 Welcome to this tutorial on network visualization. This will be a hands-on demonstration of how to do network visualization of large omics datasets. If you want to understand why I do what I do, I highly recommend that you go watch my introduction to the core concepts of network visualization. Here we will first retrieve a large physical approach in interaction network, import some omics data and visualize them on the network using discrete column mapping. Next, we'll cut up the network using network clustering and finally improve the layout of the figure. For this, we'll be working with Cytoscape and a number of different apps, specifically StringApp to retrieve the network, ClusterMaker2 to perform the clustering and finally Wi-Fi's layout algorithms to improve the layout. If you're not familiar with how to install apps in Cytoscape, I highly recommend that you go watch my basic StringApp tutorial first. This time, we'll work with a large phosphoprotomics dataset. Each row in this spreadsheet represents a phosphorylation site with a uniproduct session number for the protein, a gene name, the modified residue number, multiple lockfold change values representing comparisons across several conditions and finally a cluster assignment that summarizes the behavior across all the conditions for each site. We select the uniproduct sessions for the hundreds of rows and paste them all into the search box in Cytoscape. This time, we again perform a StringProject query but adjust the search options to retrieve a physical interaction network rather than a full functional association network. However, despite wanting only physical interactions, you get a very large interaction network. For this reason, Cytoscape by default does not show the full graphics detail. You have to turn this on in the View menu. In the right-hand panel, like last time, we turn off the StringColors and turn off the miniature structures inside the glass balls. We go to the File menu and choose to import a table from File, thereby importing the spreadsheet with all its data into the Note table. In the import dialog, we again remember to adjust that we want to use the query term column for matching the names from the spreadsheet. Once the data are imported, we go to the Visual Styles panel, select the fill color and say that we want the fill color to depend on the cluster assignment. Since it's ABC, we choose DiscreteColorScheme and use the ColorGenerator to create a color palette from ColorBrewer Z1. Now you have the Note, Colored, Red, Green and Blue depending on the behavior in theomics data. However, it's still not a good visualization of the dataset because the network is, frankly, a hairball. The solution to this is to run clustering on the network. From the StringApp panel, you can run MCL Clustering, which is actually performed by the ClusterMaker 2 app. And once you do that, you get a new network where the big hairball has been cut up into dense clusters. You still see a little bit of overlap of nodes. For this, we use the Wi-Files layout algorithms to remove overlaps slightly adjusting the layout without altering it overall. This is a pretty good starting point for visualizing a bigomics dataset. Thanks for watching this tutorial. If you want to learn how to do more advanced visualization of theomics data in Cytoscape, take a look at this brief introduction to theomics visualizer app.