Cytoscape Tutorial Session 2 at UT-KBRIN Bioinformatics Summit 2014 (4/11/2014)

Cytoscape Tutorial Session 2 at UT-KBRIN Bioinformatics Summit 2014 (4/11/2014)
Keiichiro Ono Bioinformatics Summit 2014 4/11/2014 Cytoscape Tutorial 2: Advanced Topics
- Effective Visualization with Cytoscape - Use Cytoscape with external data analysis tools - Cytoscape and The Web Part 2: Agenda
- This section is a bit conceptual rather than practical, but it is very important to understand before creating actual data visualizations Part 2: Agenda
Effective Visualization
Now, you know… - Basic features of Cytoscape - How to load network / table data - Basic Analysis / Filtering - Layout - Edit Visual Styles - Ready to create great visualizations!
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 Great Visualization…?
Bad Visualizations - Unfortunately, there are lots of bad data visualizations… - Too many colors - Too many mappings - Lack of Story
YPL201C YPL211W YML007WYPL131W YOR327CYDR171W YCL067C YPL222W YLR264W YNL098C YLL028WYIL162W YNL189W YOR212W YPR080W YPR145W YLL019C YLR284CYPL031C YFR037CYML074C YPL240CYPR048W YBR274W YBR050C YML032C YJR022WYBR248C YDR382W YER081WYIR009W YDR244W YOL016C YER103W YGR058WYLR256W YOR355WYIL061C YER111C YMR309C YIL113W YMR117C YDL194W YOL058WYBR045CYDL081C YER143W YBR043CYKL204W YGR019WYEL041W YER133W 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 YIL133CYMR146C YBR160WYML054C YKR099WYBR093C YCR086WYDL130W YEL009CYBR135W YJL157C YDR461WYNL154C YIL069CYJR109CYIL015W YIL070C YEL039CYDR412W YHR115CYDL063C YNL117W YIL143C YDR146CYLR310C YHR053CYPR110CYBL079W YNL069C YNL311CYGL044C You need to learn how to avoid this and need to make something like…
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
I’m not a designer… - But learning basic principles of design and data visualization is not so hard - Creating 10/10 visualization is difficult, but 8/10 is the goal for us - Let’s avoid pitfalls!
What is BAD Visualization? - Lack of story - What’s the point? - Hard to understand - Too many or too few visual mappings - Ugly
Story (or Goal) - Example: - I want to show the changing levels of gene expression for three time points - Assign gene expression profile to the primary visual property in your visualization
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)
“Cool” does not always mean “Effective” - This is what I’ve learned from my past experiences…
Case Study: 3D Visualization - Background: - In late 90’s, 3D graphics card was cheap enough for entry-level workstations - Many researchers made tons of 3D graphics applications for data visualization
3D Network View by igraph
Carpendale et al. 96. Distortion Viewing Techniques for 3-D Data
What is the Advantage?
…And My Mistakes
Experimental 3D renderer for Cytoscape
Technology Oriented, Lack of Story!
What was the problem? … It would be more accurate to say that visual space has 2.05 dimensions.
Lessons Learned… - Introduce additional dimension / complexity to the visualization only when it is necessary - Animation, 3D, charts on nodes, etc. - Use minimal set of visual channels to make the visualization understandable - Define story (or goal) before creating actual visualization - Understand human perception
Goal of Scientific Data Visualization - Help scientists to understand their data sets - Tell a STORY
- Just follow some simple principles - Info-Graphics != Data Visualization - Art/Design : Science - Infographics 8:2 - Scientific Visualization 1:9 You Don’t Have to be a Professional Designer
What is Good Visualization? http://www.visualcomplexity.com/vc/
- One of the unfortunate trends in data-driven life sciences is that they increasingly use programmers to abstract data so that mundane information looks visually appealing - this is motivated by the desire to appear on the cover of the glossy life sciences journals. - Comment from Wired Magazine article “Circle of Life: The Beautiful New Way to Visualize Biological Data” http://www.wired.com/wiredscience/2013/11/wired-data-life-martin-krzywinski/
An Extreme Example (I’m not saying this is bad, but…)
http://youtu.be/WTHtYZcH6fk
Don’t be Too Cool! - Cool visualizations are sometime useless for scientists - But still good for journal cover page… - Balance coolness and effectiveness - Think about audience (or users if it is interactive)
Visualizing Heterogeneous Data In a Diagram is HARD - Visualization itself is a research area - You should learn about commonly used techniques and principles from experts
Human Interactome data from BioGRID visualized by Cytoscape
Large Scale Visualizations are Pointless in Many Cases
Good Large-Scale Visualizations
Ultimately, you want…
SDHA Tyrosine metabolism FH Arginine and proline metabolism C00149 C00122 K00239... SUCLG2 C15973 Valine SUCLG2 C00091 DLD DLST C16254C00042 C16255DLAT C05125C00024 PDHA1 K01643... C00417C00158 ACO1 ACLY DLD MDH1 C00036 C15972C15973 CS IDH1 C00022 ACO1 PC PDHA1 C00311 Alanine Fatty acid degradation Fatty acid biosynthesis Valine TITLE:Citrate cycle (TCA cycle) Glyoxylate and dicarboxylate metabolism Fatty acid elongation D-Glutamine and D-glutamate metabolism K17753 IDH1 Ascorbate and aldarate metabolism C00026 IDH3A... C05379 Alanine C05381 C15972 K00174... C00068 OGDHOGDH C00074 PCK1 C00068 Glycolysis / Gluconeogenesis K00169... K01610
But this is still useful
Costanzo et al.
Targeting the Audience - Even meaningless (but cool) visualization is useful as a eye-catcher or journal cover page - When you need figures for your publication, minimize the noise in your visualization and keep it simple
Data Visualization Tools http://selection.datavisualization.ch/
Effective Visualization for Non-Designers
- Excellent resource for data visualization Tamara Munzner’s Web Site  http://www.cs.ubc.ca/~tmm/
Resources - Jock Mackinlay. 1986. Automating the design of graphical presentations of relational information.ACM Trans. Graph. 5, 2 (April 1986), 110-141.
Effectiveness Principle Encode most important attributes with highest ranked channels [Mackinlay 86]
Jock Mackinlay. 1986. Automating the design of graphical presentations of relational information.ACM Trans. Graph. 5, 2 (April 1986), 110-141.
Channels are NOT Equal! - Understand human perception - Use proper channel for proper data
Jock Mackinlay. 1986. Automating the design of graphical presentations of relational information.ACM Trans. Graph. 5, 2 (April 1986), 110-141.
In Cytoscape… 1. Position: Node Position 2. Length: Edge Length 3. Area: Node Size, Edge Width 4. Color: Node/Edge/Label Color 5. Density: Node/Edge/Label Transparency
1. Position
Power of Layout
C16255C00074 C00026 C16254 C00068 C05125 Alanine MDH1 Valine Fatty acid biosynthesis C00024 C00036 Fatty acid degradation ACLY Glyoxylate and dicarboxylate metabolism C00022 C00068 DLST DLDPDHA1 SDHA Arginine and proline metabolism FH C00149 Tyrosine metabolism C15973 DLD DLAT C00042 D-Glutamine and D-glutamate metabolism OGDH C00417 Ascorbate and aldarate metabolism ACO1 Alanine C00311 C15972 PDHA1C15973 SUCLG2 C00091 Valine SUCLG2 C00122 ACO1 C00158 CS Fatty acid elongation C15972C05381 OGDH PC PCK1 Glycolysis / Gluconeogenesis IDH1 IDH3A...C05379 IDH1 TITLE:Citrate cycle (TCA cycle) K00239...K00174...K01610 K01643...K00169...K17753
Layouts - Some cases, manual editing is necessary - Start from tweaked automatic layout, and then use techniques discussed later
Group Similar Nodes
Cytoscape Function for This - Apply layout to selected nodes only
Use Case - Show group of nodes in same cellular location - Same functional groups
Tweak Layout Parameters
- Layout - Settings to tweak parameters Cytoscape Function for This
Stacking & Grouping
- Manual Layout Cytoscape Function for This
2. Length
In Cytoscape - Edge Length - Can be used for the similarity of the connected nodes - Long = less related - Short = closely related
Scaling
3. Area
In Cytoscape - Node Size / Edge Width - Two strongest visual channels for mapping your data - Use these two for your important data - Automatic layout algorithms can be applied only to selected group of nodes
Edge Weight to Width
C00122 SDHA FH Tyrosine metabolism Arginine and proline metabolism K00239... Valine SUCLG2 C15973 DLST SUCLG2 C00091 DLD C16254C00042 C05125 DLD DLAT PC C00022C16255C00024 PDHA1 PDHA1 C15973 C15972 C00158 C00311 IDH1 ACO1 C00417 ACO1 K00169... C00074 PCK1 C00068 Glycolysis / Gluconeogenesis K01610 Alanine Fatty acid biosynthesis Valine Glyoxylate and dicarboxylate metabolism TITLE:Citrate cycle (TCA cycle) Fatty acid degradation Fatty acid elongation C05379 K17753IDH3A... C00026 Ascorbate and aldarate metabolism D-Glutamine and D-glutamate metabolism IDH1 Alanine K00174... OGDH C00068 C15972 C05381 OGDH C00149 K01643... MDH1 ACLYC00036 CS
4. Color
In Cytoscape - Node/Edge/Label Color - Less accurate, but still useful especially when you map to continuous values - Automatic layout algorithms can be applied only to selected group of nodes
Expression Values To Node Colors
Common Pitfall: Use Too Many Colors - Simply awful - Hard to understand - Doesn’t tell anything!
Colors for Categorical Data - Again, limitation of our perception — Use up to 6~7 Colors - Preferably, 3-4 - Less is better!
5. Density
In Cytoscape - Node/Edge/Label Transparency - Use to emphasize important region of the network - Density of connections - Use edge bundling for dense network
YNL036W YDR312WYNL121C YNL183CYNL213CYAL054C YJL176C YML012W YKR082W YFL048C YOR205C YNR038W YMR197C YKR059W YNL189W YDL032W YOR207C YPL217C YBL039C YBR078W YBR030W YNL068C YJL063C YGL120C YLL008W YER111C YIR023W YPL204W YDL056W YEL009C YOR372C YGR162W YMR012W YJL138C YOR117W YNL085W YOR116C YBR011C YDL145C YCR053W YIL131C YAL023C YOR272W YDL213C YDR207C YOR206W YLR182WYOR039W YKL172W YDL014W YJL109C YKR081C YPL012W YGL228W YOL004W YBL038W YDL035C YGL229C YBR247C YER006W YNL132W YOL139C YLR175W YKL143W YJR105W YNL117W YHR090C YBR146W YDR283C YBR029C YGR059W YKL144C YOR261C YHR200W YDL106C YLR025W YOL108C YPR187W YOR310C YHL029C YKL016C YMR079W YMR198W YMR093W YOR210W YMR078C YKL014C YGR231C YGR232W YLL033W YFR004W YLL034C YLR222C YLR129W YLR399C YGR145WYBR077C YIL035C YOR145C YKL015WYPL126W YDR208W YDR384CYJL191W YDR385W YLR337C YDR448W YLR264W YGR090W YMR172W YOR061W YDL116W YGL019W YMR229C YML069W YDL060W YOL116W YDR449C YKR060W YKR095W YKR057W YPL131W YDL075W YPL249C-A YPL086C YIL133C YER130C YKL057C YOL040C YLL043W YER082C YGR253C YBL007C YLR409C YCR057C YNL163C YER056C-A YEL054CL076C YOR369C W YKL006W YPL080C YMR230W 8W YHR141C YGR027C YDR470CYJL136C YOR235W YIL069C YLR387C YPL199C147C YOR293W L034W 1C YOR312CYDR471W R194W YOL121C YJL177W YLR183C YLR184W YDL061C YBR118W-A YDR064W YLR326W 189W YPR132WYLR447C YHR142WYBR085W YDL083C YLR325C YNR037C YMR128W YNL306W YNR035C YGL195W YDL029W YDR363W-A YDR280W YIL032C YJR116W YML013C-A YHR116W YKL020C YDR194C YPL037C YDR422C YPR178W YNL322C YPL228W YDR296W YHL027W YEL037C YHR078W YKL196C YAL053W YDR339C YCR003W YOR309C YGR186W YBR101C YIR010W YOL036W YCL001W-A YCR001WYOR322C YER117W YGL136C YLR208W YBL087C YER116C YNL178W YPL184C 3W W R182C YGR118W YML073CYOL120C YBR116CYOR183W YLL045C YPL034W YMR164C YLR439W YAL022C YGR250C YBL080C YHR161C YPL242C YLL011WYNL313C YLR056WYDR130C YKR029C YOR056C YLR400W YDR405W YGR183C YGR270W YGR128C YMR296C YCR082W YNL037C YNL267W YNL116WYGL106W YDR234W YPL232WYNL059C YJR104CYNL057W YMR033W YKL112W YNL119W YGR129W YML081W YDL130W-A YDL190C YDL209C YMR005W YDL160C YHR162W YHL028W YLR055CYNL312W YOL077C YCL004W YGL122C YKL028W YGR056W YGL107C YGL222C YPL159C YKL029C YJL183W YGR268C YJL062W YJL111WYNL118C YPR018W YDL012C YEL017C-A YKL135C YDR285W YAL043C YLR293C YOR209C YDR326C YIL036WYDL105W YMR092C YCL016C YGL192W YGL092W YDR501W YNL112W YMR116CYJR042W YGL207W YDR500C YPR103W YLR367W YLR029C YOR234C YGL103W YLR333C YML064CFR031C-A YMR257C W YDR174W YDR024W YGL123W YML063W YHL001WYPR104C YGL100W YOR151C YLR061W YOR208WYMR129W YDR284C Y YNL321W YGR185C YFL018C YDR463W YIL031WYNR046W YBR211C YDL193W YPR176C YDR233CYDL208W YDL159W YDR195WYDR329C YDR245W YEL017W YLR024C YHR165C YGR252W YOL076W YOR057W YIL048W YKL190W YBR283CYGL194C YDR361C YKL060C YKL195WYBR212W YKL179CYCL001W YCL011C YLR096W YFL017W-AYKL019W YDR404C YPL036W YLR095C YAL043C-A YMR061WYPR186C YFL047W YCL017C YCL005W YDR330W YPR129W YMR060C YKL177W YHR199C YNL307C YLR229C YMR297W YML081C-AYBR284W YJL008C YCL031C YOL068CYNL149C YJR138WYIL135C YNL255C YOR147WYHR064C YKR056W YAL041W YGR119C YKL005C YOR262W YDL189W YDL122W YDL010W YDR340W YLR287C-A YGL135W YJR147W YDR450WYBL072C YJR145C YLR167W YGL104C YGR149W YPR065W YLR438C-A YJR146W YLR074C YLR403W YJL206CYNR018W YML025C YPL183W-A YNL241C YER079W YML100W YFR017C YOR028C YGL114W YEL045C YOR138C YDR259C YJL101C YMR261C YLR131C YJL067W YLR166C YOR032C YDR039C YLR075W YBR126C YKL062W YLR340W YIL149C YLR438W YEL044W YIL148W YLR105C YOR140W YEL046C W YPL090C R242C YPR131C -A YNL162W R203C YJL189W R085C YDR025W YHL016C YIL018W YOL128C YPR102C YCR093W YIL094C YDR394WYKL145W YNL262W YNL113W YOR323C YNL287W YIL076W YKR067W YGL245W YFR051CYOR150W YPR119W YDR261C YKL008C YGR234W YJL158CYNL284C YHR206W YGL137WYDR238C YJR137C YJL148W YJR110W YBR218C YDL225W YMR188C YFR040W YAL036CYKL104C YNR016C YBR025C YDL226C YER078C YOR110WYDR146C YKL007W YLR174W YJR109C YHR018C YML028W YGL062W YBR07 YMR3 YER155C YJR064W
Other Tips
Avoid Data Overload - Mapping too many attributes makes your visualization awful! - It is hard to see the overall trend if too many channels are used in a image
X
Move Label Position
# of Visual Properties is Limited - Use them effectively - Don’t use too much in the same view
STMN1 SMARCD3 SMARCA4 SMARCD3 TUBB HTT OPTN PPARG PSMD1 MAP4K4 ATP6V1C1 Start from Scratch - If you are not sure you need the decoration or not, remove it - Example: Node border, edge arrow - Even labels are not always required!
“Great Artists Steal…”
Summary - Learn basic principles of data visualization - Write a story before creating visualization - What do you want to tell by the diagram?
External Tools
External Tools - Biological data analysis is not simple! - There is no such thing: one-size-fits-all - Need to understand de-facto standard tools to save your time
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
Network Data Ana Analysis Graph Analysis NetworkX igraph Cytoscape Python Pandas NumPy SciPy Excel Visua IPython 3rd Party Apps NetworkAnalyzer
Data Analysis Tools Analysis VisualizationData Preparation
Data Analysis Tools - Languages / Platforms - R + Bioconductor - Python + Pandas - MATLAB - Excel - Graph analysis library - igraph - NetworkX
Data Visualization Tools - Data visualization on web browsers are getting more and more important… - Cytoscape.js - sigma.js - D3.js
- Need more analysis functions - Cytoscape can perform network analysis interactively, but does not have complete suite of network data analysis tools - These days, cutting-edge methods and algorithms are implemented in Python - Easy to implement, yet fast (because of NumPy/SciPy) - Batch analysis - Visualize in web browsers Why Multiple Tools?
- Avoid reinventing the wheel - igraph and NetworkX have a lots of network analysis functions. Why should we repeat it again? - Collaboration rather than competition - General policy for our project Why Multiple Tools?
Glue for Applications - There are two ways to use external tools with Cytoscape - Common file formats - RESTful API for programatic access (Ongoing)
- Use popular, standard, widely-used data formats ! - GraphML (Recommended) - CSV/TSV - Not a format, but easy to process in scripting languages and spreadsheet File-Base Data Exchange
Realistic Example - Prepare data in Python - Load data from Bioconductor - Calculate network statistics with igraph - Export networks and tables in GraphML format  - Visualize it in Cytoscape
Realistic Example - Prepare data in Python - Load data from Bioconductor - Calculate network statistics with igraph - Export networks and tables in GraphML format  - Visualize it in Cytoscape
Coming Soon… - Programatic access to Cytoscape objects and functions via REST - /networks/ID/nodes/NODEID - /apply/layout?network=ID - We need your opinion!
Communication Bus NDEx (DB) Browser Cytoscape Desktop
Web and Cytoscape
- Prepare/integrate/analyze data with R/Python or traditional desktop applications - Visualize & publish it as web apps Trends in Data Visualization
Web!
Into The Web… - Cytoscape is a Java desktop application - Need glue modules to use existing Cytoscape features from web browsers
New in Cytoscape 3.1.0: Export Networks and Visual Styles to Cytoscape.js Format JS Integration to Cytoscape
Cytoscape.js is NOT - Complete web application - Compatible with Cytoscape Apps - Replacement for Cytoscape
Export to Cytoscape.js Demo
Open Q&A Session
2014 Keiichiro Ono kono@ucsd.edu

Cytoscape Tutorial Session 2 at UT-KBRIN Bioinformatics Summit 2014 (4/11/2014)

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