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NEW ANALYSIS METHODS FOR MICROARRAY DATA
by
Juan Nunez-Iglesias
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTATIONAL BIOLOGY AND BIOINFORMATICS)
December 2009
Copyright 2009 Juan Nunez-Iglesias
Object Description
| Title | New analysis methods for microarray data |
| Author | Nunez-Iglesias, Juan |
| Author email | nunezigl@usc.edu; jni.soma@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computational Biology & Bioinformatics |
| School | College of Letters, Arts and Sciences |
| Date defended/completed | 2009-08-21 |
| Date submitted | 2009 |
| Restricted until | Unrestricted |
| Date published | 2009-10-14 |
| Advisor (committee chair) | Zhou, Xianghong J. |
| Advisor (committee member) |
Waterman, Michael S. Finch, Caleb E. James, Gareth |
| Abstract | Microarray technology allows the simultaneous measurement of RNA levels in a cell culture or tissue sample. This has resulted in the generation of vast amounts of data, and the methods to analyze this onslaught are only just now catching up. In this thesis, we present three novel methods for the algorithmic and statistical analysis of microarray data.; First, in joint work with Michael R. Mehan, we systematically annotated over 300 microarray datasets with phenotype information, then used these annotations to find gene coexpression modules that are specific to particular phenotypes. Though previous methods existed to find recurrent coexpression modules, ours is the first to find phenotype-specific ones, avoiding the problem past methods faced of finding mostly general, cell-cycle related modules. Second, we studied the statistics of gene networks. Although the algorithms to study gene coexpression networks have become increasingly sophisticated, the statistics have failed to keep up. As a result, it is currently impossible to estimate the statistical significance of coexpression modules, or other network features. Our statistical investigations did not solve that problem, but they contribute to that end by demonstrating the deficiencies of past models and suggesting further avenues of research. Finally, we developed a new method to jointly study microRNA and messenger RNA (mRNA) microarray data. MicroRNAs are a novel class of RNA that regulates the translation and turnover of target mRNAs. Using a non-parametric permutation approach, we were able to show that levels of miRNAs are positively correlated with their targets in brain tissue. Thanks to the non-parametric approach, we can be confident that this is true independent of any potential biases in the sample data. Additionally, we found that specific miRNA-target regulatory pairs are altered in Alzheimer’s disease brain.; In all, the work presented in this thesis advances the field of microarray analysis, and in particular demonstrates how data from multiple arrays, multiple genes, and multiple datasets can be aggregated to enhance potentially subtle signals in microarray data. |
| Keyword | microarray analysis; nonparametric statistics; permutation; Alzheimer's disease; microRNA; miRNA; coexpression networks; graph statistics |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m2668 |
| Rights | Nunez-Iglesias, Juan |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-NunezIglesias-3306 |
| Archival file | uscthesesreloadpub_Volume56/etd-NunezIglesias-3306.pdf |
Description
| Title | Page 1 |
| Full text | NEW ANALYSIS METHODS FOR MICROARRAY DATA by Juan Nunez-Iglesias A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTATIONAL BIOLOGY AND BIOINFORMATICS) December 2009 Copyright 2009 Juan Nunez-Iglesias |
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