plotGraph: Plot graph for the pathway network

In CePa: Centrality-Based Pathway Enrichment

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)

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

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.

Value

A igraph object of the pathway

Author(s)

Zuguang Gu <[e-mail]>

Examples

## Not run: 
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)

## End(Not run)

CePa documentation built on Oct. 8, 2024, 5:08 p.m.