Basic Workflow 3: Visual Styles

by Kazuhiro Takemoto*

Updated by Keiichiro Ono

(The original tutorial was developed by Dr. Takemoto, and updated by Keiichiro Ono for cy-rest.)


Welcome to part 3 of the R tutorial. In this section, you will learn how to create your own Visual Styles programmatically.

# Basic setup

port.number = 1234
base.url = paste("http://localhost:", toString(port.number), "/v1", sep="")

Reload the Data

In this tutorial, we will use the same sample data as we used in the last section. Let’s reload everything again.

network.df <- read.table("data/eco_EM+TCA.txt")
network <-,directed=T)
g.tca <- simplify(network, remove.multiple=T, remove.loops=T)
g.tca$name = "Ec TCA Cycle"

Understanding Visual Style

Path Visualization

To understand Styles in Cytoscape, let’s visualize shortest path between two nodes in the network.

Find Paths in igraph

There are many built-in path finding algorithms in igraph.

  • Find all shortest paths from D-Glucose to 2-Oxoglutarate
paths <- get.all.shortest.paths(g.tca,"D-Glucose","2-Oxoglutarate",mode="out")

# Nodes in the first path
## Vertex sequence:
##  [1] "D-Glucose"      "G6P"            "F6P"            "F16BP"         
##  [5] "GAP"            "BPG"            "3PG"            "2PG"           
##  [9] "PEP"            "Pyruvate"       "Acetyl-CoA"     "Citrate"       
## [13] "cis-Aconitate"  "Isocitrate"     "Oxalosuccinate" "2-Oxoglutarate"
# Add new attribute "path" and set value 1 for this path
V(g.tca)[paths$res[[1]]]$path <- 1

# Edges in the path
## Edge sequence:
## [15] D-Glucose      -> G6P           
## [24] G6P            -> F6P           
## [19] F6P            -> F16BP         
## [17] F16BP          -> GAP           
## [26] GAP            -> BPG           
## [10] BPG            -> 3PG           
## [5]  3PG            -> 2PG           
## [4]  2PG            -> PEP           
## [41] PEP            -> Pyruvate      
## [42] Pyruvate       -> Acetyl-CoA    
## [7]  Acetyl-CoA     -> Citrate       
## [14] Citrate        -> cis-Aconitate 
## [50] cis-Aconitate  -> Isocitrate    
## [31] Isocitrate     -> Oxalosuccinate
## [38] Oxalosuccinate -> 2-Oxoglutarate
# Add path attribute
E(g.tca, path=paths$res[[1]])$path <- 1

Styles in Cytoscape

In igraph plot function, you need to specify actual visual property values directly. For example, if you want to paint the path found in the previous section in red, you need to set actual color (red) for each edge in the path. Instead, Cytoscape used a mapping mechanism called Visual Style and you can reuse the mapping instructions for multiple networks. For more details, please read the manual.

Visualizing Paths

Because Style in Cytoscape is so flexible, there are many ways to visualize your The simplest way to visualize paths in Cytoscape is making a new attributes for the edges.

Design your Style

To understand basics of Style, let’s build a simple Style from scratch including the following instructions:

  • Paint nodes and edges on the path in red
  • Use red for node labels
  • Since this is a directed graph, add arrows to the edges

First, we need to set default values. This is just an example. Be creative to improve your visualizations!

# Name of this new style = "PathVisualization3"

# Define default values
def.node.color <- list(
  visualProperty = "NODE_FILL_COLOR",
  value = "#EEEEEE"

def.node.size <- list(
  visualProperty = "NODE_SIZE",
  value = 12

def.node.border.width <- list(
  visualProperty = "NODE_BORDER_WIDTH",
  value = 0

def.edge.width <- list(
  visualProperty = "EDGE_WIDTH",
  value = 2

def.edge.color <- list(
  value = "#aaaaaa"
) = list(
  value = "#aaaaaa"

def.node.labelposition <- list(
  visualProperty = "NODE_LABEL_POSITION",
  value = "S,NW,c,6.00,0.00"  
) <- list(

defaults <- list(def.node.color, def.node.size, def.node.labelposition, 
                 def.edge.color, def.node.border.width,, 

It looks complicated, but actually it’s simple. All you have to do is defining key-value pair for each visual variables you want to change. It’s just like CSS.


Visual Mapping is the most important concept in Cytoscape. In Cytoscape, data controls the view. This means, once mapping is set, color, size, shape, or all other visual variables are controlled by one of the column values in your data table.

Let’s define a simple mapping: if path value is 1, use RED for painting objects.

# Visual Mappings
mappings = list()

# Actual mapping.  This is a reusable component.
pair1 = list(
  key = "1",
  value = "red"

# This mapping object is also reusable!
discrete.mappings = list(pair1)

# Assign the map for node, edge, and label colors.
node.color = list(
  map = discrete.mappings

node.label.color = list(
  map = discrete.mappings

edge.color = list(
  map = discrete.mappings
) = list(
  map = discrete.mappings

# One more instruction.  Use "name" column for label.
node.label = list(

mappings = list(node.color, node.label, node.label.color, edge.color,

style <- list(, defaults = defaults, mappings = mappings)
style.JSON <- toJSON(style)

style.url = paste(base.url, "styles", sep="/")
POST(url=style.url, body=style.JSON, encode = "json")
## Response [http://localhost:1234/v1/styles]
##   Status: 200
##   Content-type: application/json
## {"title": "PathVisualization3_6"}

Well, this may look a bit cumbersome for the first time because we make everything from scratch. However, in many cases, you can use preset Styles as your starting point and customize it for your purpose. But remember, you made a reusable set of instructions how to visualize your result controled by the data. Style object is just like a CSS for Cytoscape. It is reusable, and independent from your content (data).

In this section, you made a style programmatically, but of course, you can manually create Styles from GUI if you want.

Create Visualization with our Custom Style

Finally, send everything back to Cytoscape and visualize it!

send2cy(toCytoscape(g.tca),, 'circular')
## [1] 11154

Now you see the path!