CLICK: a clustering algorithm with applications to gene expression analysis.
CLICK: a clustering algorithm with applications to gene expression analysis.
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发表时间：
2000
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通讯作者：
R. Sharan;R. Shamir
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作者：
R. Sharan;R. Shamir
新的DNA微阵列技术能够同时监测数千个基因的表达水平。当细胞经历特定条件或过程时，这允许对许多（或所有）基因的转录水平进行全局观察。分析基因表达数据需要将基因聚类成具有相似表达模式的组。我们已经开发了一种新的聚类算法，称为CLICK，这是适用于基因表达分析以及其他生物学应用。没有事先假设的结构或集群的数量。该算法利用图论和统计技术来识别高度相似的元素（内核）的紧密组，这些元素可能属于同一个真正的集群。几个启发式的程序，然后使用扩展到完整的聚类的内核。CLICK已在多种生物数据集上实施和测试，范围从基因表达、cDNA寡核苷酸指纹到蛋白质序列相似性。在所有这些应用程序中，它优于现有的算法，根据几个共同的数字的优点。CLICK也非常快，允许在几分钟内集群数千个元素，在普通工作站上几个小时内集群超过10万个元素。
Novel DNA microarray technologies enable the monitoring of expression levels of thousands of genes simultaneously. This allows a global view on the transcription levels of many (or all) genes when the cell undergoes specific conditions or processes. Analyzing gene expression data requires the clustering of genes into groups with similar expression patterns. We have developed a novel clustering algorithm, called CLICK, which is applicable to gene expression analysis as well as to other biological applications. No prior assumptions are made on the structure or the number of the clusters. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups of highly similar elements (kernels), which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clustering. CLICK has been implemented and tested on a variety of biological datasets, ranging from gene expression, cDNA oligo-fingerprinting to protein sequence similarity. In all those applications it outperformed extant algorithms according to several common figures of merit. CLICK is also very fast, allowing clustering of thousands of elements in minutes, and over 100,000 elements in a couple of hours on a regular workstation.