covariance matrices, is developed to test the goodness-of-fit of a parsimonious covariance (correlation) structure, which allows the number of genes in the corresponding covariance matrix to be larger than the number of independent tissue samples. Although, the development of Rao's score test for covariance matrices was motivated by microarray data, it is applicable to non-microarray data as well (e.g., a small clinical trial in which numerous repeated measurements are recorded for each subject).
590 $a School code: 2383.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Health Sciences, Toxicology. $3 1017752
690 $a 0308
690 $a 0383
710 2 0 $a Virginia Commonwealth University. $3 1018010
773 0 $t Dissertation Abstracts International $g 64-08B.
790 1 0 $a Gennings, Chris, $e advisor
790 1 0 $a Chinchilli, Vernon, $e advisor
790 $a 2383
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3101578