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diseases such as cancer and autism. Microarray techniques are used to detect copy number variations with high- resolution. However, the observed copy numbers are corrupted by noise, making variations breakpoints hard to detect. Various techniques had been proposed to uncover the true copy number changes. In this study, we provide a framework for the analysis of copy number datasets. It is divided into two parts: The sigma filter as pre-processing technique, and statistical models for classifying nonrandom variations across multiple samples. Finally, we compared our results with reported variations in real samples.