ArticleBioinformatics (Oxford, England)2015
Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Multidimensional molecular measurements-environment interaction analysis for disease outcomes.Biometrics · 2022Article
- Assisted differential network analysis for gene expression data.Genetic epidemiology · 2021Article
- Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion.Statistics in medicine · 2021Article
- Vertical integration methods for gene expression data analysis.Briefings in bioinformatics · 2021Review
- Integration of Proteomics and Other Omics Data.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Structured gene-environment interaction analysis.Biometrics · 2020Article
- Integrating approximate single factor graphical models.Statistics in medicine · 2020Article
- Smooth and Locally Sparse Estimation for Multiple-Output Functional Linear Regression.Journal of statistical computation and simulation · 2020Article
- Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer.Frontiers in genetics · 2020Article
- Horizontal and vertical integrative analysis methods for mental disorders omics data.Scientific reports · 2019Article
- Assisted graphical model for gene expression data analysis.Statistics in medicine · 2019Article
- Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis.Genetic epidemiology · 2019Article
- Robust network-based analysis of the associations between (epi)genetic measurements.Journal of multivariate analysis · 2018Article
- Clustering multilayer omics data using MuNCut.BMC genomics · 2018Article
- Analysis of cancer gene expression data with an assisted robust marker identification approach.Genetic epidemiology · 2017Article
- Assisted clustering of gene expression data using ANCut.BMC genomics · 2017Article
- Inferring gene regulatory relationships with a high-dimensional robust approach.Genetic epidemiology · 2017Article
- Biological Networks for Cancer Candidate Biomarkers Discovery.Cancer informatics · 2016Review
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Authors and funding
5 authors.
Funding
Abstract
motivationBoth 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.
resultsA 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. AVAILABILITY AND IMPLEMENTATION: R code is available at http://works.bepress.com/shuangge/49/.
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