Select GO Biological Process and check the Remove redundant terms check-box. At the top left of the STRING enrichment tab, click the filter icon.We are going to filter the table to only show GO Biological Process. The features are all available at the top of the STRING Enrichment tab. The STRING app includes several options for filtering and displaying the enrichment results. When the enrichment analysis is complete, a new tab titled STRING Enrichment will open in the Table Panel.In the STRING tab of the Results Panel, click the Functional Enrichment button.The STRING app has built-in enrichment analysis functionality, which includes enrichment for Gene Ontology, InterPro, KEGG Pathways, and PFAM. The network will now look something like this: STRING Enrichment in the Options menu of the Style interface, and name it de genes up.Īpply the Prefuse Force Directed layout by clicking the Apply Preferred Layout button in the toolbar. Save your new visualization under Copy Style.Finally, for node Label, set a passthrough mapping for display name.Select the ColorBrewer yellow-orange-red shades gradient. Double-cllick the color mapping to open the Continuous Mapping Editor and click the Current Palette.For node Fill Color, create a continuous mapping for 'autism' vs 'normal'.Set the default Border Width to 2, and make the default Border Paint dark gray.Set the default node Fill Color to light gray.Change the default node Shape to ellipse and check Lock node width and height.In the Style tab of the Control Panel, switch the style from STRING style v1.5 to default in the drop-down at the top.For more detailed information on data visualization, see the Visualizing Data tutorial. Next, we will create a visualization of the imported data on the network. Two new columns of data will be added to the Node Table. Select the query term column as the Key column for Network and select the Gene Name column as the key column by clicking on the header and selecting the key symbol.In Advanced Options., in the Ignore Lines Starting With field, enter #, to exclude the additional lines at the beginning of the data file.Alternatively, drag and drop the data file directly onto the Node Table. Load the downloaded E-GEOD-30573-query-results.tsv file under File menu by selecting Import → Table from File.Next we will import the RNA-Seq data and use them to create a visualization. Select File → New Network → From Selected Nodes, All Edges.To select the largest connected component, select Select → Nodes → Largest subnetwork.We will use only the largest connected component for the rest of the tutorial. The networks consists of one large connected component, several smaller networks, and some unconnected nodes. The resulting network contains up-regulated genes recognized by STRING, and interactions between them with an confidence score of 0.4 or greater. The resulting network will load automatically, and should have around 173 nodes. Click Import to continue with the import using the default choices. If any of the search terms are ambiguous, a Resolve Ambiguous Terms dialog will appear. Open the options panel and confirm you are searching Homo sapiens with a Confidence cutoff of 0.40 and 0 Maximum additional interactors.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 263 up-regulated genes. To identify a relevant network, we will use the STRING database to find a network relevant to the list of up-regulated genes. With the filter active, select and copy all entries in the Gene Name column.But in this case, all genes with a fold change greater than 2 already meet that cutoff. Next, one would normally filter out non-significant changes by filtering on the p-value as well, for example setting p-value less than 0.05.In the drop-down for the fold change column, set a filter for fold change greater than 2.Select the row containing data value headers (row 4) and select Data → Filter.We are going to define a set of up-regulated genes from the full dataset by filtering for fold change and p-value. The file contains a summary at the top, and has 4 columns of data: Gene ID, Gene Name, fold change and p-value. Download the data: Transcriptomic analysis of autistic brain reveals convergent molecular pathology.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. Install the stringApp from the Cytoscape App Store, or via Apps → App Store → Show App Store.įor this tutorial, we will use a dataset comparing transcriptomic differences between autistic and normal brain.
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