5/7/2023 0 Comments Cytoscape pathway analysis![]() ![]() The currently set threshold will be displayed in the accompanying text box. By moving the slider to the left, the Q-value threshold is adjusted to a lower value, removing any nodes that do not pass the Q-value threshold. All nodes that pass the initial Q-value threshold are displayed in the enrichment map. By default, the slider is set to Q value if the data contains Q values but can be changed to use P values by selecting the ‘P-value’ radio button. ( iv) ‘Node Table’ containing values for all variables associated with each node in the network. ( iii) Cytoscape search bar, which can be used to search for genes in the enrichment map. ( ii) The ‘Table Panel’ contains tables with node, edge and network attributes, as well as an enrichment map ‘Heat Map’ panel displaying expression for genes associated with selected nodes and edges. ( i) Cytoscape ‘Control Panel’, which contains ‘Networks’, ‘Styles’ and ‘Select’ tabs as well as the ‘EnrichmentMap’ main panel. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes however, the principles can be applied to diverse types of omics data. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. ![]()
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