An Entropy-Regularized Framework for Detecting Copy Number Variants

Abstract

The detection of DNA copy number variants (CNVs) is essential for the diagnosis and prognosis of multiple diseases including cancer. Array-based comparative genomic hybridization (aCGH) is a technique to find these aberrations. The available methods for CNV discovery are often predicated on several critical assumptions based on which various regularizations are employed. However, most of the resulting problems are not differentiable and finding their optimums needs massive computations. This paper addresses a new entropic regularization which is significantly fast and robust against various types of noise. The proposed problem takes advantage of the quadratic Renyi's entropy estimation which is not convex, but the half-quadratic programming gives an efficient solution with guaranteed convergence. We further theoretically prove that minimizing the Renyi's entropy estimation would induce the sparsity and smoothness, two salient and desired features for recovered aCGH profiles. Extensive experimental results on simulated and real datasets illustrate the robustness and speed of the proposed method in comparison to the state-of-the-art algorithms.

Unveiling and Unraveling Aggregation and Dispersion Fallacies in Group MCDM

“…Such estimators are called M-estimators that can replace the mean or median in equation (4). For example, the Welsch M-estimator is one of the well-known estimators that has shown promising performance in noisy environments (Mohammadi and Farahi, 2018;. To use this estimator for computing the compositional average array, we consider the log-ratio transformed data Ŵ and estimate the aggregated log-ratio transformed priority ŵg by solving the following optimization problem:…”