diggit (version 1.4.0)

conditional: Conditional analysis of CNVs

Description

This function performs the conditional analysis of fCNVs

Usage

conditional(x, ...)
"conditional"(x, pheno = "cond", group1, group2 = NULL, cnv = 0.2, mr = 0.01, mr.adjust = c("none", "fdr", "bonferroni"), modul = 0.01, modul.adjust = c("none", "fdr", "bonferroni"), fet.pval = 0.05, cores = 1, verbose = TRUE)

Arguments

x

Object of class diggit

...

Additional parameters to pass to the function

pheno

Character string indicating the feature for sample groups

group1

Character string indicating the treatment group

group2

Optional character string indicating the reference group

cnv

Single number or vector of two numbers indicating the thresholds for CNVs

mr

Either vector of character strings indicating the MR genes, or number indicating the corrected p-value threshold for selecting the MRs

mr.adjust

Character string indicating the multiple-hypothesis correction to apply to the MR p-values

modul

Number indicating the p-value threshold for a modulator to be considered associated with the MR activity

modul.adjust

Character string indicating the multiple-hypothesis correction to apply to the aQTL results

fet.pval

Number indicating the FET p-value threshold for the association between CNVs and sample groups

cores

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

verbose

Logical, whether progress should be reported

Value

Object of class diggit with conditional analysis results

Examples

data(gbm.expression, package="diggitdata")
data(gbm.cnv, package="diggitdata")
data(gbm.aracne, package="diggitdata")
dobj <- diggitClass(expset=gbmExprs, cnv=gbmCNV, regulon=gbmTFregulon)
dobj <- fCNV(dobj)
dobj <- aqtl(dobj, mr=c("CEBPD", "STAT3"), fcnv.adjust="fdr", verbose=FALSE)
dobj <- conditional(dobj, pheno="subtype", group1="MES", group2="PN", mr="STAT3", verbose=FALSE)
dobj