1. Tutorial session 3 Network 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 12/6/20152

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 12/6/20153

4. jActiveModules (Demo) 12/6/20154

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 http://r2.amc.nl  Other resource e.g. GEO 12/6/20155

6. Use case; Assignment 3.1  Systematic perturbations  Different cell-lines  Including controls: DMSO  97 arrays: data collected from R2: hugo-once etc PI3K signature RAS/ERK signature RAS/ERK-dependent Cell lines -SHEP2 -RD PI3K-dependent Cell 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

7. Use case; Assignment 3.1 1. 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? 12/6/20157

8. Assignment 3.1: results 1. Important regions 1. Several clusters; those with most mutations might deliver additional wet lab testable pathway players (drugtargets?) 2. Caveats: 1. Maintenance (housekeeping) processes 2. Known pathways only 12/6/20158

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 12/6/20159

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 12/6/201510

11. BiNGO (Demo) 12/6/201511

12. Use case; Assignment 3.2 1. 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? 12/6/201512

13. Assignment 3.2: results 1. 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 12/6/201513

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/i ndex.html 12/6/201514

15. NetworkAnalyzer (Demo) 12/6/201515

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 12/6/201516

17. CytoHubba (Demo) 12/6/201517

18. 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? 12/6/201518

19. 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? 12/6/201519

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 12/6/201520

21. MCODE & ClusterMaker (Demo) 12/6/201521

22. 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 12/6/201522

23. 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 12/6/201523

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 12/6/201524

25. NetMatch (Demo) 12/6/201525

26. 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! 12/6/201526

27. Assignment 3.5: results 1. Simple feed forward gives lots of matches 1. Add attributes, or make more complex queries 2. Toying around can produce nice results! 12/6/201527

28. Other Useful Plugins  PSICQUICUniversalClient  AgilentLiteratureSearch  GeneMANIA  CyThesaurus  structureViz  ClusterMaker  EnrichmentMap  PiNGO  ClueGO  RandomNetworks 12/6/201528

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 12/6/201529 New hypotheses

30. End! And a final note….. 12/6/201530

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