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Correction of Copy Number Variation Data Using Principal Component Analysis
Jiayu Chen1, Jingyu Liu1,2, Vince D Calhoun1,2
1Dept. of Electrical Engineering, University of New Mexico, Albuquerque, NM.
Abstract:
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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