A pipeline for copy number variation detection based on principal component analysis

Jiayu Chen, Jingyu Liu, David Boutte, Vince D. Calhoun

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

Abstract

DNA copy number variation (CNV), an important structural variation, is known to be pervasive in the human genome and the determination of CNVs is essential to understanding their potential effects on the susceptibility to diseases. However, 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 approach for data correction, and present a novel processing pipeline for reliable CNV detection. Tested data include both simulated and real SNP array datasets. Simulations demonstrate a substantial reduction in the false positive rate of CNV detection after PCA-correction. And we also observe a significant improvement in data quality in real SNP array data after correction.

Original languageEnglish (US)
Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages6975-6978
Number of pages4
DOIs
StatePublished - 2011
Externally publishedYes
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Country/TerritoryUnited States
CityBoston, MA
Period8/30/119/3/11

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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