Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach

Bioinformatics
Xingjie ShiShuangge Ma

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/.

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Citations

Aug 20, 2019·Biometrics·Mengyun WuShuangge Ma
Aug 15, 2020·Briefings in Bioinformatics·Mengyun WuShuangge Ma
Sep 19, 2019·Scientific Reports·Shuaichao WangShuangge Ma
May 4, 2017·Genetic Epidemiology·Yangguang ZangShuangge Ma
Nov 17, 2020·Frontiers in Genetics·Quang-Huy NguyenDuc-Hau Le

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