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Pattern-selection based power analysis and discrimination of low- and high-grade myelodysplastic syndromes study
Xiaorong Yang1, Xiaobo Zhou, Wan-Ting Huang
1Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Center for Biotechnology & Informatics, Houston, Texas, United States of America.
Abstract:
Copy Number Aberration (CNA) in myelodysplastic syndromes (MDS) study using single nucleotide polymorphism (SNP) arrays have been received increasingly attentions in the recent years. In the current study, a new Constraint Moving Average (CMA) algorithm is adopted to determine the regions of CNA regions first. In addition to large regions of CNA, using the proposed CMA algorithm, small regions of CNA can also be detected. Real-time Polymerase Chain Reaction (qPCR) results prove that the CMA algorithm presents an insightful discovery of both large and subtle regions. Based on the results of CMA, two independent applications are studied. The first one is power analysis for sample estimation. An accurate estimation of sample size needed for the desired purpose of an experiment will be important for effort-efficiency and cost-effectiveness. The power analysis is performed to determine the minimum sample size required for ensuring at least (0
