Cho Seong Beom
Department of Biomedical Informatics, College of Medicine, Gachon University, Seongnam-Daero 1342, Korea.
Entropy (Basel). 2020 Dec 18;22(12):1434. doi: 10.3390/e22121434.
The integrative analysis of copy number alteration (CNA) and gene expression (GE) is an essential part of cancer research considering the impact of CNAs on cancer progression and prognosis. In this research, an integrative analysis was performed with generalized differentially coexpressed gene sets (gdCoxS), which is a modification of dCoxS. In gdCoxS, set-wise interaction is measured using the correlation of sample-wise distances with Renyi's relative entropy, which requires an estimation of sample density based on omics profiles. To capture correlations between the variables, multivariate density estimation with covariance was applied. In the simulation study, the power of gdCoxS outperformed dCoxS that did not use the correlations in the density estimation explicitly. In the analysis of the lower-grade glioma of the cancer genome atlas program (TCGA-LGG) data, the gdCoxS identified 577 pathway CNAs and GEs pairs that showed significant changes of interaction between the survival and non-survival group, while other benchmark methods detected lower numbers of such pathways. The biological implications of the significant pathways were well consistent with previous reports of the TCGA-LGG. Taken together, the gdCoxS is a useful method for an integrative analysis of CNAs and GEs.
考虑到拷贝数改变（CNA）对癌症进展和预后的影响，CNA与基因表达（GE）的综合分析是癌症研究的重要组成部分。在本研究中，使用广义差异共表达基因集（gdCoxS）进行了综合分析，gdCoxS是dCoxS的一种改进。在gdCoxS中，使用样本间距离与Renyi相对熵的相关性来测量集合间的相互作用，这需要基于组学图谱估计样本密度。为了捕捉变量之间的相关性，应用了带协方差的多元密度估计。在模拟研究中，gdCoxS的功效优于未在密度估计中明确使用相关性的dCoxS。在癌症基因组图谱计划（TCGA-LGG）数据的低级别胶质瘤分析中，gdCoxS识别出577个通路CNA和GE对，这些对在生存组和非生存组之间显示出相互作用的显著变化，而其他基准方法检测到的此类通路数量较少。显著通路的生物学意义与TCGA-LGG的先前报告高度一致。综上所述，gdCoxS是一种用于CNA和GE综合分析的有用方法。
