Keiichiro Ono UCSD Trey Ideker Lab Cytoscape Core Team

Lab Meeting Aug 4, 2015

Building Reproducible Network Data Analysis / Visualization Workflows

REST

Problems We are Trying to Solve

- Complex software stack for data analysis - Setting up environment for data analysis is not trivial, and it is time-

consuming

- Python 3.x or 2.x/NumPy/SciPy/Cython Modules - R/Bioconductor/packages - OS version, etc.

- Automation - Point-and-Click operations are not reproducible

- Applying different layouts to 100 networks by hand is possible, but ridiculous - Sharing Recipe (= common workflows) is hard

- Integration to external computing resources

Goal: Reproducible,Scalable Dry Experiments

REST

REST

- Docker - Data analysis environment in a portable container

- GitHub - Source code sharing

- Jupyter Notebook - Your electronic lab notebook

- cyREST - RESTful API module for Cytoscape

Goal: Reproducible, Scalable Dry Experiments

Data Preparation

Analysis Visualization

REST

Scenario 1: Everything on your Workstation

Notebook Server

Your Jupyter Notebook

REST

Scenario 2: Workstation + Cloud

Notebook Server

Your Jupyter Notebook

Example: Community-Detection + Edge-Weighted Layout

Source Code: bit.ly/1P4LUFU

Demo

TODO

- Integration to Cyberinfrastructure (CI)

- R Wrapper - https://github.com/tmuetze/

Bioconductor_RCy3_the_new_RCytoscape

- More realistic workflows / pipelines

Resources- cyREST

- http://apps.cytoscape.org/apps/cyrest

- py2cytoscape

- https://pypi.python.org/pypi/py2cytoscape

- RCy3

- https://github.com/tmuetze/Bioconductor_RCy3_the_new_RCytoscape