Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach
Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach
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DOI：
10.1093/bioinformatics/btv518
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发表时间：
2015-12-15
期刊：
影响因子：
5.8
通讯作者：
Ma, Shuangge
中科院分区：
文献类型：
--
作者：
Shi, Xingjie;Zhao, Qing;Ma, Shuangge
动机：基因表达水平（GE）和拷贝数改变（CNA）都具有重要的生物学意义。通用电气公司部分地受到CNA的监管，并且已经投入了大量的努力来理解它们之间的关系。调控分析具有挑战性，一个基因的表达可能受到多个CNA的调控，一个CNA可能调控多个基因的表达。GE之间和CNA之间的相关性使分析变得更加复杂。现有的方法存在局限性，不能全面描述这种规律。结果：提出了稀疏双拉普拉斯收缩法。它联合模拟多个CNA对多个GE的影响。采用惩罚的方法来达到稀疏性，并识别规则关系。计算网络邻接度来描述GE之间和CNA之间的互连。两个拉普拉斯收缩惩罚施加，以适应网络邻接措施。仿真结果表明，该方法优于竞争的替代品，更准确的标记识别。癌症基因组图谱的数据进行了分析，以进一步证明所提出的方法的优点。
Motivation: 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.Results: 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.