NDLI: Discovering Recurrent Copy Number Aberrations in Complex Patterns via Non-Negative Sparse Singular Value Decomposition

Content Provider	ACM Digital Library
Author	Li, Ao Xi, Jianing
Copyright Year	2016
Description	Author Affiliation: School of Information Science and Technology University of Science and Technology of China(School of information science and technology centers for biomedical engineering university of science and technology of china (li, Ao; Xi, Jianing))
Abstract	AbstractRecurrent copy number aberrations RCNAs in multiple cancer samples are strongly associated with tumorigenesis, and RCNA discovery is helpful to cancer research and treatment. Despite the emergence of numerous RCNA discovering methods, most of them are unable to detect RCNAs in complex patterns that are influenced by complicating factors including aberration in partial samples, co-existing of gains and losses and normal-like tumor samples. Here, we propose a novel computational method, called non-negative sparse singular value decomposition NN-SSVD, to address the RCNA discovering problem in complex patterns. In NN-SSVD, the measurement of RCNA is based on the aberration frequency in a part of samples rather than all samples, which can circumvent the complexity of different RCNA patterns. We evaluate NN-SSVD on synthetic dataset by comparison on detection scores and Receiver Operating Characteristics curves, and the results show that NN-SSVD outperforms existing methods in RCNA discovery and demonstrate more robustness to RCNA complicating factors. Applying our approach on a breast cancer dataset, we successfully identify a number of genomic regions that are strongly correlated with previous studies, which harbor a bunch of known breast cancer associated genes.
Starting Page	656
Ending Page	668
Page Count	13
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2015.2474404
Volume Number	13
Issue Number	4
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-07-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Copy number aberrations Bioinformatics Cancer Recurrent Sparse singular value decomposition
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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