标题： 基于集成模型的微阵列甲基化和表达数据聚类 显示英文标题

标题： Integrative Model-based clustering of microarray methylation and expression data

摘要： 在许多领域，研究人员对大型和复杂的生物过程感兴趣。 两个重要的例子是遗传学中的基因表达和DNA甲基化。 一个关键问题是识别这些过程的异常模式，并发现生物学上不同的组别。 本文我们开发了一种基于模型的方法来聚类此类数据。 我们的方法的基础在于为受试者的任何给定划分构建一个似然函数。 我们引入了特定于聚类的潜在指示器，与一些标准假设一起，为每个聚类施加特定的混合分布。 估计是通过EM算法完成的。 这些方法自然可以扩展到相似性质的多种数据类型，从而实现多个数据平台上的综合分析，进而提高区分能力。 显示英文摘要

摘要： In many fields, researchers are interested in large and complex biological processes. Two important examples are gene expression and DNA methylation in genetics. One key problem is to identify aberrant patterns of these processes and discover biologically distinct groups. In this article we develop a model-based method for clustering such data. The basis of our method involves the construction of a likelihood for any given partition of the subjects. We introduce cluster specific latent indicators that, along with some standard assumptions, impose a specific mixture distribution on each cluster. Estimation is carried out using the EM algorithm. The methods extend naturally to multiple data types of a similar nature, which leads to an integrated analysis over multiple data platforms, resulting in higher discriminating power.