Transcript Tutorial_Part3_incl_results_final - Bioinfo-casl

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
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Open the Neuroblastoma session and load the pvalues
from this experiment
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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
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Caveats:
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Several clusters; those
with most mutations
might deliver additional
wet lab testable pathway
players (drugtargets?)
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Maintenance
(housekeeping) processes
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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.
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What categories are enriched?
2.
Can you find these back in the article?
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Assignment 3.2: results
Quite some categories!
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Filter out less informative
top level categories: in
several deeper categories
neuron projection pops up
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A clustering method can
specify
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Use subsets only
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Worth mentioning: other
tools eg. David
In second cluster neuron
projection clearer; and
large set of mutated genes
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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
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Is it scale free?
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Can you find important players?
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Assignment 3.3: results
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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
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Run the MCODE algorithm
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Run the MCL clustering algorithm
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Compare the results
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Can you corroborate some of the clusters found in the
article?
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Are there additional filtering options?
4.
Play with the settings and observe their influence
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Assignment 3.4: results
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MCODE gives fuzzier clusters
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E.g. the syx-syb cluster
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The cluster parameters are set as attributes; these can
be used to filter
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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
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In the Drosophila session try to find a feedforward
motif
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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
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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