Comprehensive cluster analysis with Transitivity Clustering
Comprehensive cluster analysis with Transitivity Clustering
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DOI：
10.1038/nprot.2010.197
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发表时间：
2011-03-01
期刊：
影响因子：
14.8
通讯作者：
Baumbach, Jan
中科院分区：
文献类型：
--
作者：
Wittkop, Tobias;Emig, Dorothea;Baumbach, Jan
传递性聚类是一种将生物数据划分为类似对象（例如基因）组的方法。它提供了对处理典型聚类分析的每个步骤的各种功能的集成访问。为了实现这一点，Transitivity Clustering可以在线访问，并提供了三个用户友好的界面：一个功能强大的独立版本、一个web界面和一组Cytoscape插件。在本文中，我们描述了三个主要的工作流程：(i)用Cytoscape进行蛋白（超）家族检测，（ii）用不完全金标准进行蛋白同源性检测，（iii）基因表达数据聚类。该协议指导用户了解传递性聚类的最重要特性，大约需要1小时才能完成。
Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super) family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes similar to 1 h to complete.