Furby: fuzzy force-directed bicluster visualization
Furby: fuzzy force-directed bicluster visualization
复制标题
Furby：模糊力导向双簇可视化
作者：
M. Streit;S. Gratzl;Michael Gillhofer;Andreas Mayr;Andreas Mitterecker;Sepp Hochreiter
聚类分析被广泛用于发现多维数据中的模式。聚集热图是可视化单向和双向聚类结果的标准技术。在聚类的热图中，对行和/或列进行了重新排序，从而导致表示簇为连续块的表示形式。但是，对于群集可以重叠的双簇结果，不可能以这种方式重新排序矩阵而不复制行和/或列。我们提出了Furby，这是一种用于分析双簇结果的交互式可视化技术。我们的贡献是双重的。首先，该技术概述了两次群集结果，显示了形成单个群集的实际数据以及它们共享的排行和列的信息。其次，对于模糊的聚类结果，该提出的技术还使分析师能够交互式设置阈值，这些阈值将模糊（软）聚类转换为硬簇，然后可以使用热图或条形图进行研究。会员价值阈值的变化立即反映在可视化中。我们通过将应用于多组织数据集应用于可视化的双簇结果来证明Furby的价值。提出的工具允许分析师评估双簇结果的总体质量。基于此高级概述，分析师然后可以详细介绍各个双流体。这种处理模糊聚类结果的新型方法还支持分析师找到导致最佳簇的最佳阈值。
Cluster analysis is widely used to discover patterns in multi-dimensional data. Clustered heatmaps are the standard technique for visualizing one-way and two-way clustering results. In clustered heatmaps, rows and/or columns are reordered, resulting in a representation that shows the clusters as contiguous blocks. However, for biclustering results, where clusters can overlap, it is not possible to reorder the matrix in this way without duplicating rows and/or columns. We present Furby, an interactive visualization technique for analyzing biclustering results. Our contribution is twofold. First, the technique provides an overview of a biclustering result, showing the actual data that forms the individual clusters together with the information which rows and columns they share. Second, for fuzzy clustering results, the proposed technique additionally enables analysts to interactively set the thresholds that transform the fuzzy (soft) clustering into hard clusters that can then be investigated using heatmaps or bar charts. Changes in the membership value thresholds are immediately reflected in the visualization. We demonstrate the value of Furby by loading biclustering results applied to a multi-tissue dataset into the visualization. The proposed tool allows analysts to assess the overall quality of a biclustering result. Based on this high-level overview, analysts can then interactively explore the individual biclusters in detail. This novel way of handling fuzzy clustering results also supports analysts in finding the optimal thresholds that lead to the best clusters.
DOI：
10.1073/pnas.95.25.14863
发表时间：
1998-12-08
影响因子：
11.1
作者：
Eisen, MB;Spellman, PT;Botstein, D
通讯作者：
Botstein, D