Bridging the Gap Between Pathways and Experimental Data Alexander Lex

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 disease

Cannot account for variation found in real-world data

Branches can be (in)activated due to mutation,

changed gene expression,

modulation due to drug treatment,

etc.Alexander Lex | Harvard University

3

Why use Visualization?

Efficient communication of information

A -3.4

B 2.8

C 3.1

D -3

E 0.5

F 0.3Alexander Lex | Harvard University

C

B

D

F

A

E

4

Experimental Data and Pathways

[Lindroos2002]

[KEGG]

Alexander Lex | Harvard University

5

Visualization Approaches

On-Node Mapping Separate Linked Views Small Multiples

Layout Adaption Linearization

[Mey

er 2

010]

[Jun

ker 2

006]

[Lin

droo

s 20

02]


Path-Extraction

Alexander Lex | Harvard University 6

REQUIREMENTS ANALYSIS

Teaser Picture

7

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 Scale

Large number of experimentsLarge datasets have more than 500 experiments

Multiple groups/conditions


9

R II: Data Heterogeneity

Different types of data, e.g.,mRNA expression numerical

mutation statuscategorical

copy number variation ordered categorical

metabolite concentration numerical

Require different visualization techniques


10

R III: Multi-Mapping

Pathways nodes are biomoleculesProteins, 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 nodeAlexander Lex | Harvard University

C

E

E1

E2

E3

E4

CA3

KJ2

RAF

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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 layouts

Goal: preserve layouts as much as possible

Two approaches: Emulate drawing conventions

Use original layoutsAlexander Lex | Harvard University

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

14


Separate Linked Views Small Multiples


[Mey

er 2

010]

[Jun

ker 2

006]


Path-Extraction

On-Node Mapping

[Lin

droo

s 20

02]

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On-Node Mapping

Alexander Lex | Harvard University [Lindroos2002]

16

On-Node Mapping


[Westenberg 2008]

[Gehlenborg 2010]

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On-Node & Tooltip


[Streit 2008]

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On-Node Mapping

Not scalableespecially when used with „original“ layout

animation not an alternative

Good for overview with homogeneous data

Excellent for topology-based tasks

Bad for attribute-based tasks


19

On-Node Mapping Reflection

R 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


20

On-Node Mapping Reflection

R IV (Layout-Preservation)excellent!

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

bad for attribute-based tasks


21

[Lin

droo

s 20

02]

On-Node Mapping


Small Multiples


[Mey

er 2

010]

[Jun

ker 2

006]


Path-Extraction

Separate Linked Views

22



[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 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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[Lin

droo

s 20

02]

On-Node Mapping



[Mey

er 2

010]

[Jun

ker 2

006]


Path-Extraction

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 Reflection

R 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


31

Small Multiples Reflection


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“


32


[Lin

droo

s 20

02]

On-Node Mapping


Small Multiples

Linearization

[Mey

er 2

010]


Path-ExtractionLayout Adaption

[Jun

ker 2

006]

33

Layout Adaption

„Moderate“ Layout Adaptionmake space for on-node encoding


[Gehlenborg 2010][Junker 2006]

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Layout Adaption

„Extreme“ layout adaptionencode information throughposition

Alexander Lex | Harvard University [Bezerianos 2010]

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

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



35

Layout Adaption Reflection

R I (Scale)limited to a handful of conditions/experiments

R II (Heterogeneity)limited for heterogeneous data

Different story for „extreme“ layout adaption

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


36

Layout Adaption Reflection

R 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


37

Layout Adaption

[Jun

ker 2

006]


[Lin

droo

s 20

02]

On-Node Mapping


Small Multiples


Path-ExtractionLinearization

[Mey

er 2

010]

38

Linearization – Pathline


[Meyer 2010]

Combination oflayout adaption

separate linked views

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Linearization


[Meyer 2010]

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Linearization Reflection

R 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 Reflection

R IV (Layout-Preservation)not possible

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


limited for combining them!

Manual creation of linearized version

Unclear if suitable for more complex pathwaysAlexander Lex | Harvard University

42




[Mey

er 2

010]

[Jun

ker 2

006]

[Lin

droo

s 20

02]


Path-Extraction

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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


45

Pathway View

On-Node Mapping

Path highlighting with Bubble Sets [Collins2009]

SelectionStart- and end node

Iterative adding of nodes

IGF-1

low high


46

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 Reflection

R 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


53

enRoute Reflection

R 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


54

Using enRoute

enRoute 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

56

Which to use?



[Mey

er 2

010]

[Jun

ker 2

006]

[Lin

droo

s 20

02]


Path-Extraction

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Use Technique that fits your task

Topology is important

One experimental condition


On-Node Mapping

[Lindroos2002]

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Topology is important

Size of graph is limited

Handful of conditions


Small Multiples

59


Experimental data is critical

Pathways are a “sideshow”


[Shannon 2008]


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Topology & experimental data is important

Data 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


Documents

Bridging the Gap Between Pathways and Experimental Data Alexander Lex