A Comparison Study of Signal Extensions Methods for Wavelet Denoising of Array Cgh Data

Array-based comparative genome hybridization (array CGH) is a recently developed high-throughput technique to detect DNA copy number aberrations. Typically, array CGH data is noisy. Wavelet denoising was previously shown to have superior performance for denoising array CGH data. However, the effect of different signal extensions methods on the performance of wavelet denoising in this particular application has not been previously studied. In this paper, we performed a comparison study of three signal extensions methods (zero-padding, periodic extension, and symmetrization) for wavelet denoising of array CGH data using realistically generated synthetic data. Empirical results suggest that the zero-padding method outperforms the other two methods by 0.9–1.2% in terms of the overall root mean squared error. The difference is statistically significant (P < 0.01) in at least 80% of all test cases.