Intructions here

Intro

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:

Loading Network

To get started, install and launch the latest version of Cytoscape.

We will use NDEx to find a relevant network. In the Network Search interface in the Control Panel, select NDEx from the drop-down, and type in “GAL1 GAL4 GAL80”.

In the search results, find the galFiltered network with data. Click the Import network to Cytoscape icon to the left of the network name.

The network will open with the default style, similar to the network on the right:

Mine looks like this (not like in the tutorial):

Visualizing Expression Data on Networks

Probably the most common use of expression data in Cytoscape is to set the visual properties of the nodes (color, shape, border) in a network according to expression 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.

You can select nodes in the network by Shift + Click and Drag or by Shift + clicking on multiple nodes. Selecting one or more nodes in the network will update the Node Table to show only the corresponding row(s).

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

Set node fill color, default node color, node border width

Creating a Legend

Layouts

Select Nodes

Expand Selection and Create New Network

Exploring Nodes

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Digging into the biology of this network, it turns out that GAL4 is repressed by GAL80. Both nodes (GAL4 and GAL11) show fairly small changes in expression, and neither change is statistically significant: they are pale blue with thin borders. These slight changes in expression suggest that the critical change affecting the red nodes might be somewhere else in the network, and not either of these nodes. GAL4 interacts with GAL80, which shows a significant level of repression: it is medium blue with a thicker border.

Note that while GAL80 shows evidence of significant repression, most nodes interacting with GAL4 show significant levels of induction: they are rendered as red rectangles. GAL11 is a general transcription co-factor with many interactions.

Putting all of this together, we see that the transcriptional activation activity of Gal4 is repressed by Gal80. So, repression of Gal80 increases the transcriptional activation activity of Gal4. Even though the expression of Gal4 itself did not change much, the Gal4 transcripts were much more likely to be active transcription factors when Gal80 was repressed. This explains why there is so much up-regulation in the vicinity of Gal4.

Summary

In summary, we have:

Finally, we can now export this network as a publication-quality image….

Saving Results

Formats: * CX JSON * Cytoscape.js JSON * GraphML * PSI-MI * XGMML * SIF