Design and Analysis of Large Scale Gene Expression Experiments and the Application to Angiogenesis and Blood Vessel Maturation
AuthorGreer, Kevin A
AdvisorHoying, James B.
Committee ChairHoying, James B.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractThe objective of this dissertation was to develop an experimental approach and supporting software for performing and interpreting the results of micoarray-based experiments, as well as apply this approach to an experimental model of angiogenesis and blood vessel development. When this project was initiated microarray technology was in its infancy and the standard experimental design was to hybridize two samples against each other and report intensity ratios that were greater than two-fold. In order to study the changes in gene expression that occur over the course of the vascularization process, it became clear that a new approach to microarray experimental design and analysis was required. It was also clear that most researchers were ill-equipped to process and interpret the tens of thousands of data points generated by microarray experiments. To address these needs, a software package called CARMA (Computational Analysis of Replicated Measurements for Arrays) was developed to perform an analysis of variance (ANOVA) on microarray experiments that incorporate replicated measurements. Utilizing replicated measurement-based designs makes it possible to incorporate multiple samples into the experimental design and calculate both the magnitude and the statistical significance of the differences in gene expression between samples. Software was also developed to implement and compare different algorithms and distance metrics for performing hierarchical clustering. Hierarchical clustering groups genes together based on the similarity of their expression profiles, and is used to reduce the complexity of a microarray dataset and identify genes that may be involved in the same or related processes or under similar types of transcriptional control. Utilizing simulated datasets containing known clusters of genes, the ability of each each algorithm/distance metric combination to recover the original clusters was evaluated. Lastly, both CARMA and hierarchical clustering were utilized to analyze changes in gene expression during the process of vascularization in an experimental model of angiogenesis and blood vessel maturation. Based on high-level patterns of gene expression and morphological measurements obtained using this model, a multi-phase model of angiogenesis-based vascularization is presented consisting of an initial angiogenic phase, followed by a maturation and network remodeling phase.
Degree ProgramBiomedical Engineering