基因表达聚类的层次Dirichlet过程模型。

Liming Wang, Xiaodong Wang
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引用次数: 25

摘要

聚类是解释微阵列数据和基因组网络推断的重要数据处理工具。本文提出了一种基于层次Dirichlet过程(HDP)的聚类算法。HDP聚类在统计模型中引入了一种层次结构,该结构捕获了生物数据(如基因表达数据)中普遍存在的层次特征。针对HDP聚类问题,提出了一种基于中餐馆比喻的Gibbs抽样算法。我们将提出的HDP算法应用于调控网络分割和基因表达聚类。HDP算法通过揭示数据的底层层次结构而优于几种流行的聚类算法。对于酵母细胞周期数据,我们将HDP算法的结果与标准结果进行了比较,结果表明HDP算法提供了更多的信息,减少了不必要的聚类片段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Hierarchical Dirichlet process model for gene expression clustering.

Hierarchical Dirichlet process model for gene expression clustering.

Hierarchical Dirichlet process model for gene expression clustering.

Hierarchical Dirichlet process model for gene expression clustering.

: Clustering is an important data processing tool for interpreting microarray data and genomic network inference. In this article, we propose a clustering algorithm based on the hierarchical Dirichlet processes (HDP). The HDP clustering introduces a hierarchical structure in the statistical model which captures the hierarchical features prevalent in biological data such as the gene express data. We develop a Gibbs sampling algorithm based on the Chinese restaurant metaphor for the HDP clustering. We apply the proposed HDP algorithm to both regulatory network segmentation and gene expression clustering. The HDP algorithm is shown to outperform several popular clustering algorithms by revealing the underlying hierarchical structure of the data. For the yeast cell cycle data, we compare the HDP result to the standard result and show that the HDP algorithm provides more information and reduces the unnecessary clustering fragments.

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