Clustering distributions with the marginalized nested Dirichlet process
Clustering distributions with the marginalized nested Dirichlet process
复制标题
使用边缘化嵌套狄利克雷过程进行聚类分布
作者：
D. Zuanetti;P. Müller;Yitan Zhu;Shengjie Yang;Yuan Ji
我们介绍了一个边缘版本的嵌套狄利克雷过程集群分布或直方图。我们应用该模型聚类基因的模式基因相互作用。所提出的方法是基于嵌套分区，这是隐含在嵌套狄利克雷过程的原始建设。它允许模拟精确推理，而不是截断狄利克雷过程近似。更重要的是，该构造突出了嵌套狄利克雷过程作为实验单元的嵌套分区的性质。我们将该模型应用于推断与DNA错配修复（DMR）相关的基因的分布基因与其他基因的相互作用的聚类基因。基因-基因相互作用记录为两个基因共表达的自逻辑模型中的系数，调整拷贝数变异、甲基化和蛋白质活化。这些系数是从一个名为Zodiac的在线数据库中提取的，该数据库是根据癌症基因组图谱（TCGA）数据计算的。我们将结果与基于聚类分布的k均值聚类、截断NDP和层次聚类方法进行了比较。在模拟条件下以及在真实的数据集中，所提出的推理均表现出良好的性能。
We introduce a marginal version of the nested Dirichlet process to cluster distributions or histograms. We apply the model to cluster genes by patterns of gene–gene interaction. The proposed approach is based on the nested partition that is implied in the original construction of the nested Dirichlet process. It allows simulation exact inference, as opposed to a truncated Dirichlet process approximation. More importantly, the construction highlights the nature of the nested Dirichlet process as a nested partition of experimental units. We apply the proposed model to inference on clustering genes related to DNA mismatch repair (DMR) by the distribution of gene–gene interactions with other genes. Gene–gene interactions are recorded as coefficients in an auto‐logistic model for the co‐expression of two genes, adjusting for copy number variation, methylation and protein activation. These coefficients are extracted from an online database, called Zodiac, computed based on The Cancer Genome Atlas (TCGA) data. We compare results with a variation of k‐means clustering that is set up to cluster distributions, truncated NDP and a hierarchical clustering method. The proposed inference shows favorable performance, under simulated conditions and also in the real data sets.
影响因子：
10.3
作者：
Zhu, Yitan;Xu, Yanxun;Ji, Yuan
通讯作者：
Ji, Yuan