利用主成分分析法校正拷贝数变异数据。

IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2010-12-01 Epub Date: 2011-01-28 DOI:10.1109/BIBMW.2010.5703928

Jiayu Chen, Jingyu Liu, Vince D Calhoun

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摘要

由于信噪比低，使用 SNP 阵列数据进行拷贝数变异 (CNV) 检测具有挑战性。在本研究中，我们提出了一种基于主成分分析（PCA）的校正方法，以消除潜在混杂因素引起的 CNV 数据方差。模拟结果表明，校正后 CNV 检测准确率大幅提高。我们还观察到，经过校正后，真实 SNP 阵列数据的数据质量也有明显改善。

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Correction of Copy Number Variation Data Using Principal Component Analysis.

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.

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IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine

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