R: Motifs analysis and inference of 'dual regulons'.

mbrAssociation,MBR-method {RTNduals}

R Documentation

Motifs analysis and inference of 'dual regulons'.

Description

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.

Usage

## S4 method for signature 'MBR'
mbrAssociation(
  object,
  regulatoryElements = NULL,
  minRegulonSize = 15,
  doSizeFilter = FALSE,
  pValueCutoff = 0.001,
  pAdjustMethod = "bonferroni",
  estimator = "spearman",
  nPermutations = 1000,
  miFilter = TRUE,
  verbose = TRUE
)

Arguments

`object`	A processed object of class MBR
`regulatoryElements`	An optional character vector specifying which 'TNI' 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.
`doSizeFilter`	a logical value. If TRUE, negative and positive targets are independently verified by the 'minRegulonSize' argument.
`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' function for details).
`estimator`	A character value specifying the estimator used in the association analysis. One of "spearman" (default), "kendall", or "pearson".
`nPermutations`	A single integer value specifying the number of permutations for deriving p-values associating regulon pairs.
`miFilter`	A single logical value specifying to apply the 'miFilter' between two regulators.
`verbose`	A single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE).

Value

An MBR object with two data.frames in the slot 'results' listing the inferred 'dual regulons' and correspoding statistics.

Examples

##--- load a dataset for demonstration
data("tniData", package = "RTN")
gexp <- tniData$expData
annot <- tniData$rowAnnotation
tfs <- c("IRF8","IRF1","PRDM1","E2F3","STAT4","LMO4","ZNF552")

##--- construct a tni object
rtni <- tni.constructor(gexp, regulatoryElements = tfs, rowAnnotation=annot)

##--- compute regulons 
## set nPermutations>=1000
rtni <- tni.permutation(rtni, nPermutations=30)
## set nBootstrap>=100
rtni <- tni.bootstrap(rtni, nBootstrap=30)
## 'eps=NA' estimates threshold from empirical null
rtni <- tni.dpi.filter(rtni, eps=NA)

##--- construct a mbr object
rmbr <- tni2mbrPreprocess(rtni)

##--- run mbrAssociation 
## set nPermutations>=1000
rmbr <- mbrAssociation(rmbr, pValueCutoff = 0.05, nPermutations=30)

[Package RTNduals version 1.11.2 Index]