tna.mra {RTN} | R Documentation |
This function takes a TNA object and returns the results of the RMA analysis over a list of regulons from a transcriptional network (with multiple hypothesis testing corrections).
tna.mra(object, pValueCutoff=0.05, pAdjustMethod="BH", minRegulonSize=15, tnet="dpi", tfs=NULL, verbose=TRUE)
object |
a preprocessed object of class 'TNA' |
pValueCutoff |
a single numeric value specifying the cutoff for p-values considered significant. |
pAdjustMethod |
a single character value specifying the p-value adjustment method to be used (see 'p.adjust' for details). |
minRegulonSize |
a single integer or numeric value specifying the minimum number of elements in a regulon that must map to elements of the gene universe. Gene sets with fewer than this number are removed from the analysis. |
tnet |
a single character value specifying which transcriptional network should to used to compute the MRA analysis. Options: "dpi" and "ref". |
tfs |
an optional vector with transcription factor identifiers. |
verbose |
a single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE). |
a data frame in the slot "results", see 'rma' option in tna.get
.
Mauro Castro
data(tniData) data(tnaData) ## Not run: rtni <- tni.constructor(expData=tniData$expData, regulatoryElements=c("PTTG1","E2F2","FOXM1","E2F3","RUNX2"), rowAnnotation=tniData$rowAnnotation) rtni <- tni.permutation(rtni) rtni <- tni.bootstrap(rtni) rtni <- tni.dpi.filter(rtni) rtna <- tni2tna.preprocess(rtni, phenotype=tnaData$phenotype, hits=tnaData$hits, phenoIDs=tnaData$phenoIDs) #run MRA analysis pipeline rtna <- tna.mra(rtna) #get results tna.get(rtna,what="mra") ## End(Not run)