Interactive Exploration of Hierarchical Clustering Results HCE Hierarchical

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

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 -3. 64 4. 87

Dendrogram -3. 64 4. 87

Dendrogram -3. 64 4. 87

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

Demonstration • 99 Yeast genes • 7 variables (time points) • Download HCE at – www. cs. umd. edu/hcil/multi-cluster • More demonstration – A. V. Williams Bldg, 3174 – 3: 30 -5: 00 pm, May 31.

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 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 • 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 • 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 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 • • 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

Cluster Comparison

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 Bio. Informatics

Hierarchical Clustering Initial Data Items Distance Matrix Dist A B C D D A B C D 20 7 2 10 25 3

Hierarchical Clustering Initial Data Items Distance Matrix Dist A B C D D A B C D 20 7 2 10 25 3

Hierarchical Clustering Single Linkage Current Clusters Distance Matrix Dist A B C 2 A D B C D A B C D 20 7 2 10 25 3

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

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

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

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

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

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

Hierarchical Clustering Single Linkage Final Result Distance Matrix Dist ADC B A D C B