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Interactive Exploration of Hierarchical Clustering Results HCE (Hierarchical Clustering Explorer). Jinwook Seo and Ben Shneiderman Human-Computer Interaction Lab Department of Computer Science University of Maryland, College Park jinwook@cs.umd.edu.

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interactive exploration of hierarchical clustering results hce hierarchical clustering explorer

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@cs.umd.edu

cluster analysis of microarray experiment data
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.
dendrogram
Dendrogram

-3.64

4.87

dendrogram1
Dendrogram

-3.64

4.87

dendrogram2
Dendrogram

-3.64

4.87

interactive exploration techniques
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
demonstration
Demonstration
  • Nutrition facts of 77 cereals
  • 9 variables (nutrition components)
  • More demonstration
    • A.V. Williams Bldg, 3174
    • 3:30-5:00pm, May 31.
  • Download HCE at
    • www.cs.umd.edu/hcil/multi-cluster
dynamic query controls
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
dynamic query controls1
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
coordinated displays
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
overview in a limited screen space
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)

overview in a limited screen space1
Overview in a limited screen space
  • What if there are more than 1,600 items to display?
  • Alternative Overview : changing bar width (2~10)
  • Show more detail, but need scrolling
cluster comparison
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
conclusion
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

Initial Data Items

Distance Matrix

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Hierarchical Clustering

Initial Data Items

Distance Matrix

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Hierarchical Clustering

Single Linkage

Current Clusters

Distance Matrix

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Hierarchical Clustering

Single Linkage

Current Clusters

Distance Matrix

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Hierarchical Clustering

Single Linkage

Current Clusters

Distance Matrix

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Hierarchical Clustering

Single Linkage

Current Clusters

Distance Matrix

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Hierarchical Clustering

Single Linkage

Current Clusters

Distance Matrix

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Hierarchical Clustering

Single Linkage

Current Clusters

Distance Matrix

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Hierarchical Clustering

Single Linkage

Current Clusters

Distance Matrix

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Hierarchical Clustering

Single Linkage

Final Result

Distance Matrix