Differential coexpression has recently emerged as a new way to establish a fundamental difference in expression pattern among a group of genes between two populations. Earlier methods used some scoring techniques to detect changes in correlation patterns of a gene pair in two conditions. However, modeling differential coexpression by means of finding differences in the dependence structure of the gene pair has hitherto not been carried out. We exploit a copula-based framework to model differential coexpression between gene pairs in two different conditions. The Copula is used to model the dependency between expression profiles of a gene pair. For a gene pair, the distance between two joint distributions produced by copula is served as differential coexpression. We used five pan-cancer TCGA RNA-Seq data to evaluate the model that outperforms the existing state of the art. Moreover, the proposed model can detect a mild change in the coexpression pattern across two conditions. For noisy expression data, the proposed method performs well because of the popular scale-invariant property of copula. In addition, we have identified differentially coexpressed modules by applying hierarchical clustering on the distance matrix. The identified modules are analyzed through Gene Ontology terms and KEGG pathway enrichment analysis.

近年来，差异共表达已成为一种新的方法，可以在两个群体之间的一组基因之间建立表达模式的根本差异。较早的方法使用一些计分技术来检测两种情况下基因对相关模式的变化。然而，迄今为止尚未通过发现基因对的依赖性结构差异来模拟差异共表达。我们利用基于copula的框架来模拟两种不同条件下基因对之间的差异共表达。Copula用于建模基因对表达谱之间的依赖性。对于一个基因对，由copula产生的两个关节分布之间的距离用作差异共表达。我们使用了五个全癌TCGA RNA-Seq数据来评估优于现有技术水平的模型。此外，提出的模型可以检测到两种条件下共表达模式的轻微变化。对于嘈杂的表达数据，由于copula具有普遍的尺度不变性，因此该方法表现良好。此外，我们通过在距离矩阵上应用分层聚类来识别差异共表达模块。通过Gene Ontology术语和KEGG途径富集分析来分析所识别的模块。我们通过在距离矩阵上应用分层聚类来识别差异共表达模块。通过Gene Ontology术语和KEGG途径富集分析来分析所识别的模块。我们通过在距离矩阵上应用分层聚类来识别差异共表达模块。通过Gene Ontology术语和KEGG途径富集分析来分析所识别的模块。