Date of Graduation
Master of Science in Statistics and Analytics (MS)
Mark E. Arnold
Second Committee Member
Cross-Match Test, Gene Interactions, Kolmogorov-Smirnov Test, Microarray Data
Human gene network is much more complex than just pairwise interaction among the genes. Zhang et al.  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.
Khadka, S. (2017). Identifying Three-Way Gene Interactions from Microarray Data using Kolmogorov-Smirnov and Cross-Match Tests. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/2449