Abstract
Biclustering algorithms applied in classification of genomic data have two main theoretical differences compared to traditional clustering ones. First, it provides bi-dimensionality, grouping both genes and conditions together, since a group of genes can be co-regulated for a given condition but not for others. Second, it considers group overlaps, allowing genes to contribute to more than one activity. Visualizing biclustering results is a non-trivial process due to these two characteristics. Heatmaps-based techniques are considered as a standard for visualizing clustering results. They consist on reordering rows and/or columns in order to show clusters as contiguous blocks. However, for biclustering results, this same process cannot be applied without duplicating rows and/or columns. Moreover, a variety of techniques for visualizing sets and their relations has been published in the past recent years. Some of them can be considered as an ideal solution to visualize large sets with high number of possible relations between them. In this paper, we firstly review several set-visualizing techniques that we consider most suitable to satisfy the two mentioned features of biclustering and then, we discuss how these new techniques can visualize biclustering results.