Journal article
Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach
Bioinformatics (Oxford, England), Vol.31(24), pp.3977-3983
12/15/2015
DOI: 10.1093/bioinformatics/btv518
PMCID: PMC5013934
PMID: 26342102
Abstract
Both gene expression levels (GEs) and copy number alterations (CNAs) have important biological implications. GEs are partly regulated by CNAs, and much effort has been devoted to understanding their relations. The regulation analysis is challenging with one gene expression possibly regulated by multiple CNAs and one CNA potentially regulating the expressions of multiple genes. The correlations among GEs and among CNAs make the analysis even more complicated. The existing methods have limitations and cannot comprehensively describe the regulation. A sparse double Laplacian shrinkage method is developed. It jointly models the effects of multiple CNAs on multiple GEs. Penalization is adopted to achieve sparsity and identify the regulation relationships. Network adjacency is computed to describe the interconnections among GEs and among CNAs. Two Laplacian shrinkage penalties are imposed to accommodate the network adjacency measures. Simulation shows that the proposed method outperforms the competing alternatives with more accurate marker identification. The Cancer Genome Atlas data are analysed to further demonstrate advantages of the proposed method. R code is available at http://works.bepress.com/shuangge/49/.
Details
- Title: Subtitle
- Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach
- Creators
- Xingjie Shi - Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, ChinaQing Zhao - Department of Biostatistics, Yale University, New Haven, CT, USAJian Huang - Department of Statistics and Actuarial Science, University of Iowa, Iowa, IA, USAYang Xie - Department of Clinical Science, The University of Texas Southwestern Medical Center, Dallas, TX, USA andShuangge Ma - Department of Biostatistics, Yale University, New Haven, CT, USA, VA Cooperative Studies Program Coordinating Center, West Haven, CT, USA
- Resource Type
- Journal article
- Publication Details
- Bioinformatics (Oxford, England), Vol.31(24), pp.3977-3983
- Publisher
- England
- DOI
- 10.1093/bioinformatics/btv518
- PMID
- 26342102
- PMCID
- PMC5013934
- ISSN
- 1460-2059
- eISSN
- 1367-4811
- Grant note
- P30CA016359 / NCI NIH HHS P50CA121974 / NCI NIH HHS R03 CA182984 / NCI NIH HHS CA142774 / NCI NIH HHS CA182984 / NCI NIH HHS P50 CA121974 / NCI NIH HHS
- Language
- English
- Date published
- 12/15/2015
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983985889002771
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