Robust group fused lasso for multisample copy number variation detection under uncertainty

Hossein Sharifi Noghabi*, M. Mohammadi, Yao Hua Tan

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)


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 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.

Original languageEnglish
Pages (from-to)229-236
Number of pages8
JournalIET Systems Biology
Issue number6
Publication statusPublished - 2016


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