酵母基因表达谱的超顺磁性聚类
Super-paramagnetic clustering of yeast gene expression profiles
摘要：
高密度DNA阵列，用于监测基因组规模的基因表达，产生了大量的信息，需要开发有效的计算方法来分析它们。重要的第一步是提取数据中固有的基因表达的基本模式。本文介绍了一种新的聚类算法，超顺磁性聚类（SPC）的应用程序来分析最近在酵母细胞周期的研究过程中产生的基因表达谱。SPC用于将基因组织成暗示其共调节的生物学相关簇。SPC的一些优点是其对噪声和初始化的鲁棒性，团簇形成和分裂的清晰特征，以及在每个分辨率下对团簇数量的无监督自组织确定。我们的分析揭示了几组之前尚未发现的基因的有趣相关行为。
英文摘要：
High-density DNA arrays, used to monitor gene expression at a genomic scale, have produced vast amounts of information which require the development of efficient computational methods to analyze them. The important first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of a novel clustering algorithm, Super-Paramagnetic Clustering (SPC) to analysis of gene expression profiles that were generated recently during a study of the yeast cell cycle. SPC was used to organize genes into biologically relevant clusters that are suggestive for their co-regulation. Some of the advantages of SPC are its robustness against noise and initialization, a clear signature of cluster formation and splitting, and an unsupervised self-organized determination of the number of clusters at each resolution. Our analysis revealed interesting correlated behavior of several groups of genes which has not been previously identified.
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