cepa.all.parallel: use CePa package through parallel computing

In CePa: Centrality-Based Pathway Enrichment

use CePa package through parallel computing

Description

use CePa package through parallel computing

Usage

cepa.all.parallel(dif = NULL, bk = NULL, mat = NULL, label = NULL,
    pc, cen = default.centralities,
    cen.name = sapply(cen, function(x) ifelse(mode(x) == "name", deparse(x), x)),
    nlevel = "tvalue_abs", plevel = "mean", iter = 1000, ncores = 2)

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
cen	centrality measuments, it can ce a string, or a function
cen.name	centrality measurement names. By default it is parsed from cen argument
nlevel	node level transformation, should be one of "tvalue", "tvalue_sq", "tvalue_abs". Also self-defined functions are allowed, see cepa.univariate.all 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.all for detail.
iter	number of simulations
ncores	number of cores for parallel computing

Details

The function divides the pathway list into several parts and each part is sent to a core for parallel computing.

The package for parallel computing is snow.

Note: there may be warnings saying connections not closed. In fact I have closed connections after the parallel computing is done. I don't know why this happens. Maybe you breaked the computing ahead manually. However it does not matter unless you have obsessive compulsive disorder.

Value

A cepa.all class object

Author(s)

Zuguang Gu <[e-mail]>

References

Gu Z, Liu J, Cao K, Zhang J, Wang J. Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genes. BMC Syst Biol. 2012 Jun 6;6(1):56.

See Also

cepa.all

Examples

## Not run: 
data(PID.db)
# ORA extension
data(gene.list)
res.ora = cepa.all.parallel(dif = gene.list$dif, bk = gene.list$bk, pc = PID.db$NCI, ncores = 4)
# GSA extension
# P53_symbol.gct and P53_cls can be downloaded from
# http://mcube.nju.edu.cn/jwang/lab/soft/cepa/
eset = read.gct("http://mcube.nju.edu.cn/jwang/lab/soft/cepa/P53_symbol.gct")
label = read.cls("http://mcube.nju.edu.cn/jwang/lab/soft/cepa/P53.cls", 
    treatment="MUT", control="WT")
res.gsa = cepa.all.parallel(mat = eset, label = label, pc = PID.db$NCI, ncores = 4)

## End(Not run)

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