Setup

• Install the stringApp from the Cytoscape App Store, or via the App Store panel and search for "stringApp".

Experimental Data

For this tutorial, we will use a dataset comparing transcriptomic differences between autistic and normal brain. The study has been published in Voineagu et al., and we will get a summarized dataset with fold change and p-value from the EBI Gene Expression Atlas.

• Download the data here: rna-seq-data-cytoscape.csv.

Retrieve Networks from STRING

To identify a relevant network, we will use the STRING database to find a network relevant to the list of up- and down-regulated genes.

• Open the example data file, rna-seq-data-cytoscape.csv, in a spreadsheet app and select all gene names, from the Gene Names column. This represents all significantly up- and down-regulated genes.
• Launch Cytoscape. In the Network Search bar at the top of the Network Panel, select STRING protein query from the drop-down, and paste in the list of genes.
• Click the search icon to search. The resulting network will load automatically, and should have around 150 nodes.

STRING Network Regulated Genes

The resulting network contains up- and down-regulated genes recognized by STRING, and interactions between them with an confidence score of 0.4 or greater.

STRING Network Regulated Genes

The networks consists of one large connected component, several smaller networks, and some unconnected nodes. We will use only the largest connected component for the rest of the tutorial.

• To select the largest connected component, select Select → Nodes → Largest subnetwork.
• Select File → New Network → From Selected Nodes, All Edges.

Data Integration

Next we will import the RNA-Seq data and use them to create a visualization.

• Load the downloaded rna-seq-data-cytoscape.csv file under File menu by selecting Import → Table from File..... Alternatively, drag and drop the data file directly onto the Node Table.
• For the Key column for Network choose query term to match the gene names in the sample dataset.
• Click OK to import. Two new columns of data will be added to the Node Table.

Visualization

Next, we will create a visualization of the imported data on the network. For more detailed information on data visualization, see the Visualizing Data tutorial.

• In the Style tab of the Control Panel, switch the style from STRING style to default in the drop-down at the top.
• Copy the style to customize your own visualization via Copy Style... in the Options menu of the Style interface, and name it de genes.

Visualization

• Change the default node Shape to ellipse and check Lock node width and height.
• Set the default node Size to 50.
• For node Label, set a passthrough mapping for display name.
• Set the default node Fill Color to light gray.
• For node Fill Color mapping, choose column: Fold Change and mapping type: continuous. Keep the default red-blue color palette.

Visualization

Apply the Prefuse Force Directed layout by clicking the Apply Preferred Layout button in the toolbar. The network will now look something like this:

STRING Enrichment

The STRING app has built-in enrichment analysis functionality, which includes enrichment for Gene Ontology, InterPro, KEGG Pathways, and PFAM.

• In the STRING tab of the Results Panel, click the Functional Enrichment button. Keep the default settings.

STRING Enrichment

• When the enrichment analysis is complete, a new tab titled STRING Enrichment will open in the Table Panel.

STRING Enrichment

The STRING app includes several options for filtering and displaying the enrichment results. The features are all available at the top of the STRING Enrichment tab. We are going to filter the table to only show GO Biological Process.

• At the top left of the STRING enrichment tab, click the filter icon . Select GO Biological Process and check the Remove redundant terms check-box. Then click OK.
• Next, we will add a split donut chart to the nodes representing the top terms by clicking on .
• Explore custom settings via in the top right of the STRING enrichment tab.

Exporting Networks

Cytoscape provides a number of ways to save results and visualizations:

• As a session: File → Save Session, File → Save Session As...
• As an image: File → Export → Network to Image...
• To the web: File → Export → Network to Web Page... (Example)
• To a public repository: File → Export → Network to NDEx
• As a graph format file: File → Export → Network to File.
  Formats:
  • CX JSON / CX2 JSON
  • Cytoscape.js JSON
  • GraphML
  • PSI-MI
  • XGMML
  • SIF