Analysis of Gene Expression Profiles with Linear Mixed Models

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dc.contributor.advisor Greg Gibson, Committee Chair en_US
dc.contributor.advisor Russ Wolfinger, Committee Co-Chair en_US
dc.contributor.advisor Dennis Boos, Committee Member en_US
dc.contributor.advisor Spencer Muse, Committee Member en_US Hsieh, Wen-Ping en_US 2010-04-02T18:36:13Z 2010-04-02T18:36:13Z 2005-04-25 en_US
dc.identifier.other etd-03312005-002823 en_US
dc.description.abstract 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. 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. 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. 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. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject affymetrix en_US
dc.subject expression variation en_US
dc.subject probe profile en_US
dc.subject latin square en_US
dc.subject repeated measure en_US
dc.subject bivariate model en_US
dc.title Analysis of Gene Expression Profiles with Linear Mixed Models en_US PhD en_US dissertation en_US Bioinformatics en_US

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