How can we visualize a 3,000,000 x 3,000,000 cell matrix and allow analysts to explore features across a wide range of different scales? We built HiGlass, a web-based visualization tool for analysis of Hi-C and other genome-wide chromosome interaction data that enables comparison of multiple contact matrices and integration of other data types. To complement this functionality, we also created HiPiler, which enables investigators to view and explore thousands of features such as loops or TADs and correlate their appearance with their genomic locations and experimental conditions. In my talk, I will discuss the design of HiGlass and HiPiler and present a range of use cases for these applications. (Thanks to Fritz Lekschas for providing many of the slides.)

1. HiGlass + HiPiler
Making Sense of Chromosome
Interaction Data with Multi-Scale
Data Visualization Tools
Nils Gehlenborg / Harvard Medical School
Web: http://gehlenborglab.org / Twitter: @ngehlenborg
With Peter Kerpedjiev, Fritz Lekschas, Nezar Abdennur,
Hanspeter Pfister, Leonid Mirny, Peter J Park, and more!

2. 3 million × 3 million

3. HiGlass
http://higlass.io

4. http://higlass.io/app/?config=TKXaqsSIRvGEcw2dAUQvxg
2D Maps
Build a Hi-C Interaction Map Viewer

5. http://higlass.io/app/?config=TKXaqsSIRvGEcw2dAUQvxg
2D Maps

6. 1D Tracks
Build a Genome Browser

7. 1D Tracks

8. Prioritization
Orient Users in the Visualization

9. Prioritization

10. Prioritization

11. Prioritization

12. Linked Views
Support Overview and Detail

13. Linked Views

14. Linked Views++
Support Exploration and Analysis

15. Linked Views++

16. Example 1a
Schwarzer et al. Nature, 2017

17. Example 1a

18. Example 1b
Schwarzer et al. Nature, 2017

19. Example 1b

20. Many pattern instances but sparse distribution!

21. Many pattern instances but sparse distribution!
How can we
explore and compare
many local patterns in
this very large matrix?

22. HiPiler
http://hipiler.higlass.io

23. Challenges
• Detected by algorithms
• Occur frequently
• "Noisy" results
Goals
• Quality assessment
• Pattern stratification
• Pattern correlation
Points Blocks

24. • How do specific pattern or
average pattern look?
• How variable and noisy are
detected patterns?
• Are there subgroups among
the pattern?
• How are patterns related to
other data attributes?
• What does the patterns
neighborhood look like?

25. TECHNIQUES?
• Pan & Zoom
Kerpedjiev et al.: HiGlass
• Lenses / Multifocus
Rao and Card: Table Lens
Elmquist et al.: Melange
• Abstraction / Aggregation
Dunne et al.: Motif Simplification
Elmquist et al.: ZAME
• Small Multiples
Bach et al.: Multipiles

26. Cut the Matrix into Pieces!

27. Cut the Matrix into Pieces!

28. Cut the Matrix into Pieces!

29. Cut the Matrix into Pieces!

30. Cut the Matrix into Pieces!

31. HiPiler

32. HiPiler

33. HiPiler

34. HiPiler

35. HiPiler

36. 1. FILTERING
Assess quality & separate signal from noise

37. 1. FILTERING

38. 1. FILTERING

39. 1. FILTERING

40. 1. FILTERING

41. 1. FILTERING

42. 2. AGGREGATE
Stratify patterns and assess pattern variability

43. 2. AGGREGATE

44. 2. AGGREGATE

45. 2. AGGREGATE

46. 3. CONTEXT
Correlate patterns with each another
& other pattern types

47. 3. CONTEXT

48. 3. CONTEXT

49. Pile Inspection Attribute correlations
Multidimensional Clustering Dataset Comparison
More at http://hipiler.higlass.io

50. 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

51. CONCLUSION
Coordinate
Aggregate
Arrange &
Filter
Separate
Explore

52. HiGlass + HiPiler
LIVE
higlass.io
hipiler.higlass.io
CODE
github.com/hms-dbmi/higlass
github.com/flekschas/hipiler

53. I am hiring!
POSTDOCS
Data Visualization
Cancer Genomics
Chromatin Interaction + Epigenomics