conditional-methods: Conditional analysis of CNVs
In diggit: Inference of Genetic Variants Driving Cellular Phenotypes

Description Usage Arguments Value Examples

This function performs the conditional analysis of fCNVs

conditional(x, ...)

## S4 method for signature 'diggit'
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)

`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

Object of class diggit with conditional analysis results

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