This document discusses modern tools for graph analysis and making graph workflows reproducible. It introduces cyREST, a RESTful API for programmatic access to Cytoscape, and language-specific wrappers like RCy3 and py2cytoscape that provide natural APIs. These tools allow running Cytoscape workflows in notebooks and remote machines. It also covers graph libraries for analysis like NetworkX, igraph, graph-tool, and PGX for smaller graphs, and distributed frameworks like GraphX, GraphLab Create, and Neo4j for extremely large graphs with billions of nodes. The document recommends not using NetworkX for large data and considering cloud-based options for difficult to install tools.

1. Overview of Modern
Graph Analysis Tools
Keiichiro Ono
Cytoscape Core Developer Team
UC, San Diego Trey Ideker Lab / National Resource for Network Biology
5/24/2016 Ideker Lab Meeting

2. Recap
Cytoscape Session File — for sharing results
But what about process?

3. http://www.the-scientist.com/?articles.view/articleNo/43632/title/Get-With-the-Program/
https://theconversation.com/how-computers-broke-science-and-what-we-can-do-to-fix-it-49938http://www.nature.com/nature/journal/v483/n7391/full/483531a.html
Reproducibility
…it’s a known issue

4. Data
Preparation
Analysis Visualization

5. Advanced Users:
Cytoscape for Interactive Visualization
R/Python for Data Manipulation / Analysis

6. Lab Notebook for in silico Experiments

7. Interactive Command-Line
+
Markdown-based Documents

8. Question
• Cytoscape is a desktop application
• Point & click GUI operation
• Easy to use, but how can we
make our workflow
reproducible?

9. REST

10. What is cyREST?
- Platform-independent, RESTful API module for Cytoscape
- Means you can access basic Cytoscape data objects
programmatically
- Now it’s a Cytoscape Core feature!
REST

11. Get full network with unique ID 52 as JSON
GET http://localhost:1234/v1/networks/52

12. But, don’t use cyREST (directly)!

13. Language-Specific Shims
For Python For R

14. RCy3
• R wrapper for cyREST
• Now a part of Bioconductor
• Easy to install
• Natural API for R users

15. py2cytoscape
• Python wrapper for cyREST
• Supports high-level API
• Cytoscape.js viewer included
• Supports for iOS/Android

16. Example

17. Creating an empty network with raw cyREST API

18. …and with py2cytoscape

19. http://nbviewer.jupyter.org/gist/keiono/
73da21846b6f73de70122bdb545c1c14

20. https://github.com/cytoscape/cyREST/wiki/Running-your-workflow-in-the-clouds

21. Now you have…
• Programmatic access to Cytoscape functions
• Notebooks to run your workflows
• Remote machines (clusters/clouds) for CPU intensive
tasks

22. Graph Libraries as Analytic Engine for Cytoscape

23. In-Memory Graph Analysis
N < millions

24. NetworkX
Pros:
- Easy to install
- Most of basic
graph operations
Cons:
- Slow!

25. igraph
Pros:
- Has a lot of analysis features
Standard graph statistics, community detection, label propagation, etc.
- Fast (comparing to NetworkX)
Cons:
- Weird API (for Python Users)

26. graph-tool
Pros:
- Fast (Optimized with C++)
- Nice visualization features
Cons:
Hard to install

27. Parallel Graph
Analytics (PGX)
- Oracle’s experimental project
- There are lots of unknowns due to its stage (early experimental release), but has a
lots of features, just like igraph

28. Don’t use NetworkX for large data sets…

29. FYI: GPU-Based
Layouts
~100x faster

30. Out-of-Core Graph Analysis
N > billions

31. GraphX
• Part of Apache Spark Project
• Industry Standard
• Lots of documentation and
supports from the community
• You can use Python and R, but
in Spark world, Scala is still the
first-class citizen…
End-to-end PageRank performance
(20 iterations, 3.7B edges)

32. GraphLab Create
• Commercial Service by Dato
• High-level API and data
structure
• SFrame/SGraph
• Their version of scalable-
DataFrames
• (Semi) automatic parallel
processing

33. Neo4j v3
- This one focuses on storing arbitrary large graph (billions of nodes /edges)
data
- Has some analysis features
- Now natively support Python

34. Summary
• Don’t use NetworkX unless it’s necessary!
• Don’t use raw cyREST API if you are Python/R users
• There are lots of new graph analysis tools
• Some of them are bit hard to install / Setup
• Candidates for CI services (?)
• We deploy to servers, and you can access from
simple API

35. 2016 Keiichiro Ono
kono@ucsd.edu