Thesis (PhD--Statistics)--University of Auckland, 2009. / " A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics." Includes bibliographical references (p.165-184).

Thesis (doctoral)--University of Tartu, 2006. / This dissertation is based on 4 papers. Includes bibliographical references.

Thesis (M. Orth.)--University of Hong Kong, 2005.

Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: Guang R. Gao, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

ABF-1 is a human class ll basic helix-loop-helix transcription factor that is expressed predominately in EBV immortalized and activated B lymphocytes. A human cell line was stably transfected with a tetracycline regulated ABF-1 expression vector. The cell line revealed tight regulation of ABF-1 expression following stable incorporation of the vector into the genomic DNA. Upon induction of ABF-1 expression, the cell line exhibited a dramatic growth rate decrease. In order to monitor genes regulated by ABF-1, cells were collected both before and after induced ABF-1 expression and subjected to a microarray analysis. Early interpretations of the microarray data support the findings that ABF-1 may be regulating gene expression in a manner that facilitates withdrawal from the cell cycle.

published_or_final_version / abstract / Mathematics / Master / Master of Philosophy

published_or_final_version / abstract / Microbiology / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Zoology / Master / Master of Philosophy

In microarray analysis, people are interested in those features that have different characters in diseased samples compared to normal samples. The usual p-value method of selecting significant genes either gives too many false positives or cannot detect all the significant features. The False Discovery Rate (FDR) method controls false positives and at the same time selects significant features. We introduced Benjamini's method and Storey's method to control FDR, applied the two methods to human Meningioma data. We found that Benjamini's method is more conservative and that, after the number of the tests exceeds a threshold, increase in number of tests will lead to decrease in number of significant genes. In the second chapter, we investigate ways to search interesting gene expressions that cannot be detected by linear models as t-test or ANOVA. We propose a novel approach to use quadratic logistic regression to detect genes in Meningioma data that have non-linear relationship within phenotypes. By using quadratic logistic regression, we can find genes whose expression correlates to their phenotypes both linearly and quadratically. Whether these genes have clinical significant is a very interesting question, since these genes most likely be neglected by traditional linear approach.