This paper presents a general half quadratic framework for simultaneous analysis of the whole array comparative genomic hybridization (aCGH) profiles in a data set. The proposed framework accommodates different M-estimation loss functions and two underlying assumptions for aCGH profiles of a data set: sparsity and low rank. Using M-estimation loss functions, this framework is more robust to various types of noise and outliers. The solution of the proposed framework is given by half quadratic (HQ) minimization. To hasten this procedure, accelerated proximal gradient (APG) is utilized. Experimental results support the robustness of the proposed framework in comparison to the state-of-the-art algorithms. HighlightsA general framework for recovering multisample aCGH data.The solution of proposed formulation is given by Half Quadratic (HQ) methods.The proposed framework accommodates different robust M-estimators.Using Accelerated Proximal Gradient (APG) for speeding up the convergence rate.Numerous experimental results on simulated datasets with non-Gaussian noise to evaluate the robustness of the proposed framework.
['Majeed Mohammadi', 'Ghosheh Abed Hodtani']
A robust aCGH data recovery framework based on half quadratic minimization