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HiGlass + HiPiler: Making Sense of Chromosome Interaction Data with Multi-Scale Data Visualization Tools

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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.)

Published in: Science
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HiGlass + HiPiler: Making Sense of Chromosome Interaction Data with Multi-Scale Data Visualization Tools

  1. 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. 2. 3 million × 3 million
  3. 3. HiGlass http://higlass.io
  4. 4. http://higlass.io/app/?config=TKXaqsSIRvGEcw2dAUQvxg 2D Maps Build a Hi-C Interaction Map Viewer
  5. 5. http://higlass.io/app/?config=TKXaqsSIRvGEcw2dAUQvxg 2D Maps
  6. 6. 1D Tracks Build a Genome Browser
  7. 7. 1D Tracks
  8. 8. Prioritization Orient Users in the Visualization
  9. 9. Prioritization
  10. 10. Prioritization
  11. 11. Prioritization
  12. 12. Linked Views Support Overview and Detail
  13. 13. Linked Views
  14. 14. Linked Views++ Support Exploration and Analysis
  15. 15. Linked Views++
  16. 16. Example 1a Schwarzer et al. Nature, 2017
  17. 17. Example 1a
  18. 18. Example 1b Schwarzer et al. Nature, 2017
  19. 19. Example 1b
  20. 20. Many pattern instances but sparse distribution!
  21. 21. Many pattern instances but sparse distribution! How can we explore and compare many local patterns in this very large matrix?
  22. 22. HiPiler http://hipiler.higlass.io
  23. 23. Challenges • Detected by algorithms • Occur frequently • "Noisy" results Goals • Quality assessment • Pattern stratification • Pattern correlation Points Blocks
  24. 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. 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. 26. Cut the Matrix into Pieces!
  27. 27. Cut the Matrix into Pieces!
  28. 28. Cut the Matrix into Pieces!
  29. 29. Cut the Matrix into Pieces!
  30. 30. Cut the Matrix into Pieces!
  31. 31. HiPiler
  32. 32. HiPiler
  33. 33. HiPiler
  34. 34. HiPiler
  35. 35. HiPiler
  36. 36. 1. FILTERING Assess quality & separate signal from noise
  37. 37. 1. FILTERING
  38. 38. 1. FILTERING
  39. 39. 1. FILTERING
  40. 40. 1. FILTERING
  41. 41. 1. FILTERING
  42. 42. 2. AGGREGATE Stratify patterns and assess pattern variability
  43. 43. 2. AGGREGATE
  44. 44. 2. AGGREGATE
  45. 45. 2. AGGREGATE
  46. 46. 3. CONTEXT Correlate patterns with each another & other pattern types
  47. 47. 3. CONTEXT
  48. 48. 3. CONTEXT
  49. 49. Pile Inspection Attribute correlations Multidimensional Clustering Dataset Comparison More at http://hipiler.higlass.io
  50. 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. 51. CONCLUSION Coordinate Aggregate Arrange & Filter Separate Explore
  52. 52. HiGlass + HiPiler LIVE
 higlass.io hipiler.higlass.io CODE
 github.com/hms-dbmi/higlass
 github.com/flekschas/hipiler
  53. 53. I am hiring! POSTDOCS
 Data Visualization Cancer Genomics Chromatin Interaction + Epigenomics


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