Description Usage Arguments Value Examples
This function takes an MBR object and compares the shared regulon targets in order to test whether regulon pairs agree on the predicted downstream effects.
1 2 3 4 | ## S4 method for signature 'MBR'
mbrAssociation(object, regulatoryElements1 = NULL,
regulatoryElements2 = NULL, minRegulonSize = 30, prob = 0.95,
estimator = "spearman", pAdjustMethod = "BH", verbose = TRUE)
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object |
A processed object of class MBR evaluated by the
methods |
regulatoryElements1 |
An optional character vector specifying which 'TNI1' regulatory elements should be evaluated. If 'NULL' all regulatory elements will be evaluated. |
regulatoryElements2 |
An optional character vector specifying which 'TNI2' regulatory elements should be evaluated. If 'NULL' all regulatory elements will be evaluated. |
minRegulonSize |
A single integer or numeric value specifying the minimum number of elements in a regulon. Gene sets with fewer than this number are removed from the analysis. |
prob |
A quantile filter applyed to the association metric used to infer 'dual regulons'. |
estimator |
A character value specifying the estimator used in the association analysis. One of "spearman" (default), "kendall", or "pearson", can be abbreviated. |
pAdjustMethod |
A single character value specifying the p-value adjustment method to be used (see 'p.adjust' for details). |
verbose |
A single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE). |
An MBR object with two data.frames in the slot 'results' listing the inferred 'dual regulons' and a hypergeometric test for each 'dual regulon'.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | data("dt4rtn", package = "RTN")
gexp <- dt4rtn$gexp
annot <- dt4rtn$gexpIDs
tfs1 <- dt4rtn$tfs[c("IRF8","IRF1","PRDM1","AFF3","E2F3")]
tfs2 <- dt4rtn$tfs[c("HCLS1","STAT4","STAT1","LMO4","ZNF552")]
##---mbrPreprocess
rmbr <- mbrPreprocess(gexp=gexp, regulatoryElements1 = tfs1,
regulatoryElements2=tfs2, gexpIDs=annot)
##---mbrPermutation
rmbr <- mbrPermutation(rmbr, nPermutations=10)
##---mbrBootstrap
rmbr <- mbrBootstrap(rmbr, nBootstrap=10)
##---mbrDpiFilter
rmbr <- mbrDpiFilter(rmbr)
##---mbrAssociation
rmbr <- mbrAssociation(rmbr, prob=0.75)
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