Tutorial session 3
Network analysis
Exploring PPI networks using Cytoscape
EMBO Practical Course Session 8
Nadezhda Doncheva and Piet Molenaar
Overview
Focus: Network analysis
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Identify active subnetworks
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Analyze Gene Ontology enrichment
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Perform topological analysis
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Find network clusters
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Find network motifs
Concepts
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Enrichment
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Clustering
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Guild by association
Data
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Stored sessions; Drosophila and Neuroblastoma
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Identify active subnetworks
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jActiveModules plugin
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Active modules are sub-networks that show differential
expression over user-specified conditions or time-points
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Microarray gene-expression attributes
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Mass-spectrometry protein abundance
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Input: interaction network and p-values for gene
expression values over several conditions
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Output: significant sub-networks that show differential
expression over one or several conditions
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jActiveModules (Demo)
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Use case; Assignment 3.1
Using neuroblastoma cell lines inhibitors to elucidate
important pathways
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2 neuroblastoma cell lines: SHEP21, SY5Y
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7 inhibitors
Profiled on Affymetrix array
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http://r2.amc.nl
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Other resource e.g. GEO
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Use case; Assignment 3.1
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Systematic perturbations
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Different cell-lines
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Including controls: DMSO
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97 arrays: data collected from R2: hugo-once etc
PI3K-dependent
Cell lines
-SY5Y
-D425
PIK90
RAS/ERK-dependent
Cell lines
-SHEP2
-RD
Harvest: RNA  Affy (97samples)
protein  WB
PI3K signature
RAS/ERK signature
RAS
RAF
U0126
PI3K
PI103
AKTi 1/2
MK2206
AKT
MEK
mTORC1
mTORC2
ERK
Rapamycin
PP242
Use case; Assignment 3.1
1.
Open the Neuroblastoma session and load the pvalues
from this experiment
2.
Run jActiveModules on the annotated network
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1.
What regions are important?
2.
Can you imagine any caveats for this method?
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Assignment 3.1: results
Important regions
1.
1.
Caveats:
2.
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Several clusters; those
with most mutations
might deliver additional
wet lab testable pathway
players (drugtargets?)
1.
Maintenance
(housekeeping) processes
2.
Known pathways only
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Gene Ontology
Provides three structured
controlled vocabularies
(ontologies) of defined
terms representing gene
product properties:
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Biological Process (23074
terms): biological goal or
objective
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Molecular Function (9392
terms): elemental
activity/task
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Cellular Component (2994
terms): location or complex
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Analyze Gene Ontology enrichment
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BiNGO plugin:
http://www.psb.ugent.be/cbd/papers/BiNGO/Home.html
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Calculates over-representation of a subset of genes with
respect to a background set in a specific GO category
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Input: subnetwork or list, background set by user
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Output: tree with nodes color reflecting
overrepresentation; also as lists
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Caveats: Gene identifiers must match; low GO term
coverage, background determining
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BiNGO (Demo)
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Use case; Assignment 3.2
Open the Neuroblastoma session and run BiNGO on
the filtered network.
1.
1.
What categories are enriched?
2.
Can you find these back in the article?
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Assignment 3.2: results
Quite some categories!
1.
1.
Filter out less informative
top level categories: in
several deeper categories
neuron projection pops up
2.
A clustering method can
specify
3.
Use subsets only
4.
Worth mentioning: other
tools eg. David
In second cluster neuron
projection clearer; and
large set of mutated genes
2.
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Compute topological parameters
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NetworkAnalyzer plugin: http://med.bioinf.mpiinf.mpg.de/netanalyzer/
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Computes a comprehensive set of simple and complex
topological parameters
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Displays the results in charts, which can be saved as
images or text files
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Can be combined with the ShortestPath plugin
http://www.cgl.ucsf.edu/Research/cytoscape/shortestPath/i
ndex.html
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NetworkAnalyzer (Demo)
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Identify hubs
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CytoHubba plugin:
http://hub.iis.sinica.edu.tw/cytoHubba/
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Computes several topological node parameters
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Identifies essential nodes based on their score and
displays them in a ranked list
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Generates a subnetwork composed of the best-scored
nodes
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CytoHubba (Demo)
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Use case; Assignment 3.3
Open the Drosophila network session
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Check the network parameters
1.
1.
Is it scale free?
2.
Can you find important players?
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Assignment 3.3: results
1.
It is scalefree; the node
degree distribution fits a
power law
2.
Depends on the type of
player you want to find;
between processes or
master regulator over
number of genes?
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Find network clusters
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Network clusters are highly interconnected sub-networks
that may be also partly overlapping
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Clusters in a protein-protein interaction network have
been shown to represent protein complexes and parts of
biological pathways
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Clusters in a protein similarity network represent protein
families
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Network clustering is available through the MCODE
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Cytoscape plugin: http://baderlab.org/Software/MCODE
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MCODE & ClusterMaker (Demo)
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Use case; Assignment 3.4
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Open the Drosophila session
1.
Run the MCODE algorithm
2.
Run the MCL clustering algorithm
1.
Compare the results
2.
Can you corroborate some of the clusters found in the
article?
3.
Are there additional filtering options?
4.
Play with the settings and observe their influence
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Assignment 3.4: results
1.
MCODE gives fuzzier clusters
2.
E.g. the syx-syb cluster
3.
The cluster parameters are set as attributes; these can
be used to filter
4.
Less stringent settings will produce additional clusters,
but also larger clusters
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Find network motifs
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NetMatch plugin:
http://alpha.dmi.unict.it/~ctnyu/netmatch.html
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Network motif is a sub-network that occurs significantly
more often than by chance alone
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Input: query and target networks, optional node/edge
labels
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Output: topological query matches as subgraphs of target
network
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Supports: subgraph matching, node/edge labels, label
wildcards, approximate paths
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NetMatch (Demo)
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Use case; Assignment 3.5
1.
In the Drosophila session try to find a feedforward
motif
2.
Finally toy around with the settings of the Vizmapper to
produce a nice paper-ready figure!
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Assignment 3.5: results
Simple feed forward gives lots of matches
1.
1.
2.
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Add attributes, or make more complex queries
Toying around can produce nice results!
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Other Useful Plugins
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PSICQUICUniversalClient
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AgilentLiteratureSearch
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GeneMANIA
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CyThesaurus
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structureViz
ClusterMaker
EnrichmentMap
PiNGO
ClueGO
RandomNetworks
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Wrapping up…
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Biological questions
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I have a protein
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I have a list of proteins
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Function, characteristics from
known interactions
Shared features, connections
I have data
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Derive causal networks
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Network
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Topology
Hubs
Clusters
New hypotheses
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End!
And a final note…..
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Announcing Cytoscape 3.0 Beta
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Easier data import
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Improved user experience
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Graphical annotations
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One-click install from AppStore
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Improved API for app developers
http://cytoscape.org