diggit (version 1.4.0)

marina: Inference of Master Regulators

Description

This function infers the master regulators for the transition between two phenotypes

Usage

marina(x, ...)
"marina"(x, y = NULL, mu = 0, regulon, per = 1000, cores = 1, verbose = TRUE)
"marina"(x, pheno = "cond", group1, group2 = NULL, mu = 0, regulon, per = 1000, cores = 1, verbose = TRUE)
"marina"(x, pheno, group1, group2 = NULL, mu = 0, regulon = NULL, per = 1000, cores = 1, verbose = TRUE)

Arguments

x

Object of class diggit, expressionSet object or numerical matrix containing the test samples

...

Additional arguments

y

Numerical matrix containing the control samples

mu

Number indicating the control mean when y is ommited

regulon

Transcriptional interactome

per

Interger indicating the number of permutations to compute the marina null model

cores

Integer indicating the number of cores to use (1 for Windows-based systems)

verbose

Logical, whether progress should be reported

pheno

Character string indicating the phenotype data to use

group1

Vector of character strings indicating the category from phenotype pheno to use as test group

group2

Vector of character strings indicating the category from phenotype pheno to use as control group

Value

Updated diggit object with Master Regulator results

Examples

cores <- 3*(Sys.info()[1] != "Windows")+1
data(gbm.expression, package="diggitdata")
data(gbm.aracne, package="diggitdata")

eset <- exprs(gbmExprs)
samples <- pData(gbmExprs)[["subtype"]]
x <- eset[, samples=="MES"]
y <- eset[, samples=="PN"]
dgo <- marina(x, y, regulon=gbmTFregulon, per=100, cores=cores)
dgo
diggitMR(dgo)[1:5]
dgo <- marina(gbmExprs, pheno="subtype", group1="MES", group2="PN", regulon=gbmTFregulon, per=100, cores=cores)
dgo
diggitMR(dgo)[1:5]
x <- diggitClass(expset=gbmExprs, regulon=gbmTFregulon)
dgo <- marina(x, pheno="subtype", group1="MES", group2="PN", per=100, cores=cores)
dgo
diggitMR(dgo)[1:5]