Dependent Laplacian Bivariate Shrinkage Estimator for Swpt Based Array-cgh Data Smoothing

Array based comparative genomic hybridization (array-CGH) has merged as a highly efficient technique for the detection of chromosomal imbalances. Characteristics of these DNA copy number aberrations provide the insights into cancer, and they are useful for the diagnostic and therapy strategies. In this paper, we propose a statistical bivariate model for array CGH data in the stationary wavelet packet transform and apply this bivariate shrinkage estimator into the array CGH smoothing study. In our experiments, we use both synthetic data and real data. In synthetic data generation, we use a Gaussian noise assumption. The results of the Root Mean Squared Error (RMSE) and the Receiver Operating Characteristic curve (ROC) demonstrate our methods outperform the existing methods.