Introduction to Biological Network Analysis and Visualization with Cytoscape Part1

1. Introduction to Biological Network Analysis and Visualization with Cytoscape Keiichiro Ono Cytoscape Core Developer Team UC, San Diego Trey Ideker Lab / National Resource for Network Biology 5/10/2016 The Scripps Research Institute Lecture 1: Basics

2. Keiichiro Ono Cytoscape Core Developer since 2005 @UCSD Trey Ideker Lab Area of Interest: Biological Data Integration & Visualization

3. Agenda • Lecture 1 (Today):    Introduction to Biological Network Analysis and Visualization • What is the beneﬁts of biological network analysis and visualization? • Introduction to Cytoscape • Preview of Lecture 2: cyREST • Lecture 2:    Reproducible Analysis & Visualization • Introduction to Jupyter Notebook • Create a reproducible network visualization workﬂows with Python

4. All documents, data, and code are available here: https://github.com/idekerlab/tsri-lecture

5. Why Network Analysis?

6. Networks?

7. EP300 PPARG SMARCD3 STMN1 SMARCA4 OPTN ATP6V1C1 PSMD1 HTT PRNP HNRNPUL1 CCDC88A CLU HSP90AB1 SMARCD3 MAP4K4 MIF4GD USP11 MARCH6TUBB EDF1 CHD8 Protein-Protein Interactions

8. Human-Curated Pathways KEGG Pathway (TCA Cycle) visualized by Cytoscape KGMLReader

9. Interactomes Human Interactome data from BioGRID visualized with Cytoscape

10. Social Networks

11. Network extracted from Panama Papers data set

12. Node 1 - Edge Type - Node 2

13. Protein 1 - Y2H - Protein 2

14. Networks vs Pathways

15. Networks Pathways

16. Networks Pathways Collection of binary interactions Human-curated / detailed Large Scale Small Scale Generated from omics-data Constructed from literature

17. Networks / Pathways = Graphs

18. Beneﬁts of Network Analysis

19. Beneﬁts of Network Analysis - You have list of N genes from your screening - Now you want to know: - Relationships among those genes - Functions - etc. Screening 1 PPARG TCF7L2 RETN IRS1 HNF1A HNF4A KCNJ11 GCK LIPC PTPN1 ABCC8 ENPP1 HNF1B

20. ENSG00000167780 ENSG00000255974 EBI-9992455 ENSG00000241119 ANP32B EBI-10055098 EBI-10055672 EBI-9871829 NR3C1 EBI-10096648 STK16 SNCG EBI-10034984 EBI-9973444 RAD50 EBI-9980932 ENSG00000070019 EBI-9871836 ENSG00000105398 EBI-9992700 ENSG00000207778 ENSG00000143819 EBI-9980935 ENSG00000125730 ENSG00000180432 ENSG00000197249 EXT2 EBI-5333164 EBI-10051521 OPTN EBI-10050213 PPARGC1A EBI-10039585 MAPK8 HNF4A EDF1 SFPQ ENSG00000110245 PPARGEP300 SMARCD3 PRNP HNRNPUL1 ATP6V1C1 HTT EBI-10050241 EBI-10039564 ENSG00000118137 PABPC4 EBI-10050232 EBI-10051518 EBI-9871840 EBI-10096653 ENSG00000095596 MARCH6 EBI-3946155 EBI-5333185 BCAR3 IRS1 PIK3CA LRRK2 Irs1 PIK3R1 PIK3R1 Grb2 Phb Grin1 H1f0 Rps3 Rpn2 Ipo9 Scml2 Cand1 Eif2ak2 Ipo8 Ppﬁbp1 Sdpr Poldip3 Tenm3 Rars Ppp2r1a Vim Rfc3 Hsp90ab1 R q c d 1 Rplp0 Hnrnpu Irs1 Snd1 Hspa8 Ung Tp53bp2 GRB2 TP53BP2 YWHAB IGF1R DDR1 SMAD3 EBI-1108795 Ctnnd1 Ran Ywhag Rab6b Ybx1 Epha2 Grin2b Flot2 Aff3 Ptpn1 PELI1 EIF2AK2 INSR YWHAE NCOR1 Pik3r1 N U M B PRKCQ TP53BP2 PIK3R3 RAP2A Mink1 CHEBI:39112 Ywhab CHEBI:39079 CDK5 STMN1 PPARGUSP11 CHEBI:45783 CHEBI:18319 HSP90AB1 MAP4K4 MIF4GD CHEBI:64310 CHEBI:49840 TUBB CLU CHD8 CCDC88A SMARCA4 SMARCD3 PSMD1 MAP4K2 MINK1 Map3k1 MAP3K1 PKM MAP3K11 CHEBI:49375 GOLIM4 ARHGAP39 GOSR1 BCOR PTRF PCNP KIDINS220 TMEM216 PHB ABCD3 1C ATP6V0D1 FLOT2 RNF213 SMARCD2 LTN1 PIAS1 Sumo1 ATP6V0A1 WRAP53 EVC2 ACTN1 GALNT2 dhbF B4E2V5 TCTN3 GHR EGFR ATP6V1B2 CDH2 TMEM17 RMDN3 PHB2 PTPN1 Cdh2 TCTN2 PDGFRB ATF2 CHEBI:17440 mviM1 MET MVP MSN MREG COL5A1 FLOT1 ASS1 CHEBI:17283 uvrB FOXM1 tyrB UBE2I glnA Ctnnb1 PSMA3 ENSG00000138795 CTNNB1 RUNX3 PSEN1 JUP DAXX TCF7L2 hmwP2 hemL2 vgrG7 fadB FBLN1 XRCC6 XRCC5 PARP1 Q99IB8-PRO_0000045599 pagA Psen1 ENSG00000168646 ENSG00000065361 p p s C Q99IB8-PRO_0000045596 Q8CLD5 TGIF1 YPO2975 p g i pyrE ENSG00000110092 ENSG00000136997

21. Beneﬁts of Network Analysis - You can see the relationships among the group of biological entities - Find drag targets - Overrepresented functions AND their connections

22. Gene List to Network to Biological Insight - How? - You need to search, integrate, and visualize multiple data sources This is what you will learn in this lecture

23. What is Cytoscape?

24. An Open Source Platform for Biological Network Data Integration, Analysis and Visualization Cytoscape

25. Cytoscape 3.4.0 (Latest Release)

26. Cytoscape - Open Source (LGPL) - Free for both commercial and academic use - Developed and maintained by universities, companies, and research institutions - UC, San Diego - University of Toronto - UC, San Francisco - ISB - And collaborators world-wide

27. Cytoscape - De-facto standard software in biological network research community - Large User and Developer Community - Expandable by Apps - This is why Cytoscape is a Platform, not a simple desktop application

28. C. Elegans Interactome from BioGRID Database ?

29. Biological Networks - Tell us anything by themselves - Just a big hairball…

30. Module 1 Module 2

31. In other words…

32. Module 1 Need a tool to extract meaningful biological modules

33. Basic Use Case

34. Networks Public Interaction Databases

35. List of Genes

36. Other Data

37. Network Data Analysis Analysis Graph Analysis NetworkX igraph Cytoscape Python Pandas NumPy SciPy Excel Visualization Desktop Gephi Cytoscape matplotlib Web Cytoscape.js sigma.js d3 NDV3 d3.chart Google Charts Data Storage Graph Neo4j GraphX Document MongoDB Relational MySQL IPython 3rd Party Apps NetworkAnalyzer

38. Network Data Analysis Analysis Visualization Desktop Gephi Cytoscape matplotlib Web Cytoscape.js sigma.js d3 NDV3 d3.chart Google Charts Data Storage

39. Network Data Analysis Analysis Graph Analysis NetworkX igraph Cytoscape Python Pandas NumPy SciPy Excel Visualization IPython 3rd Party Apps NetworkAnalyzer

40. Three Basic Steps for Data Visualization with Cytoscape

41. <?xml version="1.0" encoding="UTF-8"?> <graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">  <key id="degree" for="node" attr.name="degree" attr.type="double"/> <key id="betweenness" for="node" attr.name="betweenness" attr.type="double"/> <graph id="G" edgedefault="directed"> <node id="n0"> <data key="degree">79</data> <data key="betweenness">0</data> </node> <node id="n1"> <data key="degree">9</data> <data key="betweenness">167</data> </node> <node id="n2"> <data key="degree">18</data> <data key="betweenness">75</data> </node> <node id="n3"> <data key="degree">8</data> <data key="betweenness">12</data> </node> <node id="n4"> <data key="degree">26</data> <data key="betweenness">210</data> </node> <node id="n5"> <data key="degree">29</data> <data key="betweenness">320</data> </node> Data Integration

42. Analysis

43. Visualization

44. Drawing Biological Networks VS

45. Drawing Tools You need to specify color of each node, width of each edge, shape of nodes, etc.

46. There is one huge diﬀerence between Cytoscape and Illustrator…

47. In Cytoscape, Your Data Controls View

48. Creating Visualizations in Cytoscape Name Type BRCA1 gene MAP2K1 gene C05981 compound • Mapping from Type to Node Shape • Mapping from Type to Node Color C05981 BRCA1 MAP2K1 Creating mappings from data points to Visual Properties

49. Network Data Annotated Networks Attributes Analyzed Data

50. Apps

51. Cytoscape Apps - Extension programs to add new features to Cytoscape - formerly called Plugins - Large App developer/ user community - This is why Cytoscape is so successful in life science community!

52. Example Apps

53. ClueGO Creates and visualizes a functionally grouped network of terms/pathways

54. ReactomeFIPlugIn Explore Reactome pathways and search for diseases related pathways and network patterns using the Reactome functional interaction network

55. KEGGScape KEGG Pathway Importer for Cytoscape

56. clusterMaker2 Multi-algorithm clustering app for Cytoscape

57. cyREST (Now Part of The Core!) RESTful API for Cytoscape

58. cyREST RESTful API for Cytoscape

59. APPS.CYTOSCAPE.ORG

60. Overview of App Ecosystem 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

61. Tips for Learning New Tools

62. Choose a Right Tool

63. Choose a Right Tool Analysis VisualizationData Preparation

64. Data Visualization Tools http://selection.datavisualization.ch/

67. Tools • In some cases, you can get exact same result using different tools • Example: Data preparation (cleansing / cleansing) • But if you choose right tools, you can do it 100x faster than others. • ex: Re-formatting complex data sets • Excel vs Python Script • Some recommendations: • R/Bioconductor, Python/Pandas, Git/GitHub/Gist

68. Learning Tools = Saving Your Time

69. Hands-on: Introduction to Network Visualization with Cytoscape

70. Sample Data Files: http://cl.ly/VTJs Same data is available in lecture1/dataset

71. Data Visualization

72. - Goal: Help others to understand your data - Emphasize what you want to tell - Use color, shape, size of objects eﬀectively! - Excellent resource for data visualization - Tamara Munzner’s Web Site:   http://www.cs.ubc.ca/~tmm/ Data Visualization

73. Today’s Goal

74. Story: I want to show gene expression changes over time as a network diagram

75. YPL201C YPL211W YML007WYPL131W YOR327CYDR171W YCL067C YGL208WYER074WYBL050W YLR134WYPL149W YDR050C YMR311CYGL134W YBR112CYKL101W YNL199C YPL222W YLR264W YNL098C YLL028W YOR039W YNL135C YPR041WYDR174W YIL074C YKL028W YIL162W YNL189W YOR212W YPR080W YPR145W YLL019C YLR284CYPL031C YFR037CYML074C YPL240CYPR048W YBR274W YBR050C YML032C YJR022WYBR248C YDR382W YER081WYIR009W YDR244W YOL016C YER103W YGR058WYLR256WYAL003W YOR355WYIL061C YER111C YMR309C YPL248CYBR019CYLR362W YGL035CYPR167C YML123C YBL026WYNL091W YOR178C YIL113WYLR321C YML064C YMR117C YDL194WYNR007C YOL058WYBR045CYER065CYNL167C YGL097WYHR071W YDL078C YDL081CYDR354W YER145C YGR136WYDR311W YPR119WYER112W YLR214W YER143W YBR043CYKL204W YGR019WYEL041W YER133W YBR118WYAL038W YDR167WYMR058WYER079W YMR291W YKL012W YDL113CYDR299W YDL075W YDL236WYLR377C YNL145W YNL236W YOL156W YGL013C YHR171W YMR021C YFL038C YER090WYPR062W YAR007C YNL307CYML024WYDR335W YLR075W YNL050CYGR046W YAL040CYLR191W YMR138WYIL045W YHR005C YKL211CYLR452C YPL075WYML051W YOL123WYHR198C YMR300C YJR060W YMR043WYPR124WYLR081W YLR319CYKL074C YKL001C YDR100W YDR395W YDR009W YDR309C YPR102C YAL030W YHR084W YLR345W YBR170C YJL089WYFL026W YBR018C YGL115W YDL215CYGR009C YOL120C YFL017C YDR429C YIL052C YGL073W YGR108WYPR035W YJL190CYOL086CYBL005WYKR026C YBR155W YOR264W YKL109W YOR167C YDR070CYEL015W YIL133C YGL166WYHR030CYGL008C YMR146C YBR160W YBR020W YBR190WYDR323CYLR197W YFR014CYKL161C YML054C YKR099WYLR340WYGL106W YBR093CYCL040W YLR044C YCR086WYDL130W YJL203W YEL009CYBR135W YOR361C YGR085C YNL216W YBR109C YER124C YJL157C YDR461WYNL154CYLR117C YKR097W YIL069CYMR186W YJR109CYIL015W YER040W YGR074WYER052C YIL160CYOR290C YLR249W YGL153WYOR215CYGR254W YLR432WYCR084CYOR089C YOR303W YGL161C YLR293CYDL030WYNL036W YHR135CYER179W YDR277CYDR184C YML114C YFL039CYER054C YER110CYLR109W YLR116WYNL214W YBL069W YHR141CYER116CYJL219W YDL023C YGL202WYER062C YMR183CYFR034CYGL122C YIL105C YDL088CYPR010C YJR048W YIL070C YEL039CYDR412WYMR108W YOR204W YMR255W YLR175W YHR115CYNL164C YJL013C YDL063C YNL117W YIL143CYOR315W YDR146CYLR310CYGR014WYBR217W YJL036W YNL116W YOR120W YDR032C YPR113W YLR153C YGR048W YGR203W YNL113WYOR202W YNR050C YCL030C YJL159W YHR053CYPR110C?YLR258W YBL079W YNL069C YNL311CYDR142C YGL044CYMR044W What is Great Visualization…?

76. Design is complicated, because humans are complicated. Design is a process to avoid bad designs. Mike Bostock (New York Times Visualization Team. Creator of D3.js)

77. It is hard to generalize the design process, but we can avoid pitfalls by following some basic rules.

78. Every pixel should carry information. Edward Tufte

79. Avoid Data Overload • Mapping too many attributes makes your visualization awful! • It is hard to see the overall trend of your data sets if too many channels are used in a image

80. “Great Artists Steal…”

81. MUD HAP4 GC HA GAL1 GAL7 GAL80 GAL3 GAL11 GAL4 GAL2 SIP4 FBP1 GAL10 SWI5 SUC2 MIG1 ADH1 PGK1 CDC19 GCR1 CBF1 ENO1 ENO2 MCK1 NCE103 SSL2 TFB1 YNL091W TRP4 ARG1 GCN4 SKO1 HIS3 ADE4 ILV2 RPS17A BAS1 HIS7 RPS24B MSL1 HIS4 PDC5 PHO84 PHO4 YIL105C MET16 RPL11B RPS8B RPL11A RPL31A PHO13 PDC1 SXM1 RPL34B RPL16B ATC1 CAR1 FCY1 ICL1SRP1 TPI1 RPL18B RPL25 PHO5 RPS24A RPL18A DMC1 RAP1 RPL16A HSP42

82. MUD HAP4 GC HA GAL1 GAL7 GAL80 GAL3 GAL11 GAL4 GAL2 SIP4 FBP1 GAL10 SWI5 SUC2 MIG1 ADH1 PGK1 CDC19 GCR1 CBF1 ENO1 ENO2 MCK1 NCE103 SSL2 TFB1 YNL091W TRP4 ARG1 GCN4 SKO1 HIS3 ADE4 ILV2 RPS17A BAS1 HIS7 RPS24B MSL1 HIS4 PDC5 PHO84 PHO4 YIL105C MET16 RPL11B RPS8B RPL11A RPL31A PHO13 PDC1 SXM1 RPL34B RPL16B ATC1 CAR1 FCY1 ICL1SRP1 TPI1 RPL18B RPL25 PHO5 RPS24A RPL18A DMC1 RAP1 RPL16A HSP42 Map gene expression values to color Avoid using more colors in other components (edge/label) If necessary, map other data into non-overlapping visual properties (edge score to width)

83. Part 1: Session File and Basic Navigation

84. Cytoscape 3.4 Desktop Toolbar Network Panel Bird’s Eve View Table Browser Network Views

85. Local Column Table Tabs List Data  (Values in [ ]) Shared Column

86. Session File - Snapshot of your workspace - Networks - Tables - Visual Styles - System Properties

87. Open a Session - Click folder icon - Or, File → Open

88. Exercise 1: Loading a session

89. Navigation - Pan: Drag - Zoom - IN: Mouse Wheel UP - OUT: Mouse Wheel DOWN - Selection: Shift + Drag - Fit to Window - Selected region - Entire network

90. First Neighbor of Nodes CTR+6

91. Create New Sub-Network From Selection CTR+N

92. - Grid View

93. - Detached View

94. Part 2: Data Import

95. Network Data Formats - SIF - GML - XGMML - GraphML - BioPAX - PSI-MI - SBML - KGML (KEGG) - Excel - Text Table - CSV - Tab

96. NCBI Gene ID 672 On Chromosome 17 GO Terms DNA Repair Cell Cycle DNA Binding Ensemble ID ENSG00000012048 BRCA1

97. Data Tables for Cytoscape - Example: - Numeric - Gene expression proﬁles - Network statistics calculated in other applications, such as R - Conﬁdence scores for edges - Text (or categorical) - GO annotation for genes - List of genes related to disease X - Targets for FDA approved drugs - Genes on KEGG Pathway Y - Clusters / group / community calculated in external programs - …

98. Your Data Sets - Anything saved as a table can be loaded into Cytoscape - Excel - Tab Delimited Document - CSV - As long as proper mapping key is available, Cytoscape can map them to your networks.

99. Mapping Key in the Network Mapping Key in the Table

100. Exercise 2: Loading network and tables

101. Part 3: Visualization

102. Layouts

103. Force-Directed + Edge Bundling

104. Stacked-Node Layout + Default Edge Bend

105. Circular + Edge Bend

106. Automatic Layout - Choose proper algorithm - Tree-like data - Hierarchical Layout - Scale-Free Network - Force-directed - Circular process - Circular Layout - Tweak parameters if necessary

107. Manual Layout - Tweak result from automatic layout - Scale - Align - Rotate

108. Exercise 3: Apply layouts

109. Visual Style - Collection of mappings from Attributes to Visual Properties

110. Visual Styles - Defaults + Mappings - Expression values to node color - Gene function to node shape - Interaction detection method to edge line type - Conﬁdence score to edge width

111. Core Idea: Data Controls The View

112. Data Controls The View • Photoshop / Illustrator • You control the pixels and objects on the display • Data Visualization Tools (including Cytoscape) • Data points are mapped to visual properties • Color • Size

113. Data Controls The View

114. Expression Values To Node Colors

115. Discrete Mapping Editor Continuous Mapping Editor

116. Exercise 4: Create New Visual Style

117. Preview of Lecture 2: Reproducible Workﬂow with Jupyter Notebook and cyREST

118. Cline, Melissa S., et al. "Integration of biological networks and gene expression data using Cytoscape." Nature protocols 2.10 (2007): 2366-2382.

119. Cline, Melissa S., et al. "Integration of biological networks and gene expression data using Cytoscape." Nature protocols 2.10 (2007): 2366-2382.

120. Results

121. Sharing Results 😐

122. Sharing Results and Process 😃

123. Point & Click Operation is Easy, but not Reproducible…

124. Goal: Reproducible Science

125. Goal: Reproducible Science REST

126. Tools You Need

127. REST - GitHub - For source code sharing - IPython (Jupyter) Notebook - Your electronic lab notebook - cyREST - RESTful API module for Cytoscape

128. - - Two Google Groups - cytoscape- discuss@googlegroups.com - cytoscape- helpdesk@googlegroups.com - ANY question is OK! Getting Help

129. Further Readings

130. Further Readings • My presentation slides • http://www.slideshare.net/keiono • This deck of will be uploaded today

131. 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

132. 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 (based on 2.x) − Integration of biological networks and gene expression data using Cytoscape    Cline, et al. Nature Protocols, 2, 2366-2382 (2007).

133. Further Readings 3 - Cytoscape Tutorial Booklet:    Analysis and Visualization of Biological Networks with Cytoscape - http://www.rbvi.ucsf.edu/Outreach/Workshops/ISMBTutorial.pdf

134. 2016 Keiichiro Ono kono@ucsd.edu