An introduction to building reproducible network visualization workflows with Cytoscape, py2cytoscape, and Jupyter Notebook.

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
5/12/2016 The Scripps Research Institute
Lecture 2: Reproducible Workflows with Jupyter Notebook

2. Lecture slides are available here:
http://www.slideshare.net/keiono

3. Agenda
• Lecture 1:
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 (Today):
Reproducible Analysis & Visualization
• Introduction to Jupyter Notebook
• Create a reproducible network
visualization workflows with Python

4. Review of Lecture 1
- Network analysis / visualization is a powerful
method to get biological insights from your
screening result
- Cytoscape is the de-facto standard tool to
perform this type of analysis

5. Review of Lecture 1
-Core features of Cytoscape
-Navigation (Pan/Zoom/Select)
-Network / Table Data Import
-Automatic Layout
-Visual Style

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

7. Reproducibility

8. 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

9. Problems
- Reproducibility of biological research, especially for in vivo/vitro
experiments, is a hard problem
- But this is true even for in silico analysis!
- OS version
- Revision of scripts
- Data analysis software versions
- Version of data files
- Command line parameters written on a paper napkin
- “Black magic” only a grad student knows
- This is something we need to fix, using latest technologies and best
practices

10. Typical Workflow

11. Data
Preparation
Analysis Visualization

12. Data Preparation

13. Data
Preparation
- Cleansing
- Normalization
- Missing values
- Corrupted values
- Reformat
- Conversion

14. Data
Preparation
Analysis Visualization

15. Analysis

16. Analysis
- Filtering
- Standard graph
statistics
- Density
- Betweenness
- Centrality
- Clustering
- Community Detection
- GO enrichment analysis

17. Data
Preparation
Analysis Visualization

18. Visualization

19. Visualization
- Mapping
- Data points to
visual variables
- Layout
- For graphs:
- Force-directed
- Tree

20. Data
Preparation
Analysis Visualization

21. Data
Preparation
Analysis Visualization

22. Data
Preparation
Analysis Visualization

23. Cytoscape for Interactive Visualization
Python for Data Manipulation / Analysis

24. Lab Notebook for in silico Experiments

25. Interactive Command-Line
+
Markdown-based Documents

26. IPython Notebook?
Jupyter?

27. IPython
Notebook
Notebook UI
+ Python Kernel
Jupyter
Notebook UI
+
Language Kernel
(R/Julia/etc.)

28. Language-Agnostic
- From next version (4.x), Python Notebook will be an
implementation of Jupyter
- You can switch to other language kernels
- In this lecture, we will use Python, but you can use
language of your choice to control Cytoscape

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

31. REST

32. 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

33. Interactive Data Analysis
Environments
In-House Databases External Computing Resources
- Graph Layout
- Statistical Analysis
- Data Pre-processing
RStudio
- NumPy
- SciPy
- Pandas
- NetworkX
IPython Notebook
File / Code Hosting ServicesPublic Data Repository
PSICQUIC Services
EBI RDF Platform
Other Bioinformatics Web Applications / Services
- igraph
- rCurl
Command Line Tools
> sed
> awk
> grep
> curl
Web Browsers
Data Repository & Collaboration Service
Data Bus (Internet)
Your Workstation
Cytoscape App Store
Cytoscape Desktop
Apps
Core
REST

34. REST
Cytoscape 3.1+
Clients
POST
PUT
DELETE
GET
How cyREST Works

35. Mapping Cytoscape API to HTTP Methods
Create
Read
Update
Delete
Cytoscape Operations
POST
GET
PUT
DELETE
HTTP Methods

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

37. http://localhost:1234/v1/networks/52

38. Language-Specific Shims
For Python For R

39. REST

40. REST
Lab notebook to record
your workflow
Make Cytoscape
controllable via scripts
Manage
multiple versions of your
notebooks and other
scripts

41. -
- Two Google Groups
- cytoscape-
discuss@googlegroups.com
- cytoscape-
helpdesk@googlegroups.com
- ANY question is OK!
Getting Help

42. Further Readings

43. Further Readings
• My presentation slides
• http://www.slideshare.net/keiono
• cyREST web sites
• http://apps.cytoscape.org/apps/cyrest
• https://github.com/idekerlab/cyREST/wiki
• py2cytoscape — https://github.com/idekerlab/
py2cytoscape

44. 2016 Keiichiro Ono
kono@ucsd.edu