 Welcome to this brief introduction to Omics Visualizer. I've earlier talked about how you can use Cytoscape to import networks from various sources, import Omics data, and then map these data to visual properties for example, showing false change values as colors using a color gradient. That way, you can visualize Omics data on networks to produce figures like this. If you're not already familiar with this, I strongly recommend that you go watch my brief introduction to the Cytoscape platform before continuing. There is a problem, however. When dealing with Omics data, we often have multiple conditions, for example, time-core studies, or we have site-specific data coming typically from phosphoprotomic studies. In those situations, having one color per protein is simply not enough to represent the data. The obvious solution would be to partition each node like this so that one node can simultaneously have multiple colors. Omics Visualizer allows you to do precisely that. It's a Cytoscape app that allows you to import a table with multiple rows per protein, for example, a row per phosphorylation site as in this table. Once you've imported the table, you can filter it in various ways, and then either retrieve a string network via the string app or map the data table to a network that you've imported yourself. Once you have the data linked to a network, you can visualize the data on that network, mapping data to colors. This can be done in several different ways. You can use a pie visualization to show either discrete mappings of categorical data like this, where you have three parts representing different phosphorylation sites with different colors representing different categories. Similarly, you can use continuous mappings to represent quantitative data like lock fold change values to map the data onto the nodes. There's also the donut visualization which has the advantage that it allows you to show even multiple values per slice. If you have multiple conditions and site-specific data, you can show the data this way, having each concentric circle representing a different condition and each slice representing here a different phosphorylation site. You can use this of course to visualize data on networks inside escape. For small networks, the donut visualization works very well since it allows you to show all the information at the same time. So here you see a small network, where for each protein we show phosphorylation sites with regulation under multiple conditions. You can also simplify this since the labels are optional, that way making a network looking this way. You will typically want to do that if you want to visualize larger networks. You can see for yourself and imagine how it would look if I could labels on every phosphorylation site. However, even the simpler version tends to lead to visual overload for very large networks. For this reason, I recommend using the pie visualization instead since it gives better visibility of the data when having smaller nodes. Here you see the same network shown with the pie visualization instead of the donut visualization. Of course, it has the disadvantage that you can only show one value per slice. That's all I have to say about Omics Visualizer, try it out for yourself. If you want to learn more about data visualization on networks, go watch this presentation. Thanks for your attention.