Open AccessRobust group fused lasso for multisample copy number variation detection under uncertainty
Author(s) -
Sharifi Noghabi Hossein,
Mohammadi Majid,
Tan YaoHua
Publication year - 2016
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2015.0081
Subject(s) - estimator , structural variation , copy number variation , computer science , outlier , lasso (programming language) , robustness (evolution) , variation (astronomy) , noise (video) , robust statistics , range (aeronautics) , algorithm , genome , pattern recognition (psychology) , artificial intelligence , mathematics , biology , statistics , genetics , engineering , gene , physics , world wide web , astrophysics , image (mathematics) , aerospace engineering
One of the most important needs in the post‐genome era is providing the researchers with reliable and efficient computational tools to extract and analyse this huge amount of biological data, in which DNA copy number variation (CNV) is a vitally important one. Array‐based comparative genomic hybridisation (aCGH) is a common approach in order to detect CNVs. Most of methods for this purpose were proposed for one‐dimensional profiles. 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 analysing multi‐sample aCGH profiles. In this study, the authors propose robust group fused lasso which utilises the robust group total variations. Instead of l 2,1 norm, the l 1 − l 2 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.