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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!
3 million × 3 million
HiGlass
http://higlass.io
http://higlass.io/app/?config=TKXaqsSIRvGEcw2dAUQvxg
2D Maps
Build a Hi-C Interaction Map Viewer
http://higlass.io/app/?config=TKXaqsSIRvGEcw2dAUQvxg
2D Maps
1D Tracks
Build a Genome Browser
1D Tracks
Prioritization
Orient Users in the Visualization
Prioritization
Prioritization
Prioritization
Linked Views
Support Overview and Detail
Linked Views
Linked Views++
Support Exploration and Analysis
Linked Views++
Example 1a
Schwarzer et al. Nature, 2017
Example 1a
Example 1b
Schwarzer et al. Nature, 2017
Example 1b
Many pattern instances but sparse distribution!
Many pattern instances but sparse distribution!
How can we
explore and compare
many local patterns in
this very large matrix?
HiPiler
http://hipiler.higlass.io
Challenges
• Detected by algorithms
• Occur frequently
• "Noisy" results
Goals
• Quality assessment
• Pattern stratification
• Pattern correlation
Points Blocks
• 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?
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
Cut the Matrix into Pieces!
Cut the Matrix into Pieces!
Cut the Matrix into Pieces!
Cut the Matrix into Pieces!
Cut the Matrix into Pieces!
HiPiler
HiPiler
HiPiler
HiPiler
HiPiler
1. FILTERING
Assess quality & separate signal from noise
1. FILTERING
1. FILTERING
1. FILTERING
1. FILTERING
1. FILTERING
2. AGGREGATE
Stratify patterns and assess pattern variability
2. AGGREGATE
2. AGGREGATE
2. AGGREGATE
3. CONTEXT
Correlate patterns with each another
& other pattern types
3. CONTEXT
3. CONTEXT
Pile Inspection Attribute correlations
Multidimensional Clustering Dataset Comparison
More at http://hipiler.higlass.io
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
CONCLUSION
Coordinate
Aggregate
Arrange &
Filter
Separate
Explore
HiGlass + HiPiler
LIVE

higlass.io
hipiler.higlass.io
CODE

github.com/hms-dbmi/higlass

github.com/flekschas/hipiler
I am hiring!
POSTDOCS

Data Visualization
Cancer Genomics
Chromatin Interaction + Epigenomics


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