Bridging the Gap Between Pathways and Experimental Data

Bridging the Gap Between Pathways and Experimental Data

Alexander Lex

2

Experimental Data and Pathways

Pathways represent consensus knowledge for a healthy organism or specific diseaseCannot account for variation found in real-world dataBranches can be (in)activated due to

mutation,

changed gene expression,

modulation due to drug treatment,

etc.

Alexander Lex | Harvard University

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Why use Visualization?Efficient communication of information

A -3.4B 2.8C 3.1

D -3E 0.5F 0.3


C

B

D

F

A

E

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Experimental Data and Pathways

[Lindroos2002]

[KEGG]


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Visualization Approaches

On-Node Mapping Separate Linked Views Small Multiples

Layout Adaption Linearization

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Alexander Lex | Harvard University Path-Extraction

Alexander Lex | Harvard University 6

REQUIREMENTS ANALYSIS

Teaser Picture

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What to Consider when Visualizing Experimental Data and Pathways

Conflicting GoalsPreserving topology of pathways

Showing lots of experimental data

Five RequirementsIdeal visualization technique addresses all


8

R I: Data ScaleLarge number of experiments

Large datasets have more than 500 experiments

Multiple groups/conditions


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R II: Data HeterogeneityDifferent types of data, e.g.,

mRNA expression numerical

mutation statuscategorical

copy number variation ordered categorical

metabolite concentration numerical

Require different visualization techniques


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R III: Multi-MappingPathways nodes are biomolecules

Proteins, nucleic acids, lipids, metabolites

Experimental data often on a „gene“ level

Multiple genes can produce protein

Multiple genes encode one protein

Result: many „gene“ values map to one pathway node


C

EE1E2E3E4

CA3KJ2RAF

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R IV: Preserving the Layout Pathways are available in carefully designed layouts

e.g., KEGG, WikiPathways, Biocarta

Users are familiar with layoutsGoal: preserve layouts as much as possibleTwo approaches:

Emulate drawing conventions

Use original layouts


12

R V: Supporting Multiple Tasks

Two central tasks:Explore topology of pathway

Explore the attributes of the nodes (experimental data)

Need to support both!


C

B

D

F

A

E

Alexander Lex | Harvard University 13

VISUALIZATION TECHNIQUES

Teaser Picture

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Separate Linked Views Small Multiples


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On-Node Mapping

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On-Node Mapping

Alexander Lex | Harvard University [Lindroos2002]

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On-Node Mapping


[Westenberg 2008]

[Gehlenborg 2010]

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On-Node & Tooltip


[Streit 2008]

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On-Node MappingNot scalable

especially when used with „original“ layout

animation not an alternative

Good for overview with homogeneous data

Excellent for topology-based tasksBad for attribute-based tasks


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On-Node Mapping ReflectionR I (Scale)

bad if working with static layouts

limited when working with layout adaption

R II (Heterogeneity)bad – can‘t encode multiple datasets

R III (Multi-Mapping)bad – can‘t encode multiple mappings


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On-Node Mapping ReflectionR IV (Layout-Preservation)

excellent!

R V (Multiple Tasks)excellent for topology-based tasks

bad for attribute-based tasks


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[Lin

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On-Node Mapping


Small Multiples


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Separate Linked Views

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[Shannon 2008]

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24



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Separate Linked Views Reflection

R I (Scale)excellent for large numbers of attributes

R II (Heterogeneity)excellent for heterogeneous data

e.g., one view per data type

R III (Multi-Mapping)good – simple highlighting for multiple elements


26

Separate Linked Views Reflection

R IV (Layout-Preservation)excellent!

R V (Multiple Tasks)good for topology-based tasks

good for attribute-based tasks

awful for combining them!Association node-attribute only one by one


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On-Node Mapping



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Small Multiples

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Small Multiples


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Small Multiples

Alexander Lex | Harvard University [Barsky 2008]

Video!

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Small Multiples ReflectionR I (Scale)

limited to a handful of conditions/experiments

differences don‘t „pop out“

R II (Heterogeneity)limited for heterogeneous data

e.g., one view per data type

R III (Multi-Mapping)bad – no obvious solution


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Small Multiples ReflectionR IV (Layout-Preservation)

excellent!

R V (Multiple Tasks)good for topology-based tasks

limited for attribute-based tasks

limited for combining them!comparing one by one -> change blindness

Typically requires „focus duplicate“


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[Lin

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On-Node Mapping


Small Multiples

Linearization

[Mey

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Alexander Lex | Harvard University Path-ExtractionLayout Adaption

[Junk

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Layout Adaption„Moderate“ Layout Adaption

make space for on-node encoding


[Gehlenborg 2010][Junker 2006]

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Layout Adaption„Extreme“ layout adaption

encode information throughposition

Alexander Lex | Harvard University [Bezerianos 2010]

Video: http://www.youtube.com/watch?v=NLiHw5B0Mco

http://www.youtube.com/watch?v=NLiHw5B0Mco



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Layout Adaption ReflectionR I (Scale)

limited to a handful of conditions/experiments

R II (Heterogeneity)limited for heterogeneous data

Different story for „extreme“ layout adaptionR III (Multi-Mapping)

OK– give nodes with multi-mappings extra space


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Layout Adaption ReflectionR IV (Layout-Preservation)

not possible

R V (Multiple Tasks)limited for topology-based tasks


limited for combining them!space for trade-off between topology and attribute tasks


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Layout Adaption

[Junk

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On-Node Mapping


Small Multiples

Alexander Lex | Harvard University Path-ExtractionLinearization

[Mey

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Linearization – Pathline


[Meyer 2010]

Combination oflayout adaption

separate linked views

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Linearization


[Meyer 2010]

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Linearization ReflectionR I (Scale)

good for many experiments

R II (Heterogeneity)good for multiple datasets

R III (Multi-Mapping)good – give nodes with multi-mappings extra space


41

Linearization ReflectionR IV (Layout-Preservation)

not possible

R V (Multiple Tasks)limited for topology-based tasks


limited for combining them!

Manual creation of linearized versionUnclear if suitable for more complex pathways


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CALEYDO ENROUTE


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Pathway View

A

E

C

B

D

F

Pathway View

C

B

D

F

A

E

enRoute View

Concept

Group 1Dataset 1

Group 2Dataset 1

Group 1Dataset 2

B

C

F

A

D

E


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Pathway View

On-Node MappingPath highlighting with Bubble Sets [Collins2009]

SelectionStart- and end node

Iterative adding of nodes

IGF-1

low high


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enRoute View – Path Representation

• Design of KEGG [Kanehisa2008]

• Abstract branch nodes– Additional topological

information– Incoming vs. outgoing

branches– Expandable

• Branch switching


47

Experimental Data Representation

Gene Expression Data (Numerical)

Copy Number Data (Ordered Categorical)

Mutation Data


48

enRoute View – Putting All Together


49

Video!


http://enroute.caleydo.org

http://enroute.caleydo.org/

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Glioblastoma Multiforme Example


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Glioblastoma Multiforme Example


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enRoute ReflectionR I (Scale)

Excellent, can handle large amounts of data

R II (Heterogeneity)Excellent, can handle various datasets

R III (Multi-Mapping)Excellent, can resolve multi-mappings without ambiguity


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enRoute ReflectionR IV (Layout-Preservation)

Excellent - preserves pathway layout

Not preserved in extracted path

R V (Multiple Tasks)Good for topology-based tasks

High-level topology through pathway view

Topology of path in enRoute view

Excellent for attribute-based tasksCan handle large, grouped and heterogeneous data


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Using enRouteenRoute part of Caleydo Biomolecular Visualization Framework

http://caleydo.org

Caleydo is free for all – open source project

More in Marc‘s talk!


http://caleydo.org/

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SUMMARY & RECOMMENDATIONS


Teaser Picture

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Which to use?



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Use Technique that fits your task

Topology is importantOne experimental condition


On-Node Mapping

[Lindroos2002]

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Topology is importantSize of graph is limitedHandful of conditions


Small Multiples

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Experimental data is criticalPathways are a “sideshow”


[Shannon 2008]


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Topology & experimental data is importantData is heterogeneous


Path Extraction

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What’s Nice About That?

Caleydo supports all of them ;)


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FUTURE CHALLENGES


Teaser Picture

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Other Pathway-Related Challenges

Cross-connections between pathways

Alexander Lex | Harvard University [Klukas 2007]

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Other Pathway-Related Challenges

Effect of compounds (medication) on pathways


[Lounkine 2012]

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Bridging the Gap Between Pathways and Experimental Data

Alexander Lex, Harvard [email protected]://caleydo.org

?Marc Streit

Hans-Jörg Schulz Christian Partl

Dieter SchmalstiegPeter J. Park

Nils Gehlenborg


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Bridging the Gap Between Pathways and Experimental Data

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