In brief, it is optimized to create a manageable network that brings together the most significantly activated or inhibited upstream regulators, diseases, functions, and pathways from your analysis and present them in a way that reduces the redundancy of the predictions and is not overly connected. The algorithm is a heuristic that uses a number of factors to select and connect entities from an analysis. Overall strategy of the Graphical Summary For example, the same summary is shown below with the Subcellular layout. You can change the overall layout of the nodes by clicking the Change Layout button and choosing an option. You can change the font size of the node labels in the summary by clicking this button in the toolbar and using the pull-down menu next to “Node Name Font Size”. Pasted networks will not contain any of the inferred edges. If you wish to use Build or Overlay tools on the Graphical Summary, you will need to select all the nodes and copy/paste them into a new My Pathway. Note that revising the summary algorithmically with the slider may bring back nodes that you may have deleted manually. The default position is in the center of the slider.Ĭhanges made by revising the summary with the slider are saved with your analysis automatically, whereas manually deleting or repositioning nodes or edges requires you to click the Save button on the toolbar if you want to retain those changes after closing your analysis. To re-run and change the size, click the button in the toolbar and use the size slider to increase or decrease the number of nodes appearing in the Graphical Summary.
Graphical analysis how to#
How to change the Graphical Summary (if desired)Ĭurrently, the nodes in the Graphical Summary can be changed in two ways: 1) by deleting nodes and / or edges manually, or 2) by re-running the algorithm with a different overall size setting. The inferred edge between the nodes and their activation states suggests that ZEB1 activation and CST5 inhibition have a similar effect on downstream molecules. As shown above, the analysis predicts ZEB1 is activated (i.e., it has a positive z-score and is therefore colored orange) and CST5 is inhibited (i.e., negative z-score and therefore blue). The section at the end of this article goes into detail about the network is designed. Inferred edges in the Graphical Summary, such as the one between CST5 and ZEB1 at the lower right, are shown with dotted lines (rather than solid or dashed) and are not used elsewhere in IPA currently. Key aspects of these claudin-low cells are that they have activated the epithelial-mesenchymal transition (EMT) through activation of specific transcription factors such as ZEB1, SNAI1, and SNAI2 and have tissue-invasive tendencies, which are well represented in the generated summary: Such inferred relationships help you visualize related biological activities.īelow is the Graphical Summary for an analysis of a set of aggressive “claudin-low” breast cancer cell lines contrasted to less aggressive luminal A type breast cancer cell lines (based on PMID 20813035 ). The algorithm that constructs the summary uses machine learning techniques to prioritize and connect entities that are in some cases not yet connected by findings in the QIAGEN Knowledge Graph. The Graphical Summary can include entities such as canonical pathways, upstream regulators, diseases, and biological functions. This feature selects and connects a subset of the most significant entities predicted in the analysis, creating a coherent and comprehensible synopsis of the analysis. The aim of the Graphical Summary is to provide a quick overview of the major biological themes in your IPA Core Analysis and to illustrate how those concepts relate to one another.
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