Advanced Topic: Automation
cytoscape.github.io/cytoscape-tutorials/presentations/advanced-automation-2019-mu.html
Marshall University
1 March 2019
Goals and Motivations
- Command programmatic control over Cytoscape
- Integrate Cytoscape into your bioinformatics pipelines
- Publish, share and export networks online
Introductions
- Adjunct Assistant Prof, Pharmaceutical Chemistry
- Executive Director RBVI
- Cytoscape team since 2006
- Author of over a dozen Cytoscape apps
Introductions
- Associate director, Bioinformatics Core
- Executive director, National Resource for Network Biology
- Cytoscape team since 2006
- Author of half a dozen Cytoscape apps
Introductions
- Clinicians
- Bench Biologists
- Bioinformaticians
- Computer Scientists
- Chemists
- Mathematicians
- Other
Guided Walkthrough
Cytoscape Rmd Notebooks
- Overview of RCy3
- Cancer networks and data
Example Use Cases
- Omics data - I have a ----------- fill in the blank (microarray, RNASeq, Proteomics, ATACseq, MicroRNA, GWAS …) dataset. I have normalized and scored my data. How do I overlay my data on existing interaction data?
- Coexpression data -I have a dataset that represents relationships. How do I represent it as a network.
- Omics data - I have a -----------fill in the blank (microarray, RNASeq, Proteomics, ATACseq, MicroRNA, GWAS …) dataset. I have normalized and scored my data. I have run my data through a functional enrichment tool and now have a set of enriched terms associated with my dataset. How do I represent my functional enrichments as a network?
Wrap-up
Questions and Discussion
"Exam"
To make sure you now have a working knowledge of Cytoscape automation, choose one of the following datasets to analyze in R and Cytoscape:
- Colins (2007): A merged dataset of three yeast APMS experiments. Find the putative complexes.
- GPL54: A yeast heat shock microarray experiment from GEO. Create a network of the most significant genes based on the magnitude of the fold change and color the nodes by expression.