Let’s take that known data and see if we can subset the data into 4 subtypes. Looking at the result, is seems like there are definite bands of expression data that look similar across a group of patients, but differ between groups, however, the exact number of clusters and divisions between them aren't completely clear.īased on previously reported results, it has been suggested that there are 4 different subtypes of glioblastoma multiforme.
The resulting TreeView should look like the figure on the next slide. The checkboxesĬluster attributes as well as nodes, Ignore nodes/edges with no data,Īnd Show TreeView when complete should all be checked. Select Pearson correlation for Distance metric andSelect all of the samples (columns beginning with TCGA-) in the In Cytoscape, selectĪpps → clusterMaker → Hierarchical cluster. clusterMaker2 is a Cytoscape app that provides a number of clustering algorithms including hierarchical and k-means. Let’s cluster all of the data to see if there are any significant patterns that have been averaged out by looking at all samples in the aggregate. Only one gene shows significant average expression changes across all samples, RPS4Y1, a ribosomal protein that has been shown to be linked to glioblastoma, but it’s still a relatively unsatisfying result that only a single protein changes expression in glioblastoma patients. No additional genes will be selected in this case (but this approach will be reused later). You’ll also need to change the type of match (at the top of the panel) to Match any (OR). Repeat the same process as above, but set the values to be between 1 and 5.This will select all genes that are significantly underexpressed on average, across all samples (only one gene should be selected). Select the "Mean log2FC" column and set the values to be between -5 and -1. In this case we’re going to add a Column Filter. Open the Select tab and click on the + button to add a new condition.We’ll use the Select tab in the Control Panel to find the significant overall expression changes. You should be able to see this in the Table Panel.This will import all of the data in the spreadsheet and associate each row with the corresponding node.It turns out that all of the defaults are correct for just importing the data, so click on OK.This will bring up the Import Columns From Table dialog. Open the file again, but now use File → Import → Table from File.This will create a grid of 1500 unconnected nodes, where each node represents a gene. You’ll see a warning about no edges, but that’s OK. Click only on the GeneName column and set this column as the Source Node column (green circle).Click on Select None to disable all columns.This will bring up the Import Network From Table dialog. Alternatively, drag and drop the data file directly onto the Node Table. Import the file to create a network using File → Import → Network from File.
#CYTOSCAPE CONTEXT MENU INSTALL#
Go to the app store to install the Functional Enrichment Collection.
#CYTOSCAPE CONTEXT MENU MANUAL#
If this is your first time launching Cytoscape, refer the manual for a basic introduction.
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