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DETECTING AND UNDERSTANDING DIFFERENTIATION OF MICROARRAY
EXPRESSION DATA
by
Chao Cheng
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 2006
Copyright 2006 Chao Cheng
Object Description
| Title | Detecting and understanding differentiation of microarray expression data |
| Author | Cheng, Chao |
| Author email | chaochen@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computational Biology & Bioinformatics |
| School | College of Letters, Arts and Sciences |
| Date defended/completed | 2006-10-10 |
| Date submitted | 2006 |
| Restricted until | Unrestricted |
| Date published | 2006-10-23 |
| Advisor (committee chair) | Li, Lei |
| Advisor (committee member) |
Longo, Valter D. Sun, Fengzhu |
| Abstract | This thesis consists of three parts, reflecting three levels of Microarray data analysis.; In the first part, we introduce a new normalization method for Affymetrix oligonucleotide based arrays. Our perspective is to find a transformation that matches the distributions of hybridization levels of those probes corresponding to undifferentiated genes between arrays. We address two important issues. First, array-specific spatial patterns exist due to uneven hybridization and measurement process. Second, in some cases a substantially large portion of genes are differentially expressed between a target and a reference array. For the purpose of normalization we need to identify a subset that excludes those probes corresponding to differentially expressed genes and abnormal probes due to experimental variation. Least trimmed squares (LTS) is a natural choice to achieve this goal. Substantial differentiation is protected in LTS by setting an appropriate trimming fraction. To take into account any spatial pattern of hybridization, we divide each array into sub-arrays and normalize probe intensities within each sub-array. We illustrate the problem and solution through an Affymetrix spike-in data set with defined perturbation and a data set of primate brain expression.; In the second part, we describe a novel method to identify substantially perturbed genes in the treatment/control time course data sets. It is often difficult to compare expression patterns of a gene of two time courses for the following reasons: (1) the number of sampling time points may be different or hard to be aligned between the treatment and the control time courses; (2) estimation of the function that describes the expression of a gene in a time course is difficult and error-prone due to the limited number of time points. We propose a novel method to identify the differentially expressed genes between two time courses which avoid direct comparison of gene expression patterns of the two time courses. This method does not require alignment between the two time courses to be compared. Instead of attempting to "align" and compare the two time courses directly, we first convert the treatment and control time courses into two neighborhood systems that reflect the underlying relationships between genes. We then identify the differentially expressed genes by comparing the two gene relationship networks from the two neighborhood systems. To verify our method, we apply it to several treatment-control time course data sets. The results are consistent with the previous results and also give some new biologically meaningful findings.; In the third part, we describe our integrative analysis of Microarray data from longlived yeast mutants. To understand gene expression change in these mutants from a systematic perspective, we combine Microarray data with many other data sources, such as literatures, Gene Ontology, KEGG, and so on. Our results show that these longlived mutants share some common features in gene expression changes. Gene categories involved in basal transcription, translation and ion transportation tend to be downregulated. The glycolysis/gluconeogenisis pathway is significantly activated, whereas the oxidative phosphorylation pathway and the citric acid cycle pathway are somehow repressed. These findings may shed light on the underlying mechanisms of longevity of these mutants. |
| Keyword | microarray; time course; differentiation; ageing |
| 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 |
| Type | texts |
| Legacy record ID | usctheses-m100 |
| Rights | Cheng, Chao |
| 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-Cheng-20061023 |
| Archival file | uscthesesreloadpub_Volume29/etd-Cheng-20061023.pdf |
Description
| Title | Page 1 |
| Full text | DETECTING AND UNDERSTANDING DIFFERENTIATION OF MICROARRAY EXPRESSION DATA by Chao Cheng 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 2006 Copyright 2006 Chao Cheng |
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