ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2011
A pipeline for copy number variation detection based on principal component analysis.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- CNV-PCC: An efficient method for detecting copy number variations from next-generation sequencing data.Frontiers in bioengineering and biotechnology · 2022Article
- Association between copy number variation losses and alcohol dependence across African American and European American ethnic groups.Alcoholism, clinical and experimental research · 2014Article
- Effect of homozygous deletions at 22q13.1 on alcohol dependence severity and cue-elicited BOLD response in the precuneus.Addiction biology · 2013Article
- The impact of copy number deletions on general cognitive ability and ventricle size in patients with schizophrenia and healthy control subjects.Biological psychiatry · 2013Article
- Association of genetic copy number variations at 11 q14.2 with brain regional volume differences in an alcohol use disorder population.Alcohol (Fayetteville, N.Y.) · 2012Article
- A pilot study on collective effects of 22q13.31 deletions on gray matter concentration in schizophrenia.PloS one · 2012Article
- Rare copy number deletions predict individual variation in human brain metabolite concentrations in individuals with alcohol use disorders.Biological psychiatry · 2011Article
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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