Tutorial session 3Network analysis

Exploring PPI networks using Cytoscape

EMBO Practical Course Session 8

Nadezhda Doncheva and Piet Molenaar

Overview

Focus: Network analysis Identify active subnetworks

Analyze Gene Ontology enrichment

Perform topological analysis

Find network clusters

Find network motifs

Concepts Enrichment Clustering Guild by association

Data Stored sessions; Drosophila and Neuroblastoma

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Identify active subnetworks jActiveModules plugin Active modules are sub-networks that show

differential expression over user-specified conditions or time-points Microarray gene-expression attributes

Mass-spectrometry protein abundance

Input: interaction network and p-values for gene expression values over several conditions

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 2 neuroblastoma cell lines: SHEP21, SY5Y

7 inhibitors

Profiled on Affymetrix array http://r2.amc.nl

Other resource e.g. GEO

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Use case; Assignment 3.1 Systematic perturbations

Different cell-lines

Including controls: DMSO

97 arrays: data collected from R2: hugo-once etc

PI3K signatureRAS/ERK signature

RAS/ERK-dependentCell lines-SHEP2

-RD

PI3K-dependentCell lines

-SY5Y-D425

Harvest: RNA Affy (97samples) protein WB

PI3K

AKT

mTORC1 mTORC2

RAS

MEK

RAF

ERK

PI103

PP242

PIK90

Rapamycin

U0126

AKTi 1/2

MK2206

Use case; Assignment 3.11. Open the Neuroblastoma session and load

the pvalues from this experiment

2. Run jActiveModules on the annotated network

1. What regions are important?

2. Can you imagine any caveats for this method?

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Assignment 3.1: results1. Important regions

1. Several clusters; those with most mutations might deliver additional wet lab testable pathway players (drugtargets?)

2. Caveats:

1. Maintenance (housekeeping) processes

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Gene Ontology Provides three structured

controlled vocabularies (ontologies) of defined terms representing gene product properties: Biological Process (23074

terms): biological goal or objective

Molecular Function (9392 terms): elemental activity/task

Cellular Component (2994 terms): location or complex

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Analyze Gene Ontology enrichment BiNGO plugin:

http://www.psb.ugent.be/cbd/papers/BiNGO/Home.html

Calculates over-representation of a subset of genes with respect to a background set in a specific GO category

Input: subnetwork or list, background set by user Output: tree with nodes color reflecting

overrepresentation; also as lists Caveats: Gene identifiers must match; low GO

term coverage, background determining

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BiNGO (Demo)

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Use case; Assignment 3.21. Open the Neuroblastoma session and run

BiNGO on the filtered network.

1. What categories are enriched?

2. Can you find these back in the article?

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Assignment 3.2: results1. Quite some categories!

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

2. In second cluster neuron projection clearer; and large set of mutated genes

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Compute topological parameters NetworkAnalyzer plugin:

http://med.bioinf.mpi-inf.mpg.de/netanalyzer/ Computes a comprehensive set of simple and

complex topological parameters Displays the results in charts, which can be

saved as images or text files Can be combined with the ShortestPath plugin

http://www.cgl.ucsf.edu/Research/cytoscape/shortestPath/index.html

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NetworkAnalyzer (Demo)

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Identify hubs CytoHubba plugin:

http://hub.iis.sinica.edu.tw/cytoHubba/ Computes several topological node

parameters Identifies essential nodes based on their score

and displays them in a ranked list 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

1.Check the network parameters

1. Is it scale free?

2. Can you find important players?

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Assignment 3.3: results1. 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 Network clusters are highly interconnected sub-

networks that may be also partly overlapping Clusters in a protein-protein interaction

network have been shown to represent protein complexes and parts of biological pathways

Clusters in a protein similarity network represent protein families

Network clustering is available through the MCODE

Cytoscape plugin: http://baderlab.org/Software/MCODE

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MCODE & ClusterMaker (Demo)

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Use case; Assignment 3.4 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: results1. 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 NetMatch plugin:

http://alpha.dmi.unict.it/~ctnyu/netmatch.html

Network motif is a sub-network that occurs significantly more often than by chance alone

Input: query and target networks, optional node/edge labels

Output: topological query matches as subgraphs of target network

Supports: subgraph matching, node/edge labels, label wildcards, approximate paths

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NetMatch (Demo)

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Use case; Assignment 3.51. 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: results1. Simple feed forward gives lots of matches

1. Add attributes, or make more complex queries

2. Toying around can produce nice results!

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Other Useful Plugins PSICQUICUniversalCli

ent AgilentLiteratureSear

ch GeneMANIA CyThesaurus

structureViz ClusterMaker EnrichmentMap PiNGO ClueGO RandomNetworks

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Wrapping up… Biological questions

I have a protein Function,

characteristics from known interactions

I have a list of proteins Shared features,

connections

I have data Derive causal networks

Network Topology Hubs Clusters

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New hypotheses

End!

And a final note…..

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Announcing Cytoscape 3.0 Beta Easier data import Improved user experience Graphical annotations One-click install from AppStore Improved API for app developers

http://cytoscape.org