Statistical Stability and Biological Validity of Clustering Algorithms for Analyzing Microarray Data
Date of Award
8-8-2005
Degree Type
Closed Thesis
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
First Advisor
Susmita Datta - Chair
Second Advisor
Yu-Sheng Hsu
Third Advisor
Yichuan Zhao
Fourth Advisor
Ghengsheng Qin
Abstract
Simultaneous measurement of the expression levels of thousands to ten thousand genes in multiple tissue types is a result of advancement in microarray technology. These expression levels provide clues about the gene functions and that have enabled better diagnosis and treatment of serious disease like cancer. To solve the mystery of unknown gene functions, biological to statistical mapping is needed in terms of classifying the genes. Here we introduce a novel approach of combining both statistical consistency and biological relevance of the clusters produced by a clustering method. Here we employ two performance measures in combination for measuring statistical stability and functional similarity of the cluster members using a set of gene expressions with known biological functions. Through this analysis we construct a platform to predict about unknown gene functions using the outperforming clustering algorithm.
DOI
https://doi.org/10.57709/1059659
Recommended Citation
Karmakar, Saurav, "Statistical Stability and Biological Validity of Clustering Algorithms for Analyzing Microarray Data." Thesis, Georgia State University, 2005.
doi: https://doi.org/10.57709/1059659