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Heat Map Visualizations Allow Comparison of Multiple Clustering Results and Evaluation of Dataset Quality: Application to Microarray Data

机译：热图可视化允许比较多个聚类结果并评估数据集质量：应用于微阵列数据

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Since clustering algorithms are heuristic, multiple clustering algorithms applied to the same dataset will typically not generate the same sets of clusters. This is especially true for complex datasets such as those from microarray time series experiments. Two such microarray datasets describing gene expression activities from regenerating newt forelimbs at various times following limb amputation were used in this study. A cluster stability matrix, which shows the number of times two genes appear in the same cluster, was generated as a heat map. This was used to evaluate the overall variation among the clustering algorithms and to identify similar clusters. A comparison of the cluster stability matrices for two related microarray experiments with different levels of precision was shown to be an effective basis for comparing the quality of the two sets of experiments. A pairwise heat map was generated to show which pairs of clustering algorithms grouped the data into similar clusters.
机译：由于聚类算法是启发式的，因此应用于同一数据集的多个聚类算法通常不会生成相同的聚类集。对于复杂数据集（例如来自微阵列时间序列实验的数据集）尤其如此。在这项研究中，使用了两个这样的微阵列数据集，描述了肢体截肢后不同时间再生new的前肢的基因表达活性。生成簇稳定性矩阵，该矩阵显示两个基因出现在同一簇中的次数，作为热图。这用于评估聚类算法之间的总体差异并确定相似的聚类。两个不同精度水平的相关微阵列实验的簇稳定性矩阵的比较被证明是比较两组实验质量的有效基础。生成了成对的热图，以显示哪些成对的聚类算法将数据分组为相似的聚类。

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