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

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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 [email protected]

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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 [email protected]. - PowerPoint PPT Presentation

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Page 1: Interactive Exploration of Hierarchical Clustering Results HCE (Hierarchical Clustering Explorer)

Interactive Exploration of Hierarchical Clustering Results

HCE (Hierarchical Clustering Explorer)

Jinwook Seo and Ben ShneidermanHuman-Computer Interaction Lab

Department of Computer Science

University of Maryland, College Park

[email protected]

Page 2: Interactive Exploration of Hierarchical Clustering Results HCE (Hierarchical Clustering Explorer)

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– 3:30-5:00pm, May 31.

• Download HCE at– www.cs.umd.edu/hcil/multi-cluster

http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
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Dynamic Query ControlsFilter 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

Page 9: Interactive Exploration of Hierarchical Clustering Results HCE (Hierarchical Clustering Explorer)

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)

http://www.cs.umd.edu/hcil/multi-cluster/large-image/c-overview.tif
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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 (2~10)• Show more detail, but need scrolling

http://www.cs.umd.edu/hcil/multi-cluster/large-image/c-overview.tif
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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-clusterJuly 2002 IEEE Computer Special Issue on BioInformatics

http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
http://www.cs.umd.edu/hcil/multi-cluster
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A B C D

Dist A B C D

A 20 7 2

B 10 25

C 3

D

Distance MatrixInitial Data Items

Hierarchical Clustering

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A B C D

Dist A B C D

A 20 7 2

B 10 25

C 3

D

Distance MatrixInitial Data Items

Hierarchical Clustering

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Current Clusters

Single Linkage

Hierarchical Clustering

Dist A B C D

A 20 7 2

B 10 25

C 3

D

Distance Matrix

A B CD

2

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Dist AD B C

AD 20 3

B 10

C

Distance MatrixCurrent Clusters

Single Linkage

Hierarchical Clustering

A B CD

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A B CD

Dist AD B C

AD 20 3

B 10

C

Distance MatrixCurrent Clusters

Single Linkage

Hierarchical Clustering

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Dist AD B C

AD 20 3

B 10

C

Distance MatrixCurrent Clusters

Single Linkage

Hierarchical Clustering

A BCD

3

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Dist ADC B

ADC

10

B

Distance MatrixCurrent Clusters

Single Linkage

Hierarchical Clustering

A BCD

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A BCD

Dist ADC B

ADC

10

B

Distance MatrixCurrent Clusters

Single Linkage

Hierarchical Clustering

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Dist ADC B

ADC

10

B

Distance MatrixCurrent Clusters

Single Linkage

Hierarchical Clustering

A BCD

10

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A BCD

Dist ADCB

ADCB

Distance MatrixFinal Result

Single Linkage

Hierarchical Clustering


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