1. 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

2. 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.

3. Dendrogram -3.644.87

4. Dendrogram -3.644.87

5. Dendrogram -3.644.87

6. 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

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

8. 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

9. 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

10. 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

11. 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)

12. 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

13. 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

14. Cluster Comparison

15. 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

16. ABCD Dist ABCD A2072 B1025 C3 D Distance MatrixInitial Data Items Hierarchical Clustering

17. ABCD Dist ABCD A2072 B1025 C3 D Distance MatrixInitial Data Items Hierarchical Clustering

18. Current Clusters Single Linkage Hierarchical Clustering Dist ABCD A2072 B1025 C3 D Distance Matrix ABCD 2

19. Dist ADBC 203 B10 C Distance MatrixCurrent Clusters Single Linkage Hierarchical Clustering ABCD

20. ABCD Dist ADBC 203 B10 C Distance MatrixCurrent Clusters Single Linkage Hierarchical Clustering

21. Dist ADBC 203 B10 C Distance MatrixCurrent Clusters Single Linkage Hierarchical Clustering ABCD 3

22. Dist AD C B 10 B Distance MatrixCurrent Clusters Single Linkage Hierarchical Clustering ABCD

23. ABCD Dist AD C B 10 B Distance MatrixCurrent Clusters Single Linkage Hierarchical Clustering

24. Dist AD C B 10 B Distance MatrixCurrent Clusters Single Linkage Hierarchical Clustering ABCD 10

25. ABCD Dist AD CB Distance MatrixFinal Result Single Linkage Hierarchical Clustering