 Welcome to this brief introduction to StringApp Enrichment Analysis. I've already covered in an earlier presentation how Cytoscape StringApp allows you to bring StringNetworks into the Cytoscape platform. However, the StringApp also provides Genset Enrichment Analysis functionality. If you're not familiar with Enrichment Analysis, I strongly recommend that you go watch my introduction to the core concepts before continuing. StringApp allows you to do functional enrichment for a wide range of Gensets. This includes genontology terms, pathways from numerous databases, protein domains, uniprot keywords, subcellular localization, tissue expression and associated diseases. All of these enrichment results are then shown in the StringEnrichment table, which looks like this. It shows all the enriched terms together with their false discovery rates, categories and much more. Similarly, StringApp allows you to find enriched publications. In this case, we've used text mining to turn the vast literature into Gensets so that each publication is a Genset. By doing Enrichment Analysis, we produce the StringPublication table that lists publications that are enriched for genes from your network. This table includes linkouts to PubMed. A key aspect of doing Enrichment Analysis is to choose the right background. All the results I've shown you so far were done with a genome-wide background, but that often leads to many irrelevant but significant terms. If, for example, you've done a proteomics study and you've taken all the regulated proteins and produced a network like this, you will want to use the observed proton as background. To do this, you load in the observed proton as a second network in cytoscape and use that as your custom background. That way, you find out what's special about those proteins that are regulated compared to everything that was observed in your sample. A similar approach can be used to annotate clusters. If you have your network of regulated proteins from before and you run a clustering algorithm on it to break it into functional modules, you'll have a network that looks like this. You can now select a cluster and run Enrichment Analysis using the selection as foreground and the whole network as background. That way, you find out what's special about the cluster, which can suggest which label you should put on it. When you do Enrichment Analysis, filtering and visualization is important. The reason is that even with a custom background, you often find too many significant terms. You can then filter them either by term category or set a redundancy cutoff that eliminates terms that are too redundant in the sense that they cover the same genes. Once you are happy with your selection of terms, you can visualize them on the network showing the top-end terms either in a pie visualization or a doughnut visualization like this. You can see in this case that two Enriched terms are shown as the two halves of the circles around each node. You can customize this visualization by choosing the number of terms or selecting the specific terms you want as well as customize the colors. You can also send the Enrichment Results to Enrichment Map, which is another cytoscape app for visualizing Enrichment Results. Finally, you can export the Enrichment Results and use it in other software. That's all I have to say about StringApp today. If you want to learn about other parts of it, take a look at this presentation. Thanks for your attention.