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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