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Related Concept Videos

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Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a higher temperature. When the...
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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Unboxing cluster heatmaps.

Sophie Engle1, Sean Whalen2, Alark Joshi3

  • 1University of San Francisco, San Francisco, 94117, CA, USA. sjengle@usfca.edu.

BMC Bioinformatics
|March 3, 2017
PubMed
Summary
This summary is machine-generated.

Cluster heatmaps are common but flawed for data visualization. New methods, like gapmaps, offer improved performance for clustering tasks by relaxing rigid grid constraints, enhancing data analysis in biology.

Keywords:
Bioinformatics visualizationData clusteringHierarchy dataQualitative evaluationQuantitative evaluationSystems biology/omics data

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Area of Science:

  • Bioinformatics
  • Data Visualization
  • Computational Biology

Background:

  • Cluster heatmaps are widely used in biological sciences for visualizing hierarchical data clusters.
  • Despite their utility, cluster heatmaps present challenges in usability and accuracy.
  • Alternative visualization methods are explored to overcome these limitations.

Purpose of the Study:

  • To investigate visualization techniques that overcome the limitations of cluster heatmaps.
  • To develop and evaluate alternatives to traditional cluster heatmaps for clustering-related tasks.

Main Methods:

  • Developed an approach to embed heatmap data within hierarchical clustering visualizations.
  • Conducted a user study with 45 practitioners to understand current heatmap usage.
  • Prototyped and evaluated alternative visualizations through interviews and a large-scale online study (approx. 200 participants).

Main Results:

  • Statistically significant performance differences were observed across various clustering tasks and perceived visual clusters.
  • The study identified specific tasks where alternative methods outperformed cluster heatmaps.
  • Gapmaps emerged as a preferred alternative among interviewed practitioners.

Conclusions:

  • Gapmaps, which introduce gaps to relax heatmap grid constraints, are a viable alternative to cluster heatmaps.
  • Gapmaps performed comparably or better than cluster heatmaps for clustering-related tasks.
  • The study recommends adopting gapmaps for improved data analysis and visualization in relevant scientific fields.