A Penalized Likelihood Estimation on Transcriptional Module-Based Clustering
A Penalized Likelihood Estimation on Transcriptional Module-Based Clustering
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DOI：
10.1007/11424857_42
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发表时间：
2005-05
期刊：
影响因子：
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通讯作者：
Ryo Yoshida;S. Imoto;T. Higuchi
中科院分区：
文献类型：
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作者：
Ryo Yoshida;S. Imoto;T. Higuchi
在本文中，我们提出了一个新的聚类程序的高维微阵列数据。微阵列数据聚类分析的主要困难在于待聚类的样本数量远小于数据的维数，数据的维数等于分析中使用的基因数量。在这种情况下，传统的基于模型的聚类的适用性受到过度学习的发生的限制。所提出的方法的一个关键思想是寻求一个线性映射的数据到低维子空间进行聚类分析之前。构造线性映射，使得变换后的数据成功地揭示了原始数据空间中存在的聚类。聚类规则应用于转换后的数据，而不是原始数据。我们还建立了这种方法和概率框架之间的联系，即混合因素模型的惩罚似然估计。通过真实的应用验证了该方法的有效性。
In this paper, we propose a new clustering procedure for high dimensional microarray data. Major difficulty in cluster analysis of microarray data is that the number of samples to be clustered is much smaller than the dimension of data which is equal to the number of genes used in an analysis. In such a case, the applicability of conventional model-based clustering is limited by the occurence of overlearning. A key idea of the proposed method is to seek a linear mapping of data onto the low-dimensional subspace before proceeding to cluster analysis. The linear mapping is constructed such that the transformed data successfully reveal clusters existed in the original data space. A clustering rule is applied to the transformed data rather than the original data. We also establish a link between this method and a probabilistic framework, that is, a penalized likelihood estimation of the mixed factors model. The effectiveness of the proposed method is demonstrated through the real application.