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
   

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2. 3 million × 3 million
   

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3. > 10.000 pattern instances but small total size
   

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4. > 10.000 pattern instances but small total size
   How can we
   explore and compare
   many local patterns in
   this very large matrix?
   

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5. HiPiler
   

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6. Social Networks
   Fans, connectors, and cliques
   Computer Networks
   Bottlenecks and hubs
   Gene Networks
   Feed-forward loops
   Giga-pixel Images
   Pattern recognition
   

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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.
   

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8. DNA Cell Nucleus Contact Sequencing
   

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9. Cell Nucleus Contact Sequencing Matrix
   Fixed Ordering
   Altered DNA ordering is associated with severe diseases!
   

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

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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?
    

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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
    

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13. Cut the Matrix into Pieces!
    

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14. Cut the Matrix into Pieces!
    

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15. Cut the Matrix into Pieces!
    

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16. Cut the Matrix into Pieces!
    

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17. Cut the Matrix into Pieces!
    

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18. Cut the Matrix into Pieces!
    

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19. HiPiler
    

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20. HiPiler
    

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

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22. 1. FILTERING
    

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23. 1. FILTERING
    

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24. 1. FILTERING
    

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25. 1. FILTERING
    

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26. 1. FILTERING
    

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27. 2. AGGREGATE
    Stratify patterns and assess pattern variability
    

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28. 2. AGGREGATE
    

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29. 2. AGGREGATE
    

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

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31. 3. CONTEXT
    

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32. Pile Inspection Attribute correlations
    Multidimensional Clustering Dataset Comparison
    More at http:/
    /vcg.seas.harvard.edu/pubs/hipiler
    

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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
    

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34. CONCLUSION
    Coordinate
    Aggregate
    Arrange &
    Filter
    Separate
    Explore
    

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35. NEXT?
    

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36. HiPiler
    PAPER
    

    vcg.seas.harvard.edu/pubs/hipiler
    LIVE
    

    hipiler.higlass.io
    CODE
    

    github.com/flekschas/hipiler
    

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