BioMaPS Institute, Rutgers University, Piscataway, New Jersey 08854, USA.
OMICS. 2010 Feb;14(1):109-13. doi: 10.1089/omi.2009.0083.
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 .
我们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，将数据分为给定数量的稳健聚类。通过结合数据和样本重采样的聚类结果，以及通过在各种算法和参数设置上进行平均，实现了准确、稳定的聚类结果，从而实现了稳健性。我们在软件中实现了几种不同的聚类算法，包括 K-Means、Partition Around Medoids、Self-Organizing Map 和层次聚类方法。对数据进行聚类后，ConsensusCluster 会生成一个共识矩阵热图，为聚类成员提供有用的可视化表示，并自动生成一个记录选定特征的日志，这些特征可以区分每对聚类。ConsensusCluster 生成的聚类比常见的软件包更稳健、更可靠，因此是一种强大的无监督学习工具，可以发现数据中的隐藏模式，这可能有助于对其进行生物学解释。该软件是免费的，可以从 http://code.google.com/p/consensus-cluster 获得。
