Bridging the Gap Between Pathways and Experimental Data

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Bridging the Gap Between Pathways and Experimental Data Alexander Lex

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Bridging the Gap Between Pathways and Experimental Data. Alexander Lex. Experimental Data and Pathways. Pathways represent consensus knowledge for a healthy organism or specific disease Cannot account for variation found in real-world data Branches can be (in)activated due to - PowerPoint PPT Presentation

Transcript of Bridging the Gap Between Pathways and Experimental Data

Page 1: Bridging the  Gap Between Pathways  and  Experimental Data

Bridging the Gap Between Pathways and Experimental Data

Alexander Lex

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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

Alexander Lex | Harvard University

C

B

D

F

A

E

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Experimental Data and Pathways

[Lindroos2002]

[KEGG]

Alexander Lex | Harvard University

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Visualization Approaches

On-Node Mapping Separate Linked Views Small Multiples

Layout Adaption Linearization

[Mey

er 2

010]

[Junk

er 2

006]

[Lin

droo

s 200

2]

Alexander Lex | Harvard University Path-Extraction

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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

Alexander Lex | Harvard University

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R I: Data ScaleLarge number of experiments

Large datasets have more than 500 experiments

Multiple groups/conditions

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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

Alexander Lex | Harvard University

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R V: Supporting Multiple Tasks

Two central tasks:Explore topology of pathway

Explore the attributes of the nodes (experimental data)

Need to support both!

Alexander Lex | Harvard University

C

B

D

F

A

E

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Alexander Lex | Harvard University 13

VISUALIZATION TECHNIQUES

Teaser Picture

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Visualization Approaches

Separate Linked Views Small Multiples

Layout Adaption Linearization

[Mey

er 2

010]

[Junk

er 2

006]

Alexander Lex | Harvard University Path-Extraction

On-Node Mapping

[Lin

droo

s 200

2]

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On-Node Mapping

Alexander Lex | Harvard University [Lindroos2002]

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On-Node Mapping

Alexander Lex | Harvard University

[Westenberg 2008]

[Gehlenborg 2010]

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On-Node & Tooltip

Alexander Lex | Harvard University

[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

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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[Lin

droo

s 200

2]

On-Node Mapping

Visualization Approaches

Small Multiples

Layout Adaption Linearization

[Mey

er 2

010]

[Junk

er 2

006]

Alexander Lex | Harvard University Path-Extraction

Separate Linked Views

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Separate Linked Views

Alexander Lex | Harvard University

[Shannon 2008]

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Separate Linked Views

Alexander Lex | Harvard University

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Separate Linked Views

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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Separate Linked Views

[Lin

droo

s 200

2]

On-Node Mapping

Visualization Approaches

Layout Adaption Linearization

[Mey

er 2

010]

[Junk

er 2

006]

Alexander Lex | Harvard University Path-Extraction

Small Multiples

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Small Multiples

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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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“

Alexander Lex | Harvard University

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Separate Linked Views

[Lin

droo

s 200

2]

On-Node Mapping

Visualization Approaches

Small Multiples

Linearization

[Mey

er 2

010]

Alexander Lex | Harvard University Path-ExtractionLayout Adaption

[Junk

er 2

006]

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Layout Adaption„Moderate“ Layout Adaption

make space for on-node encoding

Alexander Lex | Harvard University

[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

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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

Alexander Lex | Harvard University

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Layout Adaption ReflectionR IV (Layout-Preservation)

not possible

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

limited for attribute-based tasks

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

Alexander Lex | Harvard University

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Layout Adaption

[Junk

er 2

006]

Separate Linked Views

[Lin

droo

s 200

2]

On-Node Mapping

Visualization Approaches

Small Multiples

Alexander Lex | Harvard University Path-ExtractionLinearization

[Mey

er 2

010]

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Linearization – Pathline

Alexander Lex | Harvard University

[Meyer 2010]

Combination oflayout adaption

separate linked views

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Linearization

Alexander Lex | Harvard University

[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

Alexander Lex | Harvard University

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Linearization ReflectionR IV (Layout-Preservation)

not possible

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

limited for attribute-based tasks

limited for combining them!

Manual creation of linearized versionUnclear if suitable for more complex pathways

Alexander Lex | Harvard University

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Visualization Approaches

On-Node Mapping Separate Linked Views Small Multiples

Layout Adaption Linearization

[Mey

er 2

010]

[Junk

er 2

006]

[Lin

droo

s 200

2]

Alexander Lex | Harvard University Path-Extraction

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CALEYDO ENROUTE

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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Experimental Data Representation

Gene Expression Data (Numerical)

Copy Number Data (Ordered Categorical)

Mutation Data

Alexander Lex | Harvard University

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enRoute View – Putting All Together

Alexander Lex | Harvard University

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Video!

Alexander Lex | Harvard University

http://enroute.caleydo.org

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Glioblastoma Multiforme Example

Alexander Lex | Harvard University

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Glioblastoma Multiforme Example

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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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

Alexander Lex | Harvard University

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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!

Alexander Lex | Harvard University

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SUMMARY & RECOMMENDATIONS

Alexander Lex | Harvard University

Teaser Picture

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Which to use?

On-Node Mapping Separate Linked Views Small Multiples

Layout Adaption Linearization

[Mey

er 2

010]

[Junk

er 2

006]

[Lin

droo

s 200

2]

Alexander Lex | Harvard University Path-Extraction

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Use Technique that fits your task

Topology is importantOne experimental condition

Alexander Lex | Harvard University

On-Node Mapping

[Lindroos2002]

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Use Technique that fits your task

Topology is importantSize of graph is limitedHandful of conditions

Alexander Lex | Harvard University

Small Multiples

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Use Technique that fits your task

Experimental data is criticalPathways are a “sideshow”

Alexander Lex | Harvard University

[Shannon 2008]

Separate Linked Views

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Use Technique that fits your task

Topology & experimental data is importantData is heterogeneous

Alexander Lex | Harvard University

Path Extraction

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What’s Nice About That?

Caleydo supports all of them ;)

Alexander Lex | Harvard University

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FUTURE CHALLENGES

Alexander Lex | Harvard University

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

Alexander Lex | Harvard University

[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

Alexander Lex | Harvard University