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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
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 [email protected]://caleydo.org
?Marc Streit
Hans-Jörg Schulz Christian Partl
Dieter SchmalstiegPeter J. Park
Nils Gehlenborg
Alexander Lex | Harvard University