CePa (version 0.8.1)

plotGraph: Plot graph for the pathway network

Description

Plot graph for the pathway network

Usage

plotGraph(x, node.name = NULL, node.type = NULL, draw = TRUE,
    tool = c("igraph", "Rgraphviz"), graph.node.max.size = 20,
    graph.node.min.size = 3, graph.layout.method = NULL)

Value

A igraph object of the pathway

Arguments

x

a cepa object

node.name

node.name for each node

node.type

node.type for each node

draw

Whether to draw the graph

tool

Use which tool to visualize the graph. Choices are 'igraph' and 'Rgraphviz'

graph.node.max.size

max size of the node in the graph

graph.node.min.size

min size of the node in the graph

graph.layout.method

function of the layout method. For the list of available methods, see layout

Author

Zuguang Gu <[e-mail]>

Details

Graph view of the pathway where the size of node is proportional to centrality value of the node.

By default, the layout for the pathway tree-like. If the number of pathway nodes is large, the layout would be a random layout.

Two packages can be selected to visualize the graph: igraph and Rgraphviz. Default package is igraph (in fact, this package just uses the data generated from the layout function in igraph package, which are the coordinate of nodes and edges. And the I re-wrote the plotting function to generate the graph). From my personal view, Rgraphviz package generated more beautiful graphs.

If the tool is set as igraph, the function returns a igraph object. And if the tool is set as Rgraphviz, the function returns a graphAM class object. So if users don't satisfy, they can draw graphs of the network with their own settings.

The function is always called through plot.cepa.all and plot.cepa.

Examples

if (FALSE) {
data(PID.db)
# ORA extension
data(gene.list)
# will spend about 20 min
res.ora = cepa.all(dif = gene.list$dif, bk = gene.list$bk, pc = PID.db$NCI)
ora = get.cepa(res.ora, id = 5, cen = 3)
plotGraph(ora)
# GSA extension
# P53_symbol.gct and P53_cls can be downloaded from
# http://mcube.nju.edu.cn/jwang/lab/soft/cepa/
eset = read.gct("P53_symbol.gct")
label = read.cls("P53.cls", treatment="MUT", control="WT")
# will spend about 45 min
res.gsa = cepa.all(mat = eset, label = label, pc = PID.db$NCI)
gsa = get.cepa(res.gsa, id = 5, cen = 3)
plotGraph(gsa)
}