Cytoscape Automation: empowering workflow-based network analysis - PubMed (original) (raw)
Cytoscape Automation: empowering workflow-based network analysis
David Otasek et al. Genome Biol. 2019.
Abstract
Cytoscape is one of the most successful network biology analysis and visualization tools, but because of its interactive nature, its role in creating reproducible, scalable, and novel workflows has been limited. We describe Cytoscape Automation (CA), which marries Cytoscape to highly productive workflow systems, for example, Python/R in Jupyter/RStudio. We expose over 270 Cytoscape core functions and 34 Cytoscape apps as REST-callable functions with standardized JSON interfaces backed by Swagger documentation. Independent projects to create and publish Python/R native CA interface libraries have reached an advanced stage, and a number of automation workflows are already published.
Keywords: Cytoscape; Interoperability; Microservice; REST; Reproducibility; Service-oriented architecture; Workflow.
Conflict of interest statement
The authors declare that they have no competing interests.
Figures
Fig. 1
Overview of the Cytoscape Automation ecosystem. Reproducible workflows (as Python/R/Javascript or Cytoscape Command scripts) and datasets control Cytoscape through Cytoscape Automation. Results can be created either directly from Cytoscape or from Python/R/Javascript themselves
Fig. 2
Relationship between the Cytoscape Desktop (including CyREST, Cytoscape apps and Cytoscape core) and Cytoscape Automation workflows. Dotted lines indicate command/data flows that pre-date Cytoscape Automation. Solid lines indicate flows created for Cytoscape Automation. New components are in green
Fig. 3
Sample Swagger page for diffuse_with_options, including markups for key areas. The Try it out! button calls Cytoscape to execute this CyREST function
Fig. 4
Sample Swagger results from using the Try it out! button to execute a CyREST call. The page shows the CyREST call that incorporates user-specified parameter values and the JSON-formatted call results
Fig. 5
Results of Cytoscape Automation workflow execution in Python and R. a Uses multiple Cytoscape apps to load and analyze two data sets, then combines them to show critical genes. b Uses multiple R libraries and analyses to create a network that is then laid out and styled in Cytoscape
Fig. 6
A three-step GenePattern workflow shown by the GenePattern Pipeline Editor. The Illumina Expression File Creator step creates a GCT file from a zip of Illumina IDAT files. The Preprocess Dataset step normalizes the GCT data, and the Hierarchical Clustering step performs clustering on genes. The second step was created by GenePattern staff to avoid adding parameters to the first or third steps
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