Date of Graduation

8-2017

Document Type

Thesis

Degree Name

Master of Science in Statistics and Analytics (MS)

Degree Level

Graduate

Department

Mathematical Sciences

Advisor/Mentor

Qingyang Zhang

Committee Member

Mark E. Arnold

Second Committee Member

Avishek Chakraborty

Keywords

Cross-Match Test, Gene Interactions, Kolmogorov-Smirnov Test, Microarray Data

Abstract

Human gene network is much more complex than just pairwise interaction among the genes. Zhang et al. [6] extracted microarray data from International Genomics Consortium (IGC), and presented the detection of three-way gene interactions in their paper using Fisher’s z-transformation test. Three-way gene interactions are closer than pairwise correlations in representing the complex gene structures. Additionally, it was more tractable than assessing four or more gene interactions. In this paper, we are simulating different models where Fisher’s test might not be as effective. Zhang et al.’s approach utilized Pearson’s correlation coefficients and involved detection of linear interactions only. Since gene interactions could show any kind of behavior, their evaluation approach might not work most of the time. Therefore, we are utilizing the dataset Zhang et al. provided in order to detect the three-way gene interaction using non-parametric tests like Kolmogorov-Smirnov and Cross-Match.

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