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

090f9e164337989b54dabd7fedfd39b4?s=47 Fritz Lekschas
October 05, 2017

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

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

090f9e164337989b54dabd7fedfd39b4?s=128

Fritz Lekschas

October 05, 2017
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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