Analysing DNA copy number changes using fused margin regression

Yuanjian Feng; Guoqiang Yu; Tian Li Wang; Ie Ming Shih; Yue Wang

doi:10.1504/IJFIPM.2010.033242

Analysing DNA copy number changes using fused margin regression

Yuanjian Feng, Guoqiang Yu, Tian Li Wang, Ie Ming Shih, Yue Wang

School of Medicine

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

DNA copy number change is an important form of structural variations in human genomes. Detecting copy number changes using DNA array data is a challenging task due to high density genomic loci, low signal to noise ratios, and normal tissue contamination. We propose Fused Margin Regression (FMR) method that combines a variable fusion rule and robust ε-insensitive loss criterion to estimate piecewise constant segments of the underlying copy number profile. We tested FMR method on both simulation and real CGH and SNP array datasets, and observed competitively improved performance as compared to several widely-adopted existing methods.

Original language	English (US)
Pages (from-to)	3-15
Number of pages	13
Journal	International Journal of Functional Informatics and Personalised Medicine
Volume	3
Issue number	1
DOIs	https://doi.org/10.1504/IJFIPM.2010.033242
State	Published - Jan 1 2010

Keywords

CGH copy number
DNA copy number change detection
FMR
Fused margin regression
L1-norm regularisation
Regularised regression
SNP copy number
SVR
Segmentation
Support vector regression
Variable fusion

ASJC Scopus subject areas

Clinical Neurology

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10.1504/IJFIPM.2010.033242

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abstract = "DNA copy number change is an important form of structural variations in human genomes. Detecting copy number changes using DNA array data is a challenging task due to high density genomic loci, low signal to noise ratios, and normal tissue contamination. We propose Fused Margin Regression (FMR) method that combines a variable fusion rule and robust ε-insensitive loss criterion to estimate piecewise constant segments of the underlying copy number profile. We tested FMR method on both simulation and real CGH and SNP array datasets, and observed competitively improved performance as compared to several widely-adopted existing methods.",

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AU - Feng, Yuanjian

AU - Yu, Guoqiang

AU - Wang, Tian Li

AU - Shih, Ie Ming

AU - Wang, Yue

PY - 2010/1/1

Y1 - 2010/1/1

N2 - DNA copy number change is an important form of structural variations in human genomes. Detecting copy number changes using DNA array data is a challenging task due to high density genomic loci, low signal to noise ratios, and normal tissue contamination. We propose Fused Margin Regression (FMR) method that combines a variable fusion rule and robust ε-insensitive loss criterion to estimate piecewise constant segments of the underlying copy number profile. We tested FMR method on both simulation and real CGH and SNP array datasets, and observed competitively improved performance as compared to several widely-adopted existing methods.

AB - DNA copy number change is an important form of structural variations in human genomes. Detecting copy number changes using DNA array data is a challenging task due to high density genomic loci, low signal to noise ratios, and normal tissue contamination. We propose Fused Margin Regression (FMR) method that combines a variable fusion rule and robust ε-insensitive loss criterion to estimate piecewise constant segments of the underlying copy number profile. We tested FMR method on both simulation and real CGH and SNP array datasets, and observed competitively improved performance as compared to several widely-adopted existing methods.

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KW - DNA copy number change detection

KW - FMR

KW - Fused margin regression

KW - L1-norm regularisation

KW - Regularised regression

KW - SNP copy number

KW - SVR

KW - Segmentation

KW - Support vector regression

KW - Variable fusion

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Analysing DNA copy number changes using fused margin regression

Abstract

Keywords

ASJC Scopus subject areas

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