title: Furby: fuzzy force-directed bicluster visualization.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors: Marc
authors: M
authors: Streit
authors: Samuel
authors: S
authors: Gratzl
authors: Michael
authors: M
authors: Gillhofer
authors: Andreas
authors: A
authors: Mayr
authors: Andreas
authors: A
authors: Mitterecker
authors: Sepp
authors: S
authors: Hochreiter
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000295:instrumentation*; D057225:Data Mining; D020407:Internet; D020411:Oligonucleotide Array Sequence Analysis