Correction of Copy Number Variation Data Using Principal Component Analysis

被引：0
|
作者：
Chen, Jiayu [1 ]
Liu, Jingyu [1 ,2 ]
Calhoun, Vince D. [1 ,2 ]
机构：
[1] Univ New Mexico, Dept Elect Engn, Albuquerque, NM 87131 USA
[2] Mind Res Network, Albuquerque, NM USA
来源：
2010 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS (BIBMW) | 2010年
基金：
美国国家卫生研究院;
关键词：
copy number variation; Log R Ratio; principal component analysis;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Copy number variation (CNV) detection using SNP array data is challenging due to the low signal-to-noise ratio. In this study, we propose a principal component analysis (PCA) based correction to eliminate variance in CNV data induced by potential confounding factors. Simulations show a substantial improvement in CNV detection accuracy after correction. We also observe a significant improvement in data quality in real SNP array data after correction.
引用
收藏
页码：827 / 828
页数：2
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