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Analysis of gene expression profiles...

Hsieh, Wen-Ping.

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Analysis of gene expression profiles with linear mixed models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analysis of gene expression profiles with linear mixed models./
Author:	Hsieh, Wen-Ping.
Description:	117 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 0649.
Contained By:	Dissertation Abstracts International66-02B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3164014
ISBN:	9780496982585

Analysis of gene expression profiles with linear mixed models.
Hsieh, Wen-Ping.

Analysis of gene expression profiles with linear mixed models. - 117 p.

Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 0649.

Thesis (Ph.D.)--North Carolina State University, 2005.

With the emergence of high throughput technology, proper interpretation of data has become critical for many aspects of biomedical research. My dissertation explores two major issues in gene expression profile microarray data analysis. One is quantification of variation across and among species and its effect on biological interpretation. The second part of my work is to develop better statistical estimates that can account for different sources of variation for significant gene detection.

ISBN: 9780496982585Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Analysis of gene expression profiles with linear mixed models.
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100 1 $a Hsieh, Wen-Ping. $3 1911974
245 1 0 $a Analysis of gene expression profiles with linear mixed models.
300 $a 117 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 0649.
500 $a Directors: Greg Gibson; Russ Wolfinger.
502 $a Thesis (Ph.D.)--North Carolina State University, 2005.
520 $a With the emergence of high throughput technology, proper interpretation of data has become critical for many aspects of biomedical research. My dissertation explores two major issues in gene expression profile microarray data analysis. One is quantification of variation across and among species and its effect on biological interpretation. The second part of my work is to develop better statistical estimates that can account for different sources of variation for significant gene detection.
520 $a A previously published dataset of oligonucleotide array data for three primate species was analyzed with linear mixed models. By decomposing the variation of expression into different explanatory factors, the differences among species as well as between tissues was revealed at the expression level. Issues of cross-species hybridization and expression divergence compared to mutation-drift equilibrium were addressed.
520 $a The power and flexibility of the linear mixed model framework for detection of differentially expressed genes was then explored with a dataset that includes spiked-in controls. The impact of probe-level sequence variation on cross-hybridization was detected through a Gibb's sampling method that highlights potential problems for short oligonucleotide microarray data analysis. A motif as short as fifteen bases can possibly cause significant cross-hybridization.
520 $a Finally, a bivariate model using information from both perfect match probes and mismatch probes was proposed as a means to increase the statistical power for detection of significant differences in gene expression. The improved performance of the method was demonstrated through Monte Carlo simulation. The detection power can increase as much as 20% with 5% false positive rate under certain circumstances.
590 $a School code: 0155.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Genetics. $3 1017730
690 $a 0308
690 $a 0369
710 2 0 $a North Carolina State University. $3 1018772
773 0 $t Dissertation Abstracts International $g 66-02B.
790 1 0 $a Gibson, Greg, $e advisor
790 1 0 $a Wolfinger, Russ, $e advisor
790 $a 0155
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3164014