This document provides an overview and agenda for an introductory workshop on network visualization and analysis using Cytoscape. The agenda includes introductions, an overview of Cytoscape concepts and user interface, six tutorials, breaks, and a presentation on pathway analysis. The document discusses loading and visualizing networks and attributes in Cytoscape, different types of biological networks, visualization techniques like layouts and data mapping, and tips for using Cytoscape effectively.

1. 1
Intro to Cytoscape
Network Visualization and
Analysis with Cytoscape
Alex Pico
apico@gladstone.ucsf.edu
Hey, early birds! USB:Cytoscape Tutorial/2.8 Installers/…

2. 2
The Plan
• USB flash drive:
– Slides .pptx
– The “Book”
– 6 Tutorials
– Cytoscape installers (plus updated plugin)

3. 3
The Plan
• Schedule
– Introductions (1:30)
– Overview
– Cytoscape concepts and UI
– Tutorial #1
– 10 min break
– WikiPathways and Pathway Analysis (2:30)
– More Tutorials…
– Coffee break
– Jing Wang - NetGestalt (3:30)
– Q&A (4:20)

4. 4
Introductions
• Me
– Executive Director, NRNB
– Systems Biology Group, Gladstone Institutes
– 6 Years on Cytoscape core development team
• GenMAPP-CS Workspaces, criteriaMapper, GO-Elite,
BubbbleRouter, Mosaic, NOA, PathExplorer
– Co-founder and developer of WikiPathways
– Background: structural biology, pathway analysis
• You?

5. 5
Why networks?
• Networks provide an integrating context for data
• Commonly understood diagrammatic
representation for concepts and relationships
• Provides structure that helps reduce
underlying complexity of the data
• More efficient than searching databases
element-by-element
• Network tools give us functionality for studying
complex processes
• Analysis of global characteristics of the
data, e.g. degree, clustering coefficient,
shortest paths, centrality, density
• Identify key elements (hubs) and
„interesting‟ subnets
• Help elucidate mechanisms of interaction
• Visualization of data superimposed upon
the network
• Help understand how a process is modulated
or attenuated by a stimulus

6. 6
Applications of Network Biology
• Gene Function Prediction –
shows connections to sets of
genes/proteins involved in same
biological process
• Detection of protein
complexes/other modular
structures –
discover modularity & higher order
organization (motifs, feedback
loops)
• Network evolution –
biological process(s) conservation
across species
• Prediction of new interactions
and functional associations –
Statistically significant domain-
domain correlations in protein
interaction network to predict
protein-protein or genetic
interaction
jActiveModules, UCSD
PathBlast, UCSD
mCode, University of Toronto
DomainGraph, Max Planck Institute

7. 7
Applications of Networks in Disease
• Identification of disease
subnetworks – identification of
disease network subnetworks that are
transcriptionally active in disease.
• Subnetwork-based diagnosis –
source of biomarkers for disease
classification, identify interconnected
genes whose aggregate expression
levels are predictive of disease state
• Subnetwork-based gene association
– map common pathway mechanisms
affected by collection of genotypes
(SNP, CNV)
Agilent Literature Search
Mondrian, MSKCC
PinnacleZ, UCSD

8. 8
The Challenge
• Making sense out of biological networks….

9. 9
The Challenge
• Biological networks (nodes and edges)
– Seldom tell us anything by themselves
– Analysis involves:
• Understanding the characteristics of the network
– Modularity
– Comparison with other networks (specifically random
networks)
– Visualization involves:
• Placing nodes in a meaningful way (layouts)
• Mapping biologically relevant data to the network
– Node size
– Node color
– Edge weights

10. 10
The Challenge

11. 11
Biological Network Taxonomy
• Pathways
– Signaling
– Metabolic
– Regulatory
– Phylogeny (could also be thought of as similarity)

12. 12
Biological Network Taxonomy
• Interaction Networks
– Protein-Protein interactions
– Protein-Ligand interactions
– Genetic interactions
– Domain-Domain interactions
– Others
• Residue or atomic interactions
• Cell/cell interactions
• Population biology
• Epidemiology
• Social networks

13. 13
Biological Network Taxonomy
• Similarity
– Protein-Protein similarity
– Chemical similarity
– Ligand similarity (SEA)
– Others
• Tag clouds
• Topic maps

14. 14
Visualization of Biological Networks
• Depiction
• Data Mapping
• Layouts
• Animation

15. 15
Depiction
• There are various ways to depict biological
networks:
– Node-Link (graph) representation
• This is what we most often think of
– Partitioned Node-Link representation
• Split graph into discrete partitions based on some
feature
– Matrix representation
• Can be useful for very dense networks
• Can also map information into cells of matrix
– e.g. degree, color scale (heat map)

16. 16
Data mapping
• Mapping of data values associated with graph
elements onto graph visuals
• Visual attributes
– Node fill color, border color, border width, size,
shape, opacity, label
– Edge type, color, width, ending type, ending size,
ending color
• Mapping types
– Passthrough (labels)
– Continuous (numeric values)
– Discrete (categories)

17. 17
Data mapping
• Avoid cluttering your visualization with too
much data
– Map the data you are specifically interested in to
call out meaningful differences
– Mapping too much data to visual attributes may
just confuse the viewer
– Can always create multiple networks and map
different values

18. 18
Layouts
• Layouts determine the location of nodes and
(sometimes) the paths of edges
• Types:
– Simple
• Grid
• Partitions
– Hierarchical
• layout data as a tree or hierarchy
• Works best when there are no loops
– Circular (Radial)
• arrange nodes around a circle
• could use node attributes to govern position
– e.g. degree sorted

19. 19
Layouts
• Types:
– Force-Directed
• simulate edges as springs
• may be weighted or unweighted
– Combining layouts
• Use a general layout (force directed) for the entire
graph, but use hierarchical or radial to focus on a
particular portion
– Multi-layer layouts
• Partition graph, layout each partition then layout
partitions
– Many, many others

20. 20
Layouts
• Use layouts to convey the relationships
between the nodes
• Layout algorithms may need to be “tuned” to
fit your network
– LayoutsSettings… menu
• Lots of parameters to change layout algorithm
behavior
• Can also consider laying out portions of your
network

21. 21
Animation
• Animation is useful to show changes in a
network:
– Over a time series
– Over different conditions
– Between species

22. 22
Introduction to Cytoscape
• Overview
• Core Concepts
– Networks vs. Attributes
– VizMapper
– Apps
• Working with Data
– Loading network from the Web
– Importing networks from csv files or Excel
– Importing attributes from csv files or Excel
– The attribute browser
• Cytoscape tips & tricks

23. 23
WHAT IS CYTOSCAPE?
www.cytoscape.org
 Visualization
 Integration
 Analysis
Cytoscape

24. 24
 Open source
 Cross platform
 Consortium
University of Toronto
Cytoscape

25. 25
Core Concepts
• Networks and Annotations
Annotations
e.g., attributes or data
Networks
e.g, biological pathways

26. 26
Core Concepts
• Visual Mapping with VizMapper
AnnotationsNetworks
VizMapper

27. 27
Core Concepts
• Cytoscape Apps!
http://apps.cytoscape.org

28. 28
Cytoscape
• Common use cases
– Visualizing:
• PPI
• Pathways
– Integration:
• Expression profiles
• Other state data
– Analysis:
• Network properties
• Data mapped onto network

29. 29
Loading Networks
• Use import network from table:
– Excel file
– Comma or tab delimited text

30. 30
Loading Networks
• Use import network from table:
– Excel file
– Comma or tab delimited text
• Use import network from web services
– Allows query and load from a variety of services:
• Pathway commons
• WikiPathways (if GPML plugin is loaded)
• NCBI Entrez Eutilities
• BioCyc

31. 31
Loading Attributes
• Loading attributes
– Use import attribute from table

32. 32
Loading Attributes
• Loading attributes
– Use import attribute from table
• The Data Panel

33. 33
Tutoral #1
Introduction to Cytoscape – Part 1
http://tutorials.cytoscape.org

34. 34
Examples/Demos
• clusterMaker
– Clustering and cluster visualizations
• Agilent LitSearch Tool
– Extracting networks from abstracts
• WikiPathways
– Search and load pathway diagrams

35. 37
Cytoscape 2.8 vs. Cytoscape 3
• Cytoscape 2.8:
– One network. All other networks are projections
on that network.
• Essentially a rooted tree
• No way to duplicate nodes without sharing attributes
• Cytoscape 3:
– Allows multiple roots.
• Can have multiple trees
• Each group of networks that shares a single root is
called a collection

36. 42
Loading Networks (3.0)
• Conceptually the same as 2.8
– Use import network from table:
• Excel file
• Comma or tab delimited text
• …but
– Must specify if you want a new network collection
(tree)
– If not, you need to specify the join column

37. 43
Loading Tables (3.0)
• Same as 2.8, except:
– Use Import table from file
– You need to specify the network collection

38. 44
Data Panel

39. 45
Visual Style Manager

40. 46
Cytoscape 2.8 vs 3.0
• Compare and Contrast
– 3.0 is more stable
– 3.0 has improved model and UI
– 2.8 has more apps
Depends on what you need and when you need it
• Timing
– Current release: 3.0.2
– 3.1 coming in October
– 22 apps and counting!

41. 47
More Tutorals
http://tutorials.cytoscape.org

42. 48
Tips & Tricks
• “Root graph”
– “There is one graph to rule them all….”
– The networks in Cytoscape are all “views” on a
single graph.
– Changing the attribute for a node in one network
will also change that attribute for a node with the
same ID in all other loaded networks
– There is no way to “copy” a node and keep the
same ID
– Make a copy of the session

43. 49
Tips & Tricks
• Network views
– When you open a large network, you will not get a
view by default
– To improve interactive performance, Cytoscape
has the concept of “Levels of Detail”
• Some visual attributes will only be apparent when you
zoom in
• The level of detail for various attributes can be changed
in the preferences
• To see what things will look like at full detail:
– ViewShow Graphics Details

44. 50
Tips & Tricks
• Sessions
– Sessions save pretty much everything:
• Networks
• Properties
• Visual styles
• Screen sizes
– Saving a session on a large screen may require
some resizing when opened on your laptop

45. 51
Tips & Tricks
• Logging
– By default, Cytoscape writes it’s logs to the Error
Dialog: HelpError Dialog
– Can change a preference to write it to the console
• EditPreferencesProperties…
• Set logger.console to true
• Don’t forget to save your preferences
• Restart Cytoscape
– (can also turn on debugging: cytoscape.debug, but
I don’t recommend it)

46. 52
Tips & Tricks
• Memory
– Cytoscape uses lots of it
– Doesn’t like to let go of it
– An occasional restart when working with large
networks is a good thing
– Destroy views when you don’t need them
– Java doesn’t give us a good way to get the memory
right at start time
• Cytoscape 2.7 does a much better job at “guessing” good
default memory sizes than previous versions

47. 53
Tips & Tricks
• .cytoscape directory
– Your defaults and any plugins downloaded from the
plugin manager will go here
– Sometimes, if things get really messed up, deleting (or
renaming) this directory can give you a “clean slate”
• Plugin manager
– “Outdated” doesn’t necessarily mean “won’t work”
– Plugin authors don’t always update their plugins
immediately after new releases
– Click on “Show outdated plugins” to see the entire list
of plugins.