HiPiler: Visual Exploration Of Large Genome Interaction Matrices With Interactive Small Multiples

From my talk at InfoVis at IEEE VIS 2017 in Phoenix.

Transcript

1. HiPiler Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples Fritz Lekschas, Benjamin Bach, Peter Kerpedjiev, Nils Gehlenborg, and Hanspeter Pfister
2. 3 million × 3 million
3. > 10.000 pattern instances but small total size
4. > 10.000 pattern instances but small total size How can we explore and compare many local patterns in this very large matrix?
5. HiPiler
6. Social Networks Fans, connectors, and cliques Computer Networks Bottlenecks and hubs Gene Networks Feed-forward loops Giga-pixel Images Pattern recognition
7. Structure of the Genome Acknowledgements: N. Abdennur, B. Alver, H. Belaghzal, A. van den Berg, J. Dekker, G. Fudenberg, J. Gibcus, A. Goloborodko, D. Gorkin, M. Imakaev, Y. Liu, L. Mirny, J. Nübler, P. Park, H. Strobelt, and S. Wang.
8. DNA Cell Nucleus Contact Sequencing
9. Cell Nucleus Contact Sequencing Matrix Fixed Ordering Altered DNA ordering is associated with severe diseases!
10. Challenges • Detected by algorithms • Occur frequently • "Noisy" results Goals • Quality assessment • Pattern stratification • Pattern correlation Points Blocks
11. • How do specific pattern or average pattern look? • How variant and noisy are detected pattern? • Are there subgroups among the pattern? • How are patterns related to other data attributes? • What does the patterns neighborhood look like?
12. TECHNIQUES? • Pan & Zoom
 Kerpedjiev et al.: HiGlass • Lenses / Multifocus
 Rao and Card: Table Lense 
 Elmquist et al.: Melange • Abstraction / Aggregation
 Dunne et al.: Motif Simplification 
 Elmquist et al.: ZAME • Small Multiples
 Bach et al.: Multipiles
13. Cut the Matrix into Pieces!
14. Cut the Matrix into Pieces!
15. Cut the Matrix into Pieces!
16. Cut the Matrix into Pieces!
17. Cut the Matrix into Pieces!
18. Cut the Matrix into Pieces!
19. HiPiler
20. HiPiler
21. 1. FILTERING Assess quality & separate signal from noise
22. 1. FILTERING
23. 1. FILTERING
24. 1. FILTERING
25. 1. FILTERING
26. 1. FILTERING
27. 2. AGGREGATE Stratify patterns and assess pattern variability
28. 2. AGGREGATE
29. 2. AGGREGATE
30. 3. CONTEXT Correlate patterns with each another & other pattern types
31. 3. CONTEXT
32. Pile Inspection Attribute correlations Multidimensional Clustering Dataset Comparison More at http:/ /vcg.seas.harvard.edu/pubs/hipiler
33. User study with 5 domain experts: Evaluating usability and usefulness Snippet approach is useful: Average / variance assessment and parameter estimation Context matters: Coordination between the snippets and matrix is highly appreciated HiPiler is easy-to-use and useful: Domain experts ask for local installations Limitations: Fixed matrix ordering and fixed aspect ratio of snippets EVALUATION
34. CONCLUSION Coordinate Aggregate Arrange & Filter Separate Explore
35. NEXT?
36. HiPiler PAPER 
 vcg.seas.harvard.edu/pubs/hipiler LIVE 
 hipiler.higlass.io CODE 
 github.com/flekschas/hipiler