Correction of copy number variation data using principal component analysis

Jiayu Chen, Jingyu Liu, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Pages827-828
Number of pages2
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 - HongKong, China
Duration: Dec 18 2010Dec 21 2010

Publication series

Name2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010

Other

Other2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
CountryChina
CityHongKong
Period12/18/1012/21/10

Keywords

  • Copy number variation
  • Log R ratio
  • Principal component analysis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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