plotGraph: Plot graph for the pathway network
In jokergoo/CePa: Centrality-Based Pathway Enrichment

Description Usage Arguments Details Value Author(s) Examples

Plot graph for the pathway network

1
2
3

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)

`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`

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.

A igraph object of the pathway

Zuguang Gu <z.gu@dkfz.de>

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

## End(Not run)