Xu Yanxun, Zhang Jie, Yuan Yuan, Mitra Riten, Müller Peter, Ji Yuan
Department of Statistics, Rice University, Houston, TX,
Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin.
IEEE Int Workshop Genomic Signal Process Stat. 2012 Dec;2012:135-138. doi: 10.1109/GENSIPS.2012.6507747.
We integrate three TCGA data sets including measurements on matched DNA copy numbers (C), DNA methylation (M), and mRNA expression (E) over 500+ ovarian cancer samples. The integrative analysis is based on a Bayesian graphical model treating the three types of measurements as three vertices in a network. The graph is used as a convenient way to parameterize and display the dependence structure. Edges connecting vertices infer specific types of regulatory relationships. For example, an edge between M and E and a lack of edge between C and E implies methylation-controlled transcription, which is robust to copy number changes. In other words, the mRNA expression is sensitive to methylational variation but not copy number variation. We apply the graphical model to each of the genes in the TCGA data independently and provide a comprehensive list of inferred profiles. Examples are provided based on simulated data as well.
我们整合了三个TCGA数据集，其中包括对500多个卵巢癌样本的匹配DNA拷贝数（C）、DNA甲基化（M）和mRNA表达（E）的测量。整合分析基于一个贝叶斯图形模型，该模型将这三种测量类型视为网络中的三个顶点。该图形用作参数化和显示依赖结构的便捷方式。连接顶点的边推断出特定类型的调控关系。例如，M和E之间的边以及C和E之间没有边意味着甲基化控制的转录，这对拷贝数变化具有鲁棒性。换句话说，mRNA表达对甲基化变异敏感，但对拷贝数变异不敏感。我们将图形模型独立应用于TCGA数据中的每个基因，并提供推断图谱的综合列表。还基于模拟数据提供了示例。
