ConsensusCluster: A Software Tool for Unsupervised Cluster Discovery in Numerical Data
ConsensusCluster: A Software Tool for Unsupervised Cluster Discovery in Numerical Data
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DOI：
10.1089/omi.2009.0083
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发表时间：
2010-02-01
影响因子：
3.3
通讯作者：
Bhanot, Gyan
中科院分区：
文献类型：
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作者：
Seiler, Michael;Huang, C. Chris;Bhanot, Gyan
我们已经创建了一个独立的软件工具，ConsensusCluster，用于分析高维单核苷酸多态性（SNP）和基因表达微阵列数据。我们的软件实现了共识聚类算法和主成分分析，将数据分层到给定数量的鲁棒聚类中。鲁棒性是通过结合数据和样本重采样的聚类结果，以及通过对各种算法和参数设置进行平均以获得准确、稳定的聚类结果来实现的。我们在软件中实现了几种不同的聚类算法，包括K-Means、围绕介质的分区、自组织映射和分层聚类方法。在对数据进行聚类之后，ConsensusCluster生成共识矩阵热图，以提供有用的集群成员关系的可视化表示，并自动生成区分每对集群的选定特征的日志。ConsensusCluster提供了比普通软件包更健壮、更可靠的集群，因此，它是一个强大的无监督学习工具，可以发现数据中隐藏的模式，这些模式可能会揭示其生物学解释。该软件是免费的，可从http://code.google.com/p/consensus-cluster获得。
We have created a stand-alone software tool, ConsensusCluster, for the analysis of high-dimensional single nucleotide polymorphism ( SNP) and gene expression microarray data. Our software implements the consensus clustering algorithm and principal component analysis to stratify the data into a given number of robust clusters. The robustness is achieved by combining clustering results from data and sample resampling as well as by averaging over various algorithms and parameter settings to achieve accurate, stable clustering results. We have implemented several different clustering algorithms in the software, including K-Means, Partition Around Medoids, Self-Organizing Map, and Hierarchical clustering methods. After clustering the data, ConsensusCluster generates a consensus matrix heatmap to give a useful visual representation of cluster membership, and automatically generates a log of selected features that distinguish each pair of clusters. ConsensusCluster gives more robust and more reliable clusters than common software packages and, therefore, is a powerful unsupervised learning tool that finds hidden patterns in data that might shed light on its biological interpretation. This software is free and available from http://code.google.com/p/consensus-cluster.