Identifying projected clusters from gene expression profiles.
Identifying projected clusters from gene expression profiles.
复制标题
从基因表达谱中识别预测的簇。
DOI：
10.1016/j.jbi.2004.05.002
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Cheung,Kei-Hoi
中科院分区：
文献类型：
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作者：
Yip,KevinY;Cheung,DavidW;Ng,MichaelK;Cheung,Kei-Hoi
在微阵列基因表达数据中，聚类可能隐藏在特定的子空间中。例如，一组共调节基因可以仅在其中存在某些调节因子的样品的子集中具有相似的表达模式。当在完整的输入空间中测量时，它们的表达模式可能不同。利用这种相似性度量的传统聚类算法可能无法识别聚类。近年来，人们提出了许多算法来识别这类投影聚类，但其中许多算法依赖于一些关键参数，用户很难确定这些参数的正确值。本文提出了一种动态调整其内部阈值的新算法。它对用户参数的依赖性很低，同时允许用户输入一些可用的领域知识。实验结果表明，该算法能够识别出一些感兴趣的投影簇。
In microarray gene expression data, clusters may hide in certain subspaces. For example, a set of co-regulated genes may have similar expression patterns in only a subset of the samples in which certain regulating factors are present. Their expression patterns could be dissimilar when measuring in the full input space. Traditional clustering algorithms that make use of such similarity measurements may fail to identify the clusters. In recent years a number of algorithms have been proposed to identify this kind of projected clusters, but many of them rely on some critical parameters whose proper values are hard for users to determine. In this paper, a new algorithm that dynamically adjusts its internal thresholds is proposed. It has a low dependency on user parameters while allowing users to input some domain knowledge should they be available. Experimental results show that the algorithm is capable of identifying some interesting projected clusters.