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TRANSCRIPT
Heatmaps: A Multivariate
Visualization Method
April 5, 2017
Why use heatmaps
• Matrix visualized with colour gradients
• Visually recognize patterns in data
• Condense multiple response and • Condense multiple response and
predictor variables into one figure
• Highlight similarities and/or
differences between predictor and
response variables
History of heatmaps
Creating heatmaps in R
1. Create data matrix.
2. Scale the data.
3. Create distance values – dist().
o euclidean, maximum, manhattan, canberra, binary or minkowski
4. Cluster the values by creating a dendrogram – hclust().4. Cluster the values by creating a dendrogram – hclust().
o ward.D, ward.D2, single, complete, average, mcquitty, median, centroid
• Heatmap -> heatmap.2
• Aheatmap -> pheatmap
• Heatmap3
• Heatmaps in ggplot2 (not as good)
References
Wilkinson, L. and M. Friendly. 2009. The History of the Cluster Heat Map. The
American Statistician 63(2):179-184. DOI: 10.1198/tas.2009.0033.