Interactive Exploration of Hierarchical Clustering Results HCE (Hierarchical Clustering Explorer)

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Interactive Exploration of Hierarchical
Clustering ResultsHCE (Hierarchical Clustering
Explorer)

• Jinwook Seo and Ben Shneiderman
• Human-Computer Interaction Lab
• Department of Computer Science
• University of Maryland, College Park
• jinwook_at_cs.umd.edu

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Cluster Analysis of Microarray Experiment Data

• About 100 20,000 gene samples
• Under 2 80 experimental conditions
• Identify similar gene samples
• startup point for studying unknown genes
• Identify similar experimental conditions
• develop a better treatment for a special group
• Clustering algorithms
• Hierarchical, K-means, etc.

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Dendrogram
-3.64
4.87
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Dendrogram
-3.64
4.87
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Dendrogram
-3.64
4.87
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Interactive Exploration Techniques

• Dynamic Query Controls
• Number of clusters, Level of detail
• Coordinated Display
• Bi-directional interaction with 2D scattergrams
• Overview of the entire dataset
• Coupled with detail view
• Visual Comparison of Different Results
• Different results by different methods

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Demonstration

• Nutrition facts of 77 cereals
• 9 variables (nutrition components)
• More demonstration
• A.V. Williams Bldg, 3174
• 330-500pm, May 31.
• Download HCE at
• www.cs.umd.edu/hcil/multi-cluster

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Dynamic Query Controls

• Filter out less similar genes
• By pulling down the minimum similarity bar
• Show only the clusters that satisfy the minimum
  similarity threshold
• Help users determine the proper number of
  clusters
• Easy to find the most similar genes

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Dynamic Query Controls

• Adjust level of detail
• By dragging up the detail cutoff bar
• Show the representative pattern of each cluster
• Hide detail below the bar
• Easy to view global structure

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Coordinated Displays

• Two experimental conditions for the x and y axes
• Two-dimensional scattergrams
• limited to two variables at a time
• readily understood by most users
• users can concentrate on the data without
  distraction
• Bi-directional interactions between displays

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Overview in a limited screen space

• What if there are more than 1,600 items to
  display?
• Compressed Overview averaging adjacent leaves
• Easy to locate interesting spots

Melanoma Microarray Experiment (3614 x 38)
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Overview in a limited screen space

• What if there are more than 1,600 items to
  display?
• Alternative Overview changing bar width (210)
• Show more detail, but need scrolling

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Cluster Comparison

• There is no perfect clustering algorithm!
• Different Distance Measures
• Different Linkage Methods
• Two dendrograms at the same time
• Show the mapping of each gene between the two
  dendrograms
• Busy screen with crossing lines
• Easy to see anomalies

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Cluster Comparison
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Conclusion

• Integrate four features to interactively explore
  clustering results to gain a stronger
  understanding of the significance of the clusters
• Overview, Dynamic Query, Coordination, Cluster
  Comparison
• Powerful algorithms Interactive tools
• Bioinformatics Visualization

www.cs.umd.edu/hcil/multi-cluster July 2002 IEEE
Computer Special Issue on BioInformatics
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Hierarchical Clustering
Distance Matrix
Initial Data Items
Dist A B C D
A 20 7 2
B 10 25
C 3
D
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Hierarchical Clustering
Distance Matrix
Initial Data Items
Dist A B C D
A 20 7 2
B 10 25
C 3
D
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Hierarchical Clustering
Single Linkage
Current Clusters
Distance Matrix
Dist A B C D
A 20 7 2
B 10 25
C 3
D
2
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Hierarchical Clustering
Single Linkage
Distance Matrix
Current Clusters
Dist AD B C
AD 20 3
B 10
C

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Hierarchical Clustering
Single Linkage
Distance Matrix
Current Clusters
Dist AD B C
AD 20 3
B 10
C

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Hierarchical Clustering
Single Linkage
Distance Matrix
Current Clusters
Dist AD B C
AD 20 3
B 10
C

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Hierarchical Clustering
Single Linkage
Distance Matrix
Current Clusters
Dist ADC B
ADC 10
B


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Hierarchical Clustering
Single Linkage
Distance Matrix
Current Clusters
Dist ADC B
ADC 10
B


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Hierarchical Clustering
Single Linkage
Distance Matrix
Current Clusters
Dist ADC B
ADC 10
B


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Hierarchical Clustering
Single Linkage
Distance Matrix
Final Result
Dist ADCB
ADCB