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National Chung Hsing University Institutional Repository - NCHUIR > 理學院 > 統計學研究所 > 依資料類型分類 > 期刊論文 >  Genotype Copy Number Variations using Gaussian Mixture Models: Theory and Algorithms

Please use this identifier to cite or link to this item: http://nchuir.lib.nchu.edu.tw/handle/309270000/145645

標題: Genotype Copy Number Variations using Gaussian Mixture Models: Theory and Algorithms
作者: Lin, Chang-Yun;Lo, Yungtai;Kenny, Q.Ye
Contributors: Wei Chun Wang
關鍵字: microarray;rate of correct classification;common copy number variants;EM algorithm
日期: 2012-10
Issue Date: 2013-07-02 10:16:00 (UTC+8)
摘要: Copy number variations (CNVs) are important in the disease association studies and are
usually targeted by most recent microarray platforms developed for GWAS studies. However, the
probes targeting the same CNV regions could vary greatly in performance, with some of the probes
carrying little information more than pure noise. In this paper, we investigate how to best combine
measurements of multiple probes to estimate copy numbers of individuals under the framework of
Gaussian mixture model (GMM). First we show that under two regularity conditions and assume all
the parameters except the mixing proportions are known, optimal weights can be obtained so that
the univariate GMM based on the weighted average gives the exactly the same classification as the
multivariate GMM does. We then developed an algorithm that iteratively estimates the parameters
and obtains the optimal weights, and uses them for classification. The algorithm performs well on
simulation data and two sets of real data, which shows clear advantage over classification based on
the equal weighted average.
Relation: Statistical Applications in Genetics and Molecular Biology, Volume 11, Issue 5,
Appears in Collections:[依資料類型分類] 期刊論文

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