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.
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Intro to Cytoscape Network Visualization and Analysis
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
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
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
– LayoutsSettings… 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
28. 28
Cytoscape
• Common use cases
– Visualizing:
• PPI
• Pathways
– Integration:
• Expression profiles
• Other state data
– Analysis:
• Network properties
• Data mapped onto network
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
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
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!
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:
– ViewShow 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: HelpError Dialog
– Can change a preference to write it to the console
• EditPreferencesProperties…
• 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.
Notes de l'éditeur
UPDATE
Medical professionals that work with patients? Bench biologists? Bioinformaticians? Software developers? CS and mathematicians? Physicists?
Cytoscape is a network visualization and analysis tool. What does that mean?Well, what are networks? They are points connected by lines; or nodes connected by edges. And what purpose do they serve? They are intuitive representations of the relationship between things (this connects to this, which connects to this). They reduce complexity by providing structure (like groupings and heirarchy). And networks and graphs like this provide efficiency over tables (reading this as a table of pair-wise interactions would be unenlightening).So, a network tool like Cytoscape gives us a way to generate and study networks. There are two main goals: Analysis and Visualization. You can analyze the inherent properites of a network (such as…). In more functional terms this might include identifying hubs or “interesting” subnetworks or following a mechanism of action. The other main goal is Visualization. You can not only visualize the network itself, but you can overlay data onto it and feed it back into the analysis. For example…
Cytoscape has been used to study…
And, specifically related to biomedical research, Cytoscape has been used to study a variety of diseases. In each of these cases, subnetworks are pulled out of the integration of networks and various biomedical datasets to identify…
So, the challenge here is to make sense of biological networks. Networks in biology come in all sorts of shapes and sizes, each inscrutable in it’s own way…
Signaling pathway: Androgen Receptor Signaling PathwayMetabolic pathway: One Carbon pathway
----- Meeting Notes (8/9/13 16:35) -----Think about YOUR data. What type is it? What network representation is most effective?
Add some examples
Add counter examples
Need layout examples
Notes: it is a Cytoscape goal that both the graph model and the data are free from Biological semantics
Notes: it is a Cytoscape goal that both the graph model and the data are free from Biological semanticsMention water distribution network publication