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首页> 外文会议>International Conference on Bioinformatics Models, Methods and Algorithms >Consensus Clustering for Cancer Gene Expression Data: Large-Scale Analysis using Evidence Accumulation Approach
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Consensus Clustering for Cancer Gene Expression Data: Large-Scale Analysis using Evidence Accumulation Approach

机译：癌症基因表达数据的共识聚类：使用证据积累方法进行大规模分析

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Clustering algorithms are extensively used on patient tissue samples in order to group and visualize the microarray data. The high dimensionality and probe specific noise make the selection of the appropriate clustering algorithm an uneasy task. This study presents a large-scale analysis of three clustering algorithms: k-means, hierarchical clustering (HC) and evidence accumulation clustering (EAC) on thirty-five cancer gene expression data sets selected to benchmark the performance of the clustering algorithms. Separated performance analysis was done on data sets from Affymetrix and cDNA chip platforms to examine the possible influence of the microarray technology. The study revealed no consistent algorithm ranking can be inferred, though in general EAC presented the best compromise of adjusted rand index (ARI) and variance. However, the results indicated that ARI variance under repeated k-means initializations offers useful information on the need to implement more complex clustering techniques. If repeated K-means converges to the same partition, also confirmed by the HC clustering, there is no need to run EAC. However, under moderate or highly variable ARI in repeated K-means, EAC should be used to reduce the uncertainty of clustering and unveil the data structure.
机译：聚类算法广泛用于患者组织样本，以便对微阵列数据进行组和可视化。高维度和探测特定噪声使得选择适当的聚类算法是一种不安的任务。本研究提出了三个聚类算法的大规模分析：K-means，分层聚类（HC）和35个癌症基因表达数据集的累计累积聚类（EAC），选择用于基准聚类算法的性能。分离的性能分析是在来自Affymetrix和CDNA芯片平台的数据集上进行的，以检查微阵列技术的可能影响。该研究揭示了可以推断一致的算法排名，但在一般的EAC呈现了调整后的兰特指数（ARI）和方差的最佳折衷。然而，结果表明，重复的K-Means初始化下的ARI差异提供了有关实现更复杂的聚类技术的有用信息。如果重复的K-means收敛到相同的分区，也通过HC群集确认，不需要运行EAC。然而，在重复的K-main中的中等或高度变量的ARI下，EAC应使用EAC来减少聚类的不确定性并揭示数据结构。

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