1 / 31

Tutorial session 3 Network analysis

Tutorial session 3 Network 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

hada
Télécharger la présentation

Tutorial session 3 Network analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Tutorial session 3Network analysis Exploring PPI networks using Cytoscape EMBO Practical Course Session 8 Nadezhda Doncheva and Piet Molenaar

  2. 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

  3. 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

  4. jActiveModules (Demo)

  5. 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

  6. Use case; Assignment 3.1 • Systematic perturbations • Different cell-lines • Including controls: DMSO • 97 arrays: data collected from R2: hugo-once etc PIK90 PI3K-dependent Cell lines -SY5Y -D425 RAS/ERK-dependent Cell lines -SHEP2 -RD RAS PI3K AKTi 1/2 MK2206 RAF AKT PI103 MEK U0126 Harvest: RNA  Affy (97samples) protein  WB mTORC1 mTORC2 ERK PI3K signature RAS/ERK signature Rapamycin PP242

  7. Use case; Assignment 3.1 • Open the Neuroblastoma session and load the pvalues from this experiment • Run jActiveModules on the annotated network • What regions are important? • Can you imagine any caveats for this method?

  8. Assignment 3.1: results • Important regions • Several clusters; those with most mutations might deliver additional wet lab testable pathway players (drugtargets?) • Caveats: • Maintenance (housekeeping) processes • Known pathways only

  9. 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

  10. 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

  11. BiNGO (Demo)

  12. Use case; Assignment 3.2 • Open the Neuroblastoma session and run BiNGO on the filtered network. • What categories are enriched? • Can you find these back in the article?

  13. Assignment 3.2: results • Quite some categories! • Filter out less informative top level categories: in several deeper categories neuron projection pops up • A clustering method can specify • Use subsets only • Worth mentioning: other tools eg. David • In second cluster neuron projection clearer; and large set of mutated genes

  14. 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

  15. NetworkAnalyzer (Demo)

  16. 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

  17. CytoHubba (Demo)

  18. Use case; Assignment 3.3 • Open the Drosophila network session • Check the network parameters • Is it scale free? • Can you find important players?

  19. Assignment 3.3: results • It is scalefree; the node degree distribution fits a power law • Depends on the type of player you want to find; between processes or master regulator over number of genes?

  20. 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

  21. MCODE & ClusterMaker (Demo)

  22. Use case; Assignment 3.4 • Open the Drosophila session • Run the MCODE algorithm • Run the MCL clustering algorithm • Compare the results • Can you corroborate some of the clusters found in the article? • Are there additional filtering options? • Play with the settings and observe their influence

  23. Assignment 3.4: results • MCODE gives fuzzier clusters • E.g. the syx-syb cluster • The cluster parameters are set as attributes; these can be used to filter • Less stringent settings will produce additional clusters, but also larger clusters

  24. 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

  25. NetMatch (Demo)

  26. Use case; Assignment 3.5 • In the Drosophila session try to find a feedforward motif • Finally toy around with the settings of the Vizmapper to produce a nice paper-ready figure!

  27. Assignment 3.5: results • Simple feed forward gives lots of matches • Add attributes, or make more complex queries • Toying around can produce nice results!

  28. Other Useful Plugins • PSICQUICUniversalClient • AgilentLiteratureSearch • GeneMANIA • CyThesaurus • structureViz • ClusterMaker • EnrichmentMap • PiNGO • ClueGO • RandomNetworks

  29. 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 New hypotheses

  30. End! And a final note…..

  31. 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

More Related