Correction of copy number variation data using principal component analysis

Jiayu Chen; Jingyu Liu; Vince D. Calhoun

doi:10.1109/BIBMW.2010.5703928

Correction of copy number variation data using principal component analysis

Jiayu Chen, Jingyu Liu, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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.

Original language	English (US)
Title of host publication	2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Pages	827-828
Number of pages	2
DOIs	https://doi.org/10.1109/BIBMW.2010.5703928
State	Published - 2010
Externally published	Yes
Event	2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 - HongKong, China Duration: Dec 18 2010 → Dec 21 2010

Publication series

Name	2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010

Other

Other	2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Country/Territory	China
City	HongKong
Period	12/18/10 → 12/21/10

Keywords

Copy number variation
Log R ratio
Principal component analysis

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

Access to Document

10.1109/BIBMW.2010.5703928

Cite this

Chen, J., Liu, J., & Calhoun, V. D. (2010). Correction of copy number variation data using principal component analysis. In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 (pp. 827-828). Article 5703928 (2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010). https://doi.org/10.1109/BIBMW.2010.5703928

Correction of copy number variation data using principal component analysis. / Chen, Jiayu; Liu, Jingyu; Calhoun, Vince D.
2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010. 2010. p. 827-828 5703928 (2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, J, Liu, J & Calhoun, VD 2010, Correction of copy number variation data using principal component analysis. in 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010., 5703928, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010, pp. 827-828, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010, HongKong, China, 12/18/10. https://doi.org/10.1109/BIBMW.2010.5703928

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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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AB - 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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Correction of copy number variation data using principal component analysis

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

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