cepa.all: Apply CePa algorithm on a list of pathways under multiple...

In CePa: Centrality-Based Pathway Enrichment

Apply CePa algorithm on a list of pathways under multiple centralities

Description

Apply CePa algorithm on a list of pathways under multiple centralities

Usage

cepa.all(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)

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

Details

All the calculation can be achieved by this function. The function is wrapper of both ORA extension and GSA extension. It chooses corresponding procedure according to the arguments specified. If the arguments contain gene lists, then the calculation is sent to functions doing ORA extension. While if the arguments contain an expression matrix and a phenotype label, the GSA extension is evoked.

The function is a wrapper of cepa.ora.all and cepa.univariate.all.

This is the core function of the package. User can refer to the vignette to find how to use it (vignette("CePa")).

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

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

There is a parallel version of the function: cepa.all.parallel.

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, cepa.ora.all, cepa.univariate.all, cepa.all.parallel

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)

# 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")
# will spend about 45 min
res.gsa = cepa.all(mat = eset, label = label, pc = PID.db$NCI)

## End(Not run)

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