title: Unboxing cluster heatmaps.
abstract: BACKGROUND
  Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks.
  
  
  RESULTS
  We developed an approach to "unbox" the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit git.io/vw0t3 for our results.
  
  
  CONCLUSIONS
  The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps.
authors.affiliation: University of San Francisco, San Francisco, 94117, CA, USA. sjengle@usfca.edu.
authors.forename: Sophie
authors.initials: S
authors.lastname: Engle
authors.affiliation: Gladstone Institutes, San Francisco, 94158, CA, USA.
authors.forename: Sean
authors.initials: S
authors.lastname: Whalen
authors.affiliation: University of San Francisco, San Francisco, 94117, CA, USA.
authors.forename: Alark
authors.initials: A
authors.lastname: Joshi
authors.affiliation: Gladstone Institutes, San Francisco, 94158, CA, USA.|Division of Biostatistics, Institute for Human Genetics, and Institute for Computational Health Sciences, University of California, San Francisco, 94158, CA, USA.
authors.forename: Katherine S
authors.initials: KS
authors.lastname: Pollard
mesh_terms: D045744:Cell Line, Tumor; D016000:Cluster Analysis; D019295:Computational Biology; D006801:Humans; D020014:K562 Cells
keywords: Bioinformatics visualization; Data clustering; Hierarchy data; Qualitative evaluation; Quantitative evaluation; Systems biology/omics data