Introduction to Cytoscape: a Hands-On Tutorial

Tutorial slides for a hands-on session at University of tokyo.

https://vis.hgc.jp/tutorials/#cytoscape

Transcript

1. Cytoscape Hands-On: Introduction to Biological Network Analysis and Visualization with Cytoscape Feb. 25, 2013 University of Tokyo HGC Keiichiro Ono Cytoscape Core Developer Team University of California, San Diego Trey Ideker Lab
2. Welcome! - Keiichiro Ono - Cytoscape Core Developer since 2005 - Area of Interest: Data Integration & Visualization - University of California, San Diego Trey Ideker Lab - This presentation file is available on my Speaker Deck account - speakerdeck.com/keiono
3. Please Ask Questions! - Anytime you can atop me and ask questions - In English or Japanese
4. Today’s Agenda - Basic Concepts - Browsing Network Data - Networks and Attributes - Visualization Techniques - Basic Analysis - Expression Data Analysis - Advanced Topics
5. Prerequisites - Cytoscape 2.8.3 and EnhancedSearch Plugin should be installed on your machine. - Cytoscape 3.0.0 release is available, but at this point, it is for power users due to missing plugins. - Differences between 3.x and 2.x will be discussed
6. Other Recommended Apps - Shortest Path v1.1 - PSICQUIC Client v 0.31 - KGMLReader v 0.15 - ClusterMaker v1.10 - MCODE v1.32
7. Machine Requirement - Faster, larger is better! - Cytoscape is a Java application, and it uses lots of memory - 4+ GB of memory is recommended for large network analysis / visualization
8. Core Concepts
9. Data Types in Cytoscape - There are two types of data in Cytoscape - Network - Attribute (Data Table)
10. Network - Mathematical Graph - G = (V, E) - Nodes - Any objects - Edges - Relationships between objects
11. Network Representation 1 2 2 3 1 3 4 3
12. Basic Scope - Cytoscape is a tool for extracting meaningful information, or modules, out of large biological networks
13. Human Interactome data from BioGRID visualized by Cytoscape
14. Module 1 Module 2
15. Cytoscape is for... - Data integration - Join networks and attributes - Network data analysis - Visualization
16. Cytoscape Workflow
17. 1.Load Networks (Get network data)‏ Cytoscape Workflow
18. 1.Load Networks (Get network data)‏ 2.Load Attributes (Get data about networks)‏ Cytoscape Workflow
19. 1.Load Networks (Get network data)‏ 2.Load Attributes (Get data about networks)‏ 3.Analyze and Visualize Networks Cytoscape Workflow
20. 1.Load Networks (Get network data)‏ 2.Load Attributes (Get data about networks)‏ 3.Analyze and Visualize Networks 4.Prepare for Publication Cytoscape Workflow
21. 1.Load Networks (Get network data)‏ 2.Load Attributes (Get data about networks)‏ 3.Analyze and Visualize Networks 4.Prepare for Publication - A specific example of this workflow: Cytoscape Workflow
22. 1.Load Networks (Get network data)‏ 2.Load Attributes (Get data about networks)‏ 3.Analyze and Visualize Networks 4.Prepare for Publication - A specific example of this workflow: − Cline, et al. “Integration of biological networks and gene expression data using Cytoscape”, Nature Protocols, 2, 2366-2382 (2007). Cytoscape Workflow
24. Network Data
25. Network Data Attributes
26. Network Data Attributes
27. Network Data Attributes
28. Network Data Annotated Networks Attributes
29. Network Data Annotated Networks Attributes
30. Network Data Annotated Networks Attributes Apps
31. Network Data Annotated Networks Attributes Analyzed Data Apps
32. Network Data Annotated Networks Attributes Analyzed Data Apps
33. Network Data Annotated Networks Attributes Analyzed Data Apps
34. Attributes - Any data about nodes, edges, and networks.
35. BRCA1
36. BRCA1 NCBI Gene ID 672
37. BRCA1 NCBI Gene ID 672 On Chromosome 17
38. BRCA1 NCBI Gene ID 672 On Chromosome 17 GO Terms:
39. BRCA1 NCBI Gene ID 672 On Chromosome 17 GO Terms: DNA Repair
40. BRCA1 NCBI Gene ID 672 On Chromosome 17 GO Terms: DNA Repair Cell Cycle
41. BRCA1 NCBI Gene ID 672 On Chromosome 17 GO Terms: DNA Repair Cell Cycle DNA Binding
42. BRCA1 NCBI Gene ID 672 On Chromosome 17 GO Terms: DNA Repair Cell Cycle DNA Binding Ensemble ID ENSG00000012048
43. Node Attributes - Gene Expression Data - Human-readable gene names - Gene Ontology Terms
44. Edge Attributes - Interaction Detection Methods - Y2H, NMR, affinity chromatography, etc. - Interaction Type - Physical, genetic, predicted - Publication ID
45. Network Attributes - Experiment details - Pathway Metadata - Description - Publication ID
46. Summary - There are two types of data - Networks - Attributes - Analysis and visualization will be performed for the integrated data (networks+attributes)
47. Tips for Tackling Real-World Problems
48. Define Your Goal - You need to define your goal first - e.g. visualize relationships between group of genes and known targets of FDA approved drugs - What kind of tools are required for your goal?
49. Know What Cytoscape Can Do - There is no silver bullet! - Understand Cytoscape Core Features - Research available Apps - Data pre-processing/post-processing may be required - Excel, R / Bioconductor, Scripts, Web Tools
50. Pick a Right Tool NetworkX
51. Lesson 1: Browsing Networks
52. Goal of This Lesson - Understand Basic UI - Loading a sample Session file - Learn how to browse the network and attributes - Know useful basic shortcuts/commands
53. Cytoscape Desktop
54. Cytoscape Desktop Network Panel
55. Cytoscape Desktop Toolbar Network Panel
56. Cytoscape Desktop Toolbar Network Panel Attribute Browser
57. Cytoscape Desktop Toolbar Network Panel Attribute Browser Network Views
58. Cytoscape Desktop Toolbar Network Panel Bird’s Eve View Attribute Browser Network Views
59. Attribute Browser
60. Attribute Browser Unique ID (Immutable)
61. Attribute Browser Unique ID (Immutable) Browser Tabs
62. Attribute Browser Unique ID (Immutable) Browser Tabs List Data (Values in [ ])
63. Session File - Snapshot of your workspace - Networks - Attributes - Visual Styles - System Properties
64. Saving & Opening - In Cytoscape, Save means saving your workspace states into a Session File - Open means loading a Session file - You can open only one session at a time - Merge Session feature will be implemented in the future version of Cytoscape 3.x
65. Open a Session - Click folder icon - Or, File → Open - Use sampleData/galFiltered.cys
66. Browsing Networks - Pan: Middle-Click + Drag or Command + Left-Click + Drag on Mac - Zoom - IN: Mouse Wheel UP - OUT: Mouse Wheel DOWN - Selection: Left-Click and Drag - Fit to Window - Selected region - Entire network
67. Useful Commands - First neighbor of nodes - Create new sub-network from selection - Tile network views - Show Graphics Details - Linkouts
68. First Neighbor of Nodes CTR+6
69. Create New Sub-Network From Selection CTR+N
70. - View → Arrange Network Windows → Tiled
71. Show Graphics Details - View → Show Graphics Details
72. Show Graphics Details - View → Show Graphics Details
73. Linkout - Send ID as a query to external resources - Open a browser window and displays the result
74. Lesson 1 Demo
75. Lesson 1: Summary - Session File is a snapshot of your workspace - Creating subnetworks from selection is easy - Attribute browser is a spreadsheet-like viewer for your attributes
76. Data Integration
77. Data Integration - Loading networks and mapping attributes onto them - Cytoscape provides: - Data import from files - Direct access to remote data sources
78. Import & Export - Import - Load any type of data - Network, Attributes, Visual Styles, etc. - Export - as network files, tables, or images
79. Network Data Formats - SIF - GML - XGMML - GraphML - BioPAX - PSI-MI - SBML - KGML (KEGG) - Excel - Delimited Text Table - CSV - Tab 47
80. Network Import - Usually, imported from pre-formatted data file - Or, use Table Import feature to select columns to be used as edges
81. Loading & Mapping Attributes - In most cases you need to import them from tables - e.g. Expression matrix saved as Excel workbook
84. Lesson 2: Loading Your Data
85. Loading & Mapping Data - http://opentutorials.cgl.ucsf.edu/index.php/ Tutorial:Introduction_to_Cytoscape#Loading _a_Simple_Network
87. Load Table as a Network - Simple list of binary interactions can be loaded as networks - Source - Interaction Type - Target - Or, Source - Target
88. Network from Excel File
89. Optional: Network From External Data Source
90. Lesson 2 Demo
91. ID Mapping - General (and old) problem in bioinformatics... - Tips - Use widely used, standard ID sets in your data files - Entrez Gene ID - Ensemble Gene ID - Use external application or Cytoscape App for mapping
92. Lesson 2: Summary - Cytoscape supports many standard network data formats - Any table data can be imported to Cytoscape by Table Import function - Preparing your table data with widely-used ID is important for easy mapping
93. Basic Analysis
94. Goal of This Section - Calculate network statistics by Network Analyzer - Filtering based on statistics - Basic search by EnhancedSearch Plugin - Try some more realistic example (requires faster machine!)
95. Core Analysis Features - Network Statistics - Search - Filtering
96. Lesson 3: Basic Analysis
97. Network Statistics
98. Network Analyzer - Provides basic statistics of networks - Degree - Centrality - Shortest Pass Length Distribution - etc.
99. Filtering by Network Statistics - NetworkAnalyzer provides all results as regular attributes - Can be used for filtering
100. Optional: Shortest Path - Shortest Path Plugin v1.1 is required - Primitive, but still useful for pathway analysis
102. Filtering
103. Filtering Example - Find nodes and edges with specific conditions - Pick nodes with degree > 5 - Select edges extracted from publication X - Find nodes annotated by GO term ID Y
107. Search
108. EnhancedSearch Query Syntax Cytoscape ESP: simple search of complex biological networks Maital Ashkenazi, Gary D. Bader, Allan Kuchinsky, Menachem Moshelion, David J. States Bioinformatics. 2008 June 15; 24(12): 1465–1466. Published online 2008 April 28. doi: 10.1093/bioinformatics/btn208 PMCID: PMC2427162
109. Combination of Basic Commands - Search & Select First Neighbors - Use EnhancedSearch to locate set of nodes/edges you are interested in - Press CTR-6 to find first neighbors
110. Lesson 3 Demo
111. Lesson 4: Advanced Analysis
112. More Realistic Example
113. Scenario - You have a list of oncogenes - In addition, you have a list of known druggable targets - You want to visualize relationship between oncogenes and druggable targets
114. Loading Human Interactome - BioGRID Release 3.2.97 - Human - Pre-filtered to remove cross-species interactions - Session file is available for download
116. List of Drug Targets
117. Import List of Druggable Targets - Simple list of known drug targets - Import from table
119. Import List Attributes - You need to specify delimiters to import list attributes correctly! - Select comma for this example
120. Filtering by Browser - DO NOT CREATE VIEW YET! - It is not necessary for filtering - Select → Nodes → Select All Nodes OR CTR-A - Sort by Drug Target (Click Column Name) - Select all rows with non-empty Drug Target cells - Right click and select from table
122. Attribute Batch Editor - Now sort by Group and select from table again - Now the selected nodes are druggable AND cancer genes. - Set values by Attribute Batch Editor. - Select Druggable genes again - Select rows with empty Group cells. These are druggable, non-cancer genes. - Group:* selects all
124. Real World Problems - Data files are not always clean! - ID Mapping - Data cleansing - Scripting - Excel - Solution - Semantic Web?
125. Lesson 4: Summary - Combination of browser and EnhancedSearch is very powerful - Create new attributes from your selection - Can be used for your visualization
126. Visualization
127. Goal of Scientific Data Visualization - Help scientists to understand their data sets - Tell a STORY!
128. Don’t be Too Cool! - Cool visualizations are sometime useless for other scientists - e.g. 3D is network view is cool, but in most cases, it is useless - Balance coolness and meaningfulness
129. Large Scale Visualizations are Pointless in Many Cases
130. Effective Visualization for Non-Designers
131. I am NOT a Designer! - But still, you can improve your visualization by following some simple rules - Info-Graphics != Data Visualization - Art : Science - Infographics 6:4 - Scientific Visualization 2:8
132. Principles - Tell a story by visualization - What’s most important?
133. - Excellent resource for data visualization Tamara Munzner’s Web Site http://www.cs.ubc.ca/~tmm/
134. Effectiveness Principle Encode most important attributes with highest ranked channels [Mackinlay 86]
135. Jock Mackinlay. 1986. Automating the design of graphical presentations of relational information.ACM Trans. Graph. 5, 2 (April 1986), 110-141.
136. Position for Grouping
137. Color for Expression Values
138. Edge Weight to Width
139. Channels are NOT Equal! - Understand human perception - Use proper channel for proper data
140. Resources - Jock Mackinlay. 1986. Automating the design of graphical presentations of relational information.ACM Trans. Graph. 5, 2 (April 1986), 110-141.
141. Tips
142. Tip 1: Don’t use too many colors - Simply awful - Hard to understand - Doesn’t tell anything!
143. Tip 2: Use Opacity Effectively
144. Tip 3: Move Label Position
145. Lesson 5: Effective Visualization
146. Elements of Network Data Visualization - Layouts - Coloring
147. Layouts
148. Layouts
149. Automatic Layout - Choose proper algorithm - Tree-like data - Hierarchical Layout - Scale-Free Network - Force-directed - Circular process - Circular Layout - Tweak parameters if necessary
150. Manual Layout - Tweak result from automatic layout - Scale - Align - Rotate
151. Coloring
152. Coloring - Relatively lower accuracy channel - but still very important in network visualization - Emphasize: - Changes, Differences, Importance
153. VizMapper
154. Start from a Clean Slate
155. Map Attributes to Visual Properties
156. Map Attributes to Visual Properties
157. Map Attributes to Visual Properties
158. Map Attributes to Visual Properties
159. Map Attributes to Visual Properties
160. Visual Style - Collection of mappings from Attributes to Visual Properties
162. Default View Editor
163. Default View Editor
164. Default View Editor Discrete Mapping Editor
165. Default View Editor Discrete Mapping Editor
166. Default View Editor Discrete Mapping Editor Continuous Mapping Editor
167. Default View Editor Discrete Mapping Editor Continuous Mapping Editor
168. Default View Editor Discrete Mapping Editor Continuous Mapping Editor
174. Case Study: Expression Matrix Analysis - http://opentutorials.cgl.ucsf.edu/index.php/ Tutorial:Basic_Expression_Analysis_in_Cytos cape-Human - The result view is not a good visualization... - Can you guess why?
175. Tips
176. Avoid Data Overload - Mapping too many attributes makes your image awful! - It is hard to see the overall trend if too many channels are used in a image
178. Tiling Time-Series Data - Use same network, with same layout - Copy multiple instances of the network - Tile them and apply different Visual Styles
180. Lesson 5 Demo
181. Beyond Basics
182. Introduction to Apps
185. http://apps.cytoscape.org
186. Apps - Extending Cytoscape features - 140+ for version 2.x series - Lots of categories - (Almost) all of them are free, so just play with it to learn what’s possible
187. Pathway Visualization KEGG Pathway (TCA Cycle) visualized by Cytoscape KGMLReader
188. Module Finding
189. Data Import
190. A Must Read A travel guide to Cytoscape plugins Rintaro Saito, Michael E Smoot, Keiichiro Ono, Johannes Ruscheinski, Peng- Liang Wang, Samad Lotia, Alexander R Pico, Gary D Bader, Trey Ideker (2012) Nature Methods 9 (11) p. 1069-1076
191. Installing Apps - Easy - Just install from App manager. - For browsing, just visit App Store - http://apps.cytoscape.org/
192. Advanced Topics
193. Advanced Topics - Cytoscape 3 - Differences - App Development - Collaboration with NRNB - Google Summer of Code
194. Cytoscape 3
195. A KPNA3 HRAS BUB1 EN FAM175A CDK8 ARIH1 CHGA ELAC2 FBXW4 FGF11 EP400 UBA1 GTF2F1 HERC2 MLH1 GTF2E1 TRIM28 HSD17B1 TAF2G PLK1 AURKA BIRC5 NEK2 YRDC PLK3 RNF2 JUN DNAJA3 CDK1 CREBBP CCNA2 ORC2L ID4 CDC25C TRRAP NEK10 XRCC3 ACACA ORC3L C11orf30 GMNN C17orf70 CASP3 PGR CHGB 9606.ENSP00000358154 RNASEL RCHY1 UIMC1 CDK13 RARB FAM175B ERBB2 PMS2 STK11 SMAD3 TP53 FANCE FANCC CHD8 RAD17 WHSC2 MED13 HIST1H4A RNF53 CREB1 PSAP MAP2K1 RPA1 WWP1 ANTXR1 PALB2 BRCC3 PEG3 FBXL7 JUND AP2B1 FLI1 TOPBP1 ECHDC1 MSH2 FANCB TSPAN9 DCUN1D1 FBXO25 MED17 CDC25A PARP2 RNF31 ELK4 TOX3 ECT2 CDK4 PPP1CA SMARCB1 SMARCC2 FBXO11 HYRC GSTP1 SMC1A MED24 PIAS4 BATF MAD2L1 PIAS1 SUMO2 CNR1 MDM2 PMS1 RNF8 SUGT1 UBA52 ELK1 MELK UBB RBM LMO7 WT1 NBR1 PPP1R3A ERCC4 RBL1 ERCC1 XRCC5 MRE11A RNF144B ETS1 WRN NMI SHFM1 EZH2 ERCC2 TUBB2A MAP3K4 HIC1 PML DCAF11 CDKN1A APLP2 CDC45 TSPAN17 NUP153 EGFR FBXL3 CTCFL SMARCE1 GATA3 CHEK2 TOP3A EIF4G1 DDB1 PAX6 KRT14 PPT1 CDK16 SMARCD2 SMARCC DHFR TUBA4A IGF1R RNF168 BRCA1 PPM1D JUNB MED21 FANCA HMMR CDKN1B NUSAP1 RAD9A
196. MED13 SMARCC1 EP300 MAP3K3 GFI1B NUFIP1 GTF2F1 ENSG00000234012 G5A EP400 CBLL1 RAD51 CHGB ID4 WHSC2 FBXW4 BAP1 BRE CDK8 NCK1 8KDE69BZMi87N7sAF1369Ab14So-2 Q8NHQ3 Brca2 uBEqgVbNZFpKdFvwcyL6sloSw0g9606 GSTP1 Ncoa2 FANCL LDpqfKYgCfLDptQIUCIlr9Yfwb49606 UBB Dd2vI8mq8orSfrL52KvLX4sD/Ho-2 BRCA1 SMAD4 PPP1CA 2099 3725 2005 9972 10111 1487 9606.ENSP00000358154 3838 9412 9093 140690 4609 PAXIP1 JUN RNF2 CSTF2 GPR3 ENSG00000137337 MZT1 BMI1 VCP G5A MGF BARS RPB2 RBBP4 RIM PP-1A EWS-ATF1 JUND OSRC RCC JTK3 RBBP3 X2HRIP110 DKFZp686B04100 IPOA1 BRAP LMO4 CLSPN DHX9 RBBP7 E2F4 hRad50 HDAC2 MAP3K3 hSNF2a DBP AURKB HIST1H4L FA-H MLH1 BLM JAK2 LCFS2 HD1 LAT H2A/X WNT2B UBE2I UBE3A UbcM4 HSPA8 MRPL39 TRIP2 SP1 DKFZp686L20222 PEG3 DKFZp686N23123 DDIT1 SMARCA4 MDC1 DEF1 TRRAP hSNF2b BRCC1 NUP120 TERF2 PP1425 FIP p85-ALPHA CCNA MGC131997 XRCC3 SKP2 RSTS 6657 BRCA2 010204 000008035 6 6 FP00000007136 00000012830 SAP Cdk1 Ccnd1 Ercc1 Racgap1 Topbp1 Aspm RPA2 Immp2l FANCD1 Aurkb peptide-linker Birc5 HNRNPL efqpO06SDGYmzwbMpJ+t06UDlsY MPP-2 JXK67Oy2Ny+i96e1jAFkVJTCF50 DD2eG+gmXMcK4KCVbtT54i9U1xc9606 USF1 2lutkxFaFwUJS+5PatDg/qCXa/8 JsH9sHdvJhJOYXW58i3HjQWBu0E FANCD2 WDR16 +2eXiWJK61JwsRevyDVe+z6LKIg UBE2D1 NUP188 RPL31 MAP4K4 ABL1 MED17 TERF1 Q8IZU3 NUP2 C13orf15 dl6L440zevhf86Ba5SdPQ3cStkQ9606 ZNF350 HMG20B Bap1 PDS5B MED21 bAL7aWxtvUXKdB1zZ+HHMKM9FT89606 FLI1 ELK4 KIF1B MLP2 RPA1 MTA2 SMC3L1 CCR4 FYN MTA2 NEK1 XRCC9 ETS1 OQtyYL8HGbvuX0heZ5qCycS8zI8 kEU79P2/HWd7/1I2NJOLvFiSlBQ nDnqwbN+UgKzMvfaP0EIekAhiZQ Cyclin 02nZZfkeIi3mdZR6KqEjgW4gIO0 MAPK14 CCNE1 ERBB2 SMAD3 PGR XRCC3 HMMR PSMD14 ERCC1 PSMB3 NCOA2 MPP3 FLNA CAMKK1 E2F6 BRD7 FBXO8 FBXO10 FGFR2 MLH1 GADD45A RAD51AP1 PTEN SMAD1 HIC1 UBC RAD52 AURKB CBX1 MEN1 MAD1L1 JAK2 MDC1 SLC4A7 C11orf58 RASEF ACVR1B MAP2K4 TSSK3 OPTC PLEC LPHN3 PALLD BCCIP MLH3 XPA PHF21A SP1 ATRX NFKB1 CHEK1 MSH3 PMS2 RAD54B TP53 CHEK2 DEPDC1B FANCB PLK1 NBN BACH1 CDH1 RB1 ESR1 RAD50 FANCM MCPH1 DPH1 PARP1 HRAS MAP3K1 LIG3 ERCC4 STAT5A BLM 2962 207 SIAH3 RNASEL 7503 CCND1 BUB1B DMC1 Mad2l1 Fancc Rad51l1 Dmc1 SUMO1 Mgmt Ppp2r5b Bub1 Pten Bard1 Brca2 Cdca7 Trp53 Prdx2 Tcp1 Chek1 Fanca Rb1 1 qo2 Nfkb1 Slco4a1 Hmmr LSP1 NEK10 UBE2D3 ATM FANCI CCNA2 TP53BP1 AR FANCD2 H2AFX RPA2 PMS1 FHIT RAD51L3 PIK3CA LIG4 ELAC2 9448 XPC XRCC4 CDKN1A MSH6 SMAD4 RAD51C FANCA CDKN2A TIPARP BRCC3 SMAD2 AURKA INSC XRCC1 RAD51 TWSG1 PALB2 MRE11A BUB1 STK11 HYRC CDK1 C17orf70 UBA52 CYP17A1 641 C11orf30 MSH2 C19orf40 TOX3 FBXL7 WRN KLHDC8B 7321 ECHDC1 XRCC2 6597 10499 C5orf4 UIMC1 FAM175A 51720 59348 595 GATA3 FANCC XRCC6 FANCF XRCC5 Uba52 Men1 Pes1 Brca1 Nme2 Cdc45l Fam175a Twsg1 KPNA6 Fancm Sumo2 GCGR Terf2 DHX9 DNAH8 NEK2 Ezh1 Trp53bp1 ASPM Lmo4 853808 3717 PRDX2 SRD5A2 PPP2R5B CDCA7 COX11 3716 KIF4A NCOA3 HSD17B4 PSMD3 ZMYND11 RNF146 ZNF217 RBBP8 TOP2A TGFBR2 CSNK2B WDR16 ARL11 MND1 ZAR1L SERPINH1 ARIH1 CNTLN EZH1 851212 855055 CDKL4 RNF20 ASB9 DHX32 RAD23B USP11 PSMD6 GGNBP1 UBE2H UBE2L6 PRSS1 SKP2 SERAC1 DAPK1 BRAF RAD51L1 6599 9969 BRCA2 ECD BRCA1 6772 000024255 SCAFP00000026442 GALK1 9615.ENSCAFP00000023980 5.ENSCAFP00000028394 0028192 9615.ENSCAFP00000025953 9615.ENSCAFP00000035179 9615.ENSCAFP00000024343 BRCA1 9615.ENSCAFP00000030064 9615.ENSCAFP00000029451 POLR2A 9615.ENSCAFP00000030922 9615.ENSCAFP00000028763 Cks2 Obox3 Pik3ca 9615.ENSCAFP00000007566 Shfm1 9615.ENSCAFP00000006799 Tiparp Lphn3 9615.ENSCAFP00000027562 AFP00000023734 9615.ENSCAFP00000025994 9615.ENSCAFP00000028888 ADRA2B CCNA1 CREB1 CCNB1 TUBG MSH6 ATM MGC163290 3312 VCP 367 BRCC3 CDK4 BACH1 CSNK2A1 NCOA2 NMI 9555 PPP1CB POU2F1 HsT16930 ORC2 PDS5B CTCFL CCDC DNAJA3 Anti Zuai-1 FLNA VIM HMG20B PLK1 NCKalpha SMARCA2 EMSY HIST2H2AC SMARCC2 KAT2A SMAD3 PPP1CA RBL1 RBL2 UBA1 SMARCB1 AR H2AFY ACACA ABL1 SUB1 POLR2A MED23 GTF2H4 PRKDC NPM1 TOP2A MSH3 SMARCD2 CDK5 MED24 FANCD2 FHL2 ARNT CSTF2 CDKN2D TRP53 NF-kappaB FLJ12847 852199 ATR p150 853957 KPNA2 CDK8 851470 CCND1 SUMO1 853811 APLP2 HIST2H3A SRC MRPL36 6603 GTF2F1 MGC141879 Piasg SPT5 SPT4 DKFZp762J2115 oJw0JiiF0ME7x7eSGqUXn1ujDsg 79728 1020 OPD1 7415 898 UCpeqyqxh zU q6W0BO8vHuuZh PRM TzbYjLhQfYYis5ZHoOIz1MogPM CCNA2 CHEK2 ATDC DHX9 MYC AKT1 BRIP1 ZCCHC8 6601 2968 SSK1 PALB2 MGC45350 TOK1 S3 TP53 STAT5A DSS1 BARD1 CDK2 Ifi204 UQCC RFC1 NUP170 DHH1 FANCE ETS1 POM34 1019 USP1 RAD23A ROCK2 B3GALTL ZFHX3 TBX2 eKcZNhe4o3Sj9t+CWeVB2UBxvgk9606 9862 BRCC2 8202 9439 2Nu9AAWbBoSXW1y9FPjEO6691mU9606 CDK1 5981 983 BRCA1 KIAA0204 SGA-113M HHR23A RPA1 CDK16 Brca2 GTF2E1 ESR1 ASH RNF11 891 NUP153 SHFM1 NUP133 ASM4 KAT2B FANCG 1874 4800 CDC45 dJ199H16.1MGC149819 RAD21 6595 2175 HjbdLaovED6ZlK/153JO9Hsm86o FN+kJjmaxWZr/lWSN4mXugd0TPc 5GNzWn6eJ41h8viIYZEnxs7GUYI vDCqLkifRBymdYzE8Q8boNEF1ZY Vhaq0CUSlWCGivG2ObT5Xa7IB+E BUB1B p65 MCPH1 BCCIP FLNA CHK1 USP11 CCNE1
198. Cytoscape 3 Demo
199. HUGE Update C 2 3
200. What’s New in 3? - New Visualization features - Edge Bend, Background Images - Server-side applications - Headless Mode (in beta status) - More advanced visualization by new rendering engines - For developers: Cleaner API
201. Data Model Differences - Cytoscape 2.8 - One network. All other networks are projections on that network. - Essentially a rooted tree - No way to duplicate nodes without sharing attributes - Cytoscape 3 - Allows multiple roots - Can have multiple trees - Each group of networks that shares a single root is called a collection
202. Network 1 Network 2 SUID Shared Name Attr1 00001 Node 1 00002 Node 2 00003 Node 3 00004 Node 4 Shared Node Table SUID Name Shared Name Attr1 Attr2 00001 Node 1 Node 1 00002 Node 2 Node 2 00004 Node 4 Node 4 Network 1 Local Node Table SUID Name Shared Name Attr1 Attr2 00001 Node 1 Node 1 00003 Node 3 Node 3 00004 Node 4 Node 4 Network 2 Local Node Table
203. Upcoming Features - Official Release of Headless/Daemon Mode - Multiple Rendering Engine Support - Export session to cytoscape.js
204. App Development - Requires following skills: - Java - Maven - OSGi (for 3)
205. App Development - For new features, we recommend to implement for 3.x series - Two ways: - Simple App (Similar to 2.x development) - Bundle App (Essentially, an OSGi bundle)
206. Developer Documents - http://opentutorials.cgl.ucsf.edu/index.php/ Portal:Cytoscape3
207. Google Summer of Code
208. Google Summer of Code - Open-Source development opportunity for undergraduate / graduate students - Sponsored by Google - Students will be payed by Google - Please keep watching their web site - Application period for this year is mid April
209. github.com/cytoscape
210. Collaboration - Once you are ready to use Cytoscape for real-world problems, National Resources for Network Biology (NRNB) is always open for collaboration! - NRNB Provides support for both of - Scientific Research - Application / Tool Development - nrnb.org
214. What’s Next? - Cytoscape 2.x Tutorials - http://opentutorials.cgl.ucsf.edu/ index.php/Portal:Cytoscape
215. Getting Help - Two Google Groups - cytoscape-discuss@googlegroups.com - cytoscape-helpdesk@googlegroups.com - ANY question is OK! - Question about slides: kono@ucsd.edu
216. We Need Your Feedback - Cytoscape is an open platform developed by many research organizations - Improving user experience is an important TODO item! - Please send us your idea to improve UX
217. Open Q&A Session
218. Further Readings 1 - Introduction to Network Biology - Deciphering Protein–Protein Interactions. Part I. Experimental Techniques and Databases Shoemaker BA, Panchenko AR (2007) Deciphering Protein–Protein Interactions. Part I. Experimental Techniques and Databases. PLoS Comput Biol 3(3): e42.doi:10.1371/journal.pcbi.0030042 - Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners Shoemaker BA, Panchenko AR (2007) Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners. PLoS Comput Biol 3(4): e43. doi:10.1371/ journal.pcbi.0030043
219. Further Readings 2 - Overview of Cytoscape Apps (Plugins) - A travel guide to Cytoscape plugins Rintaro Saito, Michael E Smoot, Keiichiro Ono, Johannes Ruscheinski, Peng-Liang Wang, Samad Lotia, Alexander R Pico, Gary D Bader, Trey Ideker (2012) Nature Methods 9 (11) p. 1069-1076 - Sample Protocol − Integration of biological networks and gene expression data using Cytoscape Cline, et al. Nature Protocols, 2, 2366-2382 (2007).
220. Further Readings 3 - Cytoscape Tutorial Booklet: Analysis and Visualization of Biological Networks with Cytoscape - http://www.rbvi.ucsf.edu/Outreach/Workshops/ISMBTutorial.pdf
221. www.cytoscape.org
222. 2013 Keiichiro Ono kono@ucsd.edu