tna.get {RTN} | R Documentation |
Get information from individual slots in a TNA object. Available results from a previous analysis can be selected either by pvalue cutoff (default) or top significance.
tna.get(object, what="summary", order=TRUE, ntop=NULL, reportNames=TRUE, idkey=NULL)
object |
an object of class 'TNA' |
what |
a single character value specifying which information should be retrieved from the slots. Options: 'tnet', 'refnet', 'regulatoryElements', 'pheno', 'regulons','refregulons', 'para', 'mra', 'gsea1', 'gsea2','overlap', 'synergy', 'shadow', 'summary', 'annotation' and 'status'. Regulons can also be retrieved mapped to the available phenotype vector ('regulons.and.pheno' or 'refregulons.and.pheno') or mapped to the assiged mode of action ('regulons.and.mode' or 'refregulons.and.mode'). |
order |
a single logical value specifying whether or not the output data should be ordered by significance. Valid only for 'gsea1', 'gsea2', 'overlap', 'synergy' or 'shadow' options. |
ntop |
a single integer value specifying to select how many results of top significance from 'gsea', 'overlap', 'synergy' or 'shadow' options. |
reportNames |
a single logical value specifying to report regulons with 'names' (when reportNames=TRUE) or not (when reportNames=FALSE). This option is effective only if transcription factors were named with alternative identifiers in the pre-processing analysis. It takes effect on 'mra', 'gsea', 'overlap', 'synergy' and 'shadow' options. |
idkey |
an optional single character value specifying an ID name from the available 'TNA' annotation to be used as alias for data query outputs (obs. it has no effect on consolidated tables). |
get the slot content from an object of class 'TNA' TNA-class
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Mauro Castro
data(dt4rtn) # select 5 regulatoryElements for a quick demonstration! tfs4test <- dt4rtn$tfs[c("PTTG1","E2F2","FOXM1","E2F3","RUNX2")] ## Not run: rtni <- tni.constructor(expData=dt4rtn$gexp, regulatoryElements=tfs4test, rowAnnotation=dt4rtn$gexpIDs) rtni<-tni.permutation(rtni) rtni<-tni.bootstrap(rtni) rtni<-tni.dpi.filter(rtni) rtna<-tni2tna.preprocess(rtni, phenotype=dt4rtn$pheno, hits=dt4rtn$hits, phenoIDs=dt4rtn$phenoIDs) # run MRA analysis pipeline rtna <- tna.mra(rtna) # check summary tna.get(rtna,what="summary") # get results, e.g., MRA analysis tna.get(rtna,what="mra") ## End(Not run)