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Cytoscape is an open source software platform for integrating, visualizing, and analyzing measurement data in the context of networks. This tutorial presents a scenario of how expression and network data can be combined to tell a biological story and includes these concepts:

  • Visualizing networks using expression data.
  • Filtering networks based on expression data.
  • Assessing expression data in the context of a biological network.

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

if(!"RCy3" %in% installed.packages())
  BiocManager::install("RCy3")

library(RCy3)

Getting started

First, launch Cytoscape and keep it running whenever using RCy3. Confirm that you have everything installed and running:

cytoscapePing()
cytoscapeVersionInfo()

Loading Network

Let’s open a Cytoscape demo session file:

openSession()

Visualizing Expression Data on Networks

Probably the most common use of expression data in Cytoscape is to set the visual style of the nodes (color, shape, border) in a network according to available data. This creates a powerful visualization, portraying functional relation and experimental response at the same time. Here, we will show an example of doing this.

The data used in this example is from yeast, and represents an experiment of perturbations of the genes Gal1, Gal4, and Gal80, which are all yeast transcription factors.

For this tutorial, the experimental data was part of the Cytoscape session file you loaded earlier, and is visible in the Node Table:

We can now use the data to manipulate the visual properties of the network by mapping specific data columns to visual style properties:

The gal80Rexp expression values will be mapped to node color; nodes with low expression will be colored blue, nodes with high expression will be colored red. Significance for expression values will be mapped to Node Border Width, so nodes with significant changes will appear with a thicker border.

Set Node Fill Color

Let’s specify the node fill color as a gradient ranging from blue to red for expression values using a continuous mapping for the ‘gal80Rexp’ column:

setNodeColorMapping('gal80Rexp', colors=paletteColorBrewerRdBu, style.name="galFiltered Style")

Note: we are using paletteColorBrewerRdBu to generate a set of three standardized colors balanced for a divergent gradient (low, mid, high), which are automatically extracted from the gal80RExp column.

This produces an initial gradient ranging from blue to red for expression values. Notice that the nodes in the network change color.