Date of Award:
Doctor of Philosophy (PhD)
Mathematics and Statistics
John R. Stevens
John R. Stevens
Abby D. Benninghoff
The main purpose of this dissertation was to examine the statistical dependence of imputed microRNA (miRNA) data in a colorectal cancer study. The dissertation addressed three related statistical issues that were raised by this study. the first statistical issue was motivated by the fact that miRNA expression was measured in paired tumor-normal samples of hundreds of patients, but data for many normal samples were missing due to lack of tissue availability. We compared the precision and power performance of several imputation methods, and drew attention to the statistical dependence induced by K-Nearest Neighbors (KNN) imputation. The second statistical issue was raised by the necessity to address the bimodality of distributions of miRNA data along with the imputation-induced dependency among subjects. We proposed and compared the performance of three nonparametric methods to identify the dierentially expressed miRNAs in the paired tumor-normal data while accounting for the imputation-induced dependence. The third statistical issue was related to the development of a normalization method for miRNA data that would reduce not only technical variation but also the variation caused by the characteristics of subjects, while maintaining the true biological dierences between arrays.
Suyundikov, Anvar, "Statistical Dependence in Imputed High-Dimensional Data for a Colorectal Cancer Study" (2015). All Graduate Theses and Dissertations. 4371.
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