Switch styles

Check out the marquee style!

setVisualStyle('Marquee')

My own style

Create your own style with node attribute fill mappings and some defaults

style.name = "myStyle"
defaults <- list(NODE_SHAPE="diamond",
                 NODE_SIZE=30,
                 EDGE_TRANSPARENCY=120,
                 NODE_LABEL_POSITION="W,E,c,0.00,0.00")
nodeLabels <- mapVisualProperty('node label','id','p')
nodeFills <- mapVisualProperty('node fill color','group','d',c("A","B"), c("#FF9900","#66AAAA"))
arrowShapes <- mapVisualProperty('Edge Target Arrow Shape','interaction','d',c("activates","inhibits","interacts"),c("Arrow","T","None"))
edgeWidth <- mapVisualProperty('edge width','weight','p')

createVisualStyle(style.name, defaults, list(nodeLabels,nodeFills,arrowShapes,edgeWidth))
setVisualStyle(style.name)

Pro-tip: if you want to set NODE_WIDTH and NODE_HEIGHT independently, you also need to unlock the node dimensions with…

lockNodeDimensions(FALSE, style.name)

Add node attributes

We often know quite a lot about the nodes and edges in our graphs. By conveying this information visually, the graph will be easier to explore. 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. 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).

We continue with the simple 4-node graph, adding two kinds data values (moleculeType' andlog2fc’). The easiest way to do this is via data.frames. However, you can also include attributes together with the original graph models as Bioconductor graphs, igraphs or data.frames and then use the provided create functions to create and load in a single step (see createNetworkFromGraph, createNetworkFromIgraph and createNetworkFromDataFrames functions). Check out the other vignettes for more exampls.

    df <- data.frame (moleculeType=c('kinase','TF','cytokine','cytokine'),
                     log2fc=c(1.8,3.0,-1.2,-2.5),
                     row.names = c('A','B','C','D'), # row.names = node names
                     stringsAsFactors = FALSE)       # important when loading strings!
    loadTableData (df)

Note that adding the attributes does not in itself cause the appearance of the graph to change. Such a change requires that you specify and apply visual style mappings, which will be explained in the next section. You can, however, examine these attributes in Cytoscape, using Cytoscape’s the Data Panel to display data values associated with selected nodes immediately below the Cytoscape window.

Modifying the display: defaults and mappings

RCy3 provides an easy way to not only change the default styles, but more interestingly, RCy3 also provides easy access to mapping your data to visual styles, e.g., allowing the size, shape and color of nodes and edges to be determined by the data you have associated with those nodes and edges.

First, let’s change the the defaults.

    setNodeShapeDefault ('OCTAGON')
    setNodeColorDefault ('#AAFF88')
    setNodeSizeDefault  (60)
    setNodeFontSizeDefault (30)

Now we will add some visual mappings. Let’s map `moleculeType’ to node shapes. First, we can see which shapes are available in Cytoscape, then we can define the mapping with paired lists.

    getNodeShapes ()   # diamond, ellipse, trapezoid, triangle, etc.
    column <- 'moleculeType'
    values <- c ('kinase',  'TF','cytokine')
    shapes <- c ('DIAMOND', 'TRIANGLE', 'RECTANGLE')
    setNodeShapeMapping (column, values, shapes)

The node shape mapping is an example of a discrete mapping, where a style is defined for each, discrete value. This is useful for categorical data (like type) where there is only a limited set of possible values. This is in contast to the other two other types of mappings: continuous and passthrough. In the case of expression values, for example, we will want to use continuous mapping (e.g., to node color), defining a small set of control points, rather than an explicit color for each possible data value. Cytoscape will simply interpolate between the control points to provide a gradient of colors. Let’s try that one now

    column <- 'log2fc'
    control.points <- c (-3.0, 0.0, 3.0)
    colors <-  c ('#5588DD', '#FFFFFF', '#DD8855')
    setNodeColorMapping (column, control.points, colors)

Note that there are three colors and three control points. However, you can also specify two additional colors beyond the number of control points if you want to set extreme (or out-of-bounds) colors for values less than or greater than your control points.

    control.points <- c (-2.0, 0.0, 2.0)
    colors <-  c ('#2255CC', '#5588DD', '#FFFFFF', '#DD8855','#CC5522')
    setNodeColorMapping (column, control.points, colors)

Now, add a node size rule, using log2fc again as controlling node values.

    control.points = c (-3.0, 2.0, 3.0)
    sizes     = c (20, 80, 90)
    setNodeSizeMapping (column, control.points, sizes)

If you recall the third type of mapping, passthrough, we can see it already working in our current network example. The node labels! By default, the name column is mapped to the node label property using passthrough logic: the value is passed directly to the style property.

Selecting nodes

Let us now try selecting nodes in Cytoscape from R. Select the C node by name:

    selectNodes ('C','name')

    getSelectedNodes ()

Now we wish to extend the selected nodes to include the first neighbors of the already-selected node B. This is a common operation: for instance, after selecting one or more nodes based on experimental data or annotation, you may want to explore these in the context of interaction partners (in a protein-protein network) or in relation to upstream and downstream partners in a signaling or metabolic network. Type:

    selectFirstNeighbors ()

You will see that three nodes are now selected. Get their names back to R as a list:

    node.names <- getSelectedNodes ()

And, finally, deselection works as you’d expect by means of a general clearSelection function:

    clearSelection()
    ?clearSelection

Saving and export

Session files save everything. As with most project software, we recommend saving often!

    saveSession('vignette_session') #.cys

Note: If you don’t specify a complete path, the files will be saved relative to your current working directory in R.

    full.path=paste(getwd(),'vignette_image',sep='/')
    exportImage(full.path, 'PNG', zoom=200) #.png scaled by 200%
    exportImage(full.path, 'PDF') #.pdf
    ?exportImage