Uses stylesheets to separate presentation from data in a rendering agnostic manner.Supports selectors for terse filtering and graph querying.Uses layouts for automatically or manually positioning nodes.Fully serialisable and deserialisable via JSON.Documentation includes live code examples, doubling as an interactive requirements specification example graphs may also be freely modified in your browser’s JS console.Has a large suite of tests that can be run in the browser or the terminal.Supports rendering images of graphs on Node.js with Cytosnap.Some demos may not work in old browsers in order to keep the demo code simple. The documentation and examples are not optimised for old browsers, although the library itself is.Browsers with partial but sufficient ES5 support also work, such as IE9 and Firefox 4.Browsers circa 2012 support ES5 fully: IE10, Chrome 23, Firefox 21, Safari 6 ( caniuse).ES5 and canvas support are required, and feature detection is used for optional performance enhancements.Legacy browsers with ES5 and canvas support.Designed for users first, for both frontfacing app usecases and developer usecases.Used in commercial projects and open-source projects in production.Permissive open source license (MIT) for the core Cytoscape.js library and all first-party extensions.A fully featured graph library written in pure JS.Performing actions found in the Tools Menu in Cytoscape.Ĭontroling the panels in the Cytoscape user interface. GetTableColumns() renameTableColumn() loadTableData() mapTableColumn() Managing table columns and table column functions, like map and rename, as well as loading and extracting table data in Cytoscape. GetNodeWidth() getEdgeColor() getNetworkZoom() Retrieving current values for visual properties. Managing styles and retrieving general lists of properties relevant to multiple style modes.ĬreateVisualStyle() setVisualStyle() exportVisualStyles() getArrowShapes() MapVisualProperty() updateStyleMapping() setNodeSizeMapping() setEdgeColorMapping() SetNodeShapeDefault() setEdgeLineWidthDefault()ĭefining mappings between table column values and visual properties. Getting and setting default values for visual properties. SetNodeColorBypass() setEdgeLineStyleBypass() hideNodes() Setting and clearing bypass values for visual properties. OpenSession() saveSession() closeSession() Managing Cytoscape sessions, including save, open and close. GetCurrentView() fitContent() exportImage() toggleGraphicsDetails() Performing view operations in addition to getting and setting view properties. SelectNodes() invertNodeSelection() selectFirstNeighbors() Manipulating selection of nodes and edges in networks. Performing layouts in addition to getting and setting layout properties.Ĭreating and managing networks and retrieving information on networks, nodes and edges.ĬreateNetworkFrom…() create…FromNetwork() getNetworkSuid(), exportNetwork() getAllNodes() getEdgeCount(), getFirstNeighbors() Selecting nodes and edges based on filter criteria.ĬreateDegreeFilter() createColumnFilter() ImportNetworkFromNDEx() exportNetworkToNDEx()Ĭhecking Cytoscape System information, including versions and memory usage. GetCollectionList() getCollectionNetworks()Ĭonstructing any arbitrary CyREST API or Commands API method via standard GET, PUT, POST and DELETE protocols.ĬyrestGET() commandsPOST() cyrestAPI() commandsRun()Ĭommunicating with NDEx from within Cytoscape. Getting information about network collections. InstallApp() disableApp() getInstalledApps() Inspecting and managing apps for Cytoscape. Check out the other vignettes for more exampls. However, you can also include attributes together with the original graph models as Bioconductor graphs, igraphs or ames and then use the provided create functions to create and load in a single step (see createNetworkFromGraph, createNetworkFromIgraph and createNetworkFromDataFrames functions). We continue with the simple 4-node graph, adding two kinds data values ( moleculeType' andlog2fc’). One of the core features of Cytoscape is visual styles, which allow you to specify how data values (e.g., kinase',transcription factor’ expression ratios) should be conveyed in the visual properties of the graph (e.g., node shape, node color or size). For instance, we may know that protein A phosphorylates protein B, that A is a kinase and B a transcription factor, and that their mRNA expression (compared to a control) is a log2 fold change of 1.8 and 3.2 respectively. By conveying this information visually, the graph will be easier to explore. We often know quite a lot about the nodes and edges in our graphs.
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