CePa (version 0.8.1)

cepa: Apply CePa algorithm on a single pathway

Description

Apply CePa algorithm on a single pathway

Usage

cepa(dif = NULL, bk = NULL, mat = NULL, label = NULL, pc, pathway = NULL,
    id = NULL, cen = "equal.weight",
    cen.name = if(is.function(cen)) deparse(substitute(cen))
    else if(mode(cen) == "name") deparse(cen) else cen,
    nlevel = "tvalue_abs", plevel = "mean", iter = 1000)

Value

A cepa class object

Arguments

dif

differential gene list

bk

background gene list. If background gene list are not specified, use whole human genes

mat

expression matrix in which rows are genes and columns are samples

label

a sampleLabel object identify the design of the microarray experiment

pc

a pathway.catalogue object storing information of pathways

pathway

an igraph object or edge list

id

identify which pathway should be analysis in the pathway catalogue

cen

centrality measuments, it can ce a string, or function has been quote

cen.name

centrality measurement names. This argument should be set if the cen is a function.

nlevel

node level transformation, should be one of "tvalue", "tvalue_sq", "tvalue_abs". Also self-defined functions are allowed, see cepa.univariate for detail.

plevel

pathway level transformation, should be one of "max", "min", "median", "sum", "mean", "rank". Also, self-defined functions are allowed, see cepa.univariate for detail.

iter

number of simulations

Author

Zuguang Gu <[e-mail]>

Details

The function is a wrapper of cepa.ora and cepa.univariate. Selection of which function depends on the arguments specified.

If dif, bk, pc, pathway, id, cen, cen.name and iter are specified, the arguments are passed to cepa.ora. The centrality-extension of over-representation analysis (ORA) will be applied on the list of differential genes.

If mat, label, pc, pathway, id, cen, cen.name, nlevel, plevel and iter are specified, the arguments are passed to cepa.univariate. The centrality-extension of gene-set analysis (GSA) will be applied on the whole gene expressions.

This function is always called by cepa.all. But you can still use it if you want to analysis a single pathway under a specific centrality.

See Also

cepa.all

Examples

if (FALSE) {

data(PID.db)

# ORA extension
data(gene.list)
# will spend about 20 min
res.ora = cepa(dif = gene.list$dif, bk = gene.list$bk, pc = PID.db$NCI, id = 2)

# 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 take about 45 min
res.gsa = cepa(mat = eset, label = label, pc = PID.db$NCI, id = 2)
}