Advanced Topic: Automation

cytoscape.github.io/cytoscape-tutorials/presentations/advanced-automation-2024.html


Virtual
May 2024

Goals and Motivations

By the end of this workshop you should be able to:
  • Command programmatic control over Cytoscape
  • Integrate Cytoscape into your bioinformatics pipelines
  • Publish and share Cytoscape-powered notebooks
Introductions Cytoscape Automation Guided Walkthrough Automation Use Cases Wrap-up

Introductions

Alex Pico, Gladstone Institutes
  • Director, Bioinformatics Core
  • Executive director, National Resource for Network Biology
  • Cytoscape team since 2006
  • Co-author of over a dozen Cytoscape apps
  • Co-author of RCy3 Bioconductor package

Introductions

Yihang Xin, Gladstone Institutes
  • Software Engineer, National Resource for Network Biology
  • Cytoscape team since 2020
  • Co-author of Cytoscape apps
  • Co-author of RCy3 and py4cytoscape packages

Introductions

What about you?

  • Clinicians
  • Bench Biologists
  • Bioinformaticians
  • Computer Scientists
  • Chemists
  • Mathematicians
  • Other
Introductions Cytoscape Automation Guided Walkthrough Automation Use Cases Wrap-up
Introductions Cytoscape Automation Guided Walkthrough Automation Use Cases Wrap-up
Introductions Cytoscape Automation Guided Walkthrough Automation Use Cases Wrap-up

Automation Use Cases

Here are some common automation workflows. Pick one that is similar to the type of data you work with. Follow the steps to learn about package functions.

Ask questions if anything is unclear!

  1. Transcriptomic data: Rmd, ipynb
  2. Tumor expression and mutation data: Rmd, ipynb
  3. Proteomics data: Rmd, ipynb
Introductions Cytoscape Automation Guided Walkthrough Automation Use Cases Wrap-up

Wrap-up

What have we learned?

  • Helper packages for Cytoscape from Python and R (JS eary development).
  • Load networks from STRING (also works for NDEx, WikiPathways, etc.)
  • Load my data as data frames from local files (also cloud: github, drive, etc.)
  • Perform data visualization, mapping my data to network visual properties.
  • Perform layouts, subnetworks, filters, and analyses (including many apps).
  • Export networks and publication-quality image formats.

Wrap-up

Don't forget about Notebooks!

  • Notebooks: ipynb and Rmd files
  • GitHub: ipynb and Rmd(nb.html) files
  • Google Colab: Python and R

Wrap-up

Questions and Discussion

  • Anything unclear?
  • Anything missing?

Thank You!

Here are additional resources you may find useful: