TY - JOUR
T1 - Genotype copy number variations using Gaussian mixture models
T2 - Theory and algorithms
AU - Lin, Chang Yun
AU - Lo, Yungtai
AU - Ye, Kenny Q.
N1 - Funding Information:
KEYWORDS: microarray, rate of correct classification, common copy number variants, EM algorithm Author Notes: The research of CYL is supported by NIH P41 HG004222-01. The research of KY is in part supported by NIH P41 HG004222-01 and Simons Foundation. The authors would like to thank Drs. Dan Levy and Yunha Lee in Wigler Lab of Cold Spring Harbor Laboratory for their helps on sharing the NimbleGen HD2 data. We would also like to thank Dr. Kith Pradhan and Dr. Tao Wang for useful discussions. For all correspondence, please contact Dr. Kenny Q. Ye.
PY - 2012/9
Y1 - 2012/9
N2 - 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.
AB - 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.
KW - Common copy number variants
KW - EM algorithm
KW - Microarray
KW - Rate of correct classification
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U2 - 10.1515/1544-6115.1725
DO - 10.1515/1544-6115.1725
M3 - Article
C2 - 23079517
AN - SCOPUS:84874971703
SN - 1544-6115
VL - 11
JO - Statistical Applications in Genetics and Molecular Biology
JF - Statistical Applications in Genetics and Molecular Biology
IS - 5
M1 - 5
ER -