TY - JOUR T1 - Robust Group Fused Lasso for Multisample CNV Detection under Uncertainty JF - bioRxiv DO - 10.1101/029769 SP - 029769 AU - Hossein Sharifi Noghabi AU - Majid Mohammadi Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/10/23/029769.abstract N2 - One of the most important needs in the post-genome era is providing the researchers with reliable and efficient computational tools to extract and analyze this huge amount of biological data, in which DNA copy number variation (CNV) is a vitally important one. Array-based comparative genomic hybridization (aCGH) is a common approach in order to detect CNVs. Most of methods for this purpose were proposed for one-dimensional profile. However, slightly this focus has moved from one- to multi-dimensional signals. In addition, since contamination of these profiles with noise is always an issue, it is highly important to have a robust method for analyzing multi-sample aCGH data. In this paper, we propose Robust Grouped Fused Lasso (RGFL) which utilizes the Robust Group Total Variations (RGTV). Instead of l2,1 norm, the l1-l2 M-estimator is used which is more robust in dealing with non-Gaussian noise and high corruption. More importantly, Correntropy (Welsch M-estimator) is also applied for fitting error. Extensive experiments indicate that the proposed method outperforms the state-of-the art algorithms and techniques under a wide range of scenarios with diverse noises. ER -