conditional {diggit} | R Documentation |
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