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]

Alexander Lex | Harvard University

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

Alexander Lex | Harvard University

8

R I: Data Scale

Large number of experimentsLarge datasets have more than 500 experiments

Multiple groups/conditions

Alexander Lex | Harvard University

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

Alexander Lex | Harvard University

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

11

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!

Alexander Lex | Harvard University

C

B

D

F

A

E

Alexander Lex | Harvard University 13

VISUALIZATION TECHNIQUES

Teaser Picture

14

Visualization Approaches

Separate Linked Views Small Multiples

Layout Adaption Linearization

[Mey

er 2

010]

[Jun

ker 2

006]

Alexander Lex | Harvard University

Path-Extraction

On-Node Mapping

[Lin

droo

s 20

02]

15

On-Node Mapping

Alexander Lex | Harvard University [Lindroos2002]

16

On-Node Mapping

Alexander Lex | Harvard University

[Westenberg 2008]

[Gehlenborg 2010]

17

On-Node & Tooltip

Alexander Lex | Harvard University

[Streit 2008]

18

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

Alexander Lex | Harvard University

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

Alexander Lex | Harvard University

20

On-Node Mapping Reflection

R IV (Layout-Preservation)excellent!

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

bad for attribute-based tasks

Alexander Lex | Harvard University

21

[Lin

droo

s 20

02]

On-Node Mapping

Visualization Approaches

Small Multiples

Layout Adaption Linearization

[Mey

er 2

010]

[Jun

ker 2

006]

Alexander Lex | Harvard University

Path-Extraction

Separate Linked Views

22

Separate Linked Views

Alexander Lex | Harvard University

[Shannon 2008]

23

Separate Linked Views

Alexander Lex | Harvard University

24

Separate Linked Views

Alexander Lex | Harvard University

25

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

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

Alexander Lex | Harvard University

27

Separate Linked Views

[Lin

droo

s 20

02]

On-Node Mapping

Visualization Approaches

Layout Adaption Linearization

[Mey

er 2

010]

[Jun

ker 2

006]

Alexander Lex | Harvard University

Path-Extraction

Small Multiples

28

Small Multiples

Alexander Lex | Harvard University

29

Small Multiples

Alexander Lex | Harvard University [Barsky 2008]

Video!

30

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

Alexander Lex | Harvard University

31

Small Multiples Reflection

R 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

32

Separate Linked Views

[Lin

droo

s 20

02]

On-Node Mapping

Visualization Approaches

Small Multiples

Linearization

[Mey

er 2

010]

Alexander Lex | Harvard University

Path-ExtractionLayout Adaption

[Jun

ker 2

006]

33

Layout Adaption

„Moderate“ Layout Adaptionmake space for on-node encoding

Alexander Lex | Harvard University

[Gehlenborg 2010][Junker 2006]

34

Layout Adaption

„Extreme“ layout adaptionencode information throughposition

Alexander Lex | Harvard University [Bezerianos 2010]

Video: 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

Alexander Lex | Harvard University

36

Layout Adaption Reflection

R 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

37

Layout Adaption

[Jun

ker 2

006]

Separate Linked Views

[Lin

droo

s 20

02]

On-Node Mapping

Visualization Approaches

Small Multiples

Alexander Lex | Harvard University

Path-ExtractionLinearization

[Mey

er 2

010]

38

Linearization – Pathline

Alexander Lex | Harvard University

[Meyer 2010]

Combination oflayout adaption

separate linked views

39

Linearization

Alexander Lex | Harvard University

[Meyer 2010]

40

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

Alexander Lex | Harvard University

41

Linearization Reflection

R 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 version

Unclear if suitable for more complex pathwaysAlexander Lex | Harvard University

42

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]

Alexander Lex | Harvard University

Path-Extraction

43

CALEYDO ENROUTE

Alexander Lex | Harvard University

44

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

45

Pathway View

On-Node Mapping

Path highlighting with Bubble Sets [Collins2009]

SelectionStart- and end node

Iterative adding of nodes

IGF-1

low high

Alexander Lex | Harvard University

46

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

47

Experimental Data Representation

Gene Expression Data (Numerical)

Copy Number Data (Ordered Categorical)

Mutation Data

Alexander Lex | Harvard University

48

enRoute View – Putting All Together

Alexander Lex | Harvard University

49

Video!

Alexander Lex | Harvard University

http://enroute.caleydo.org

50

Glioblastoma Multiforme Example

Alexander Lex | Harvard University

51

Glioblastoma Multiforme Example

Alexander Lex | Harvard University

52

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

Alexander Lex | Harvard University

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

Alexander Lex | Harvard University

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!

Alexander Lex | Harvard University

55

SUMMARY & RECOMMENDATIONS

Alexander Lex | Harvard University

Teaser Picture

56

Which to use?

On-Node Mapping Separate Linked Views Small Multiples

Layout Adaption Linearization

[Mey

er 2

010]

[Jun

ker 2

006]

[Lin

droo

s 20

02]

Alexander Lex | Harvard University

Path-Extraction

57

Use Technique that fits your task

Topology is important

One experimental condition

Alexander Lex | Harvard University

On-Node Mapping

[Lindroos2002]

58

Use Technique that fits your task

Topology is important

Size of graph is limited

Handful of conditions

Alexander Lex | Harvard University

Small Multiples

59

Use Technique that fits your task

Experimental data is critical

Pathways are a “sideshow”

Alexander Lex | Harvard University

[Shannon 2008]

Separate Linked Views

60

Use Technique that fits your task

Topology & experimental data is important

Data is heterogeneous

Alexander Lex | Harvard University

Path Extraction

61

What’s Nice About That?

Caleydo supports all of them ;)

Alexander Lex | Harvard University

62

FUTURE CHALLENGES

Alexander Lex | Harvard University

Teaser Picture

63

Other Pathway-Related Challenges

Cross-connections between pathways

Alexander Lex | Harvard University [Klukas 2007]

64

Other Pathway-Related Challenges

Effect of compounds (medication) on pathways

Alexander Lex | Harvard University

[Lounkine 2012]

65

Bridging the Gap

Between Pathways and Experimental Data

Alexander Lex, Harvard Universityalex@seas.harvard.eduhttp://caleydo.org

?Marc Streit

Hans-Jörg Schulz Christian Partl

Dieter SchmalstiegPeter J. Park

Nils Gehlenborg

Alexander Lex | Harvard University