callEnrichedRegions: Detection of enriched regions

In rMAT: R implementation from MAT program to normalize and analyze tiling arrays and ChIP-chip data.

Description

This function is used to locate putative enriched regions.

Usage

callEnrichedRegions(MatScore, dMax=600, dMerge=300, nProbesMin=8, method="score", threshold=5, verbose=FALSE)
      
 

Arguments

MatScore	This object contains an Range Data file
dMax	An integer value. The sliding window side of which the adjacent probes are to average upon in order to compute the rMAT score.
dMerge	An integer value. The maximum size to merge adjacent probes and categorize them as one region for scores of adjacent probes uniformly above the input threshold.
nProbesMin	An integer value. The minimum number of probes to average upon. If the number of probes within the interval is less than nProbesMin, the rMAT score of the region will not be computed.
method	A character string value equal to "score", "pValue" or "FDR". "score" denotes the method of calling enriched regions based sliding widow scores. "pValue" denotes the method of calling enriched regions based on p-values. Method "FDR" uses an FDR procedure to call regions. See Details below.
threshold	An integer value. The threshold of rMAT Score to be labeled as an enriched region. For method=1 or 3, the higher the score, the more confident we are about enriched regions. For method=2, the lower the score, the more confident we are about enriched regions.
verbose	A logical value. If verbose is TRUE, progress information would be displayed.

Details

For more details on the calculation of the rMAT score, pvalues, etc, please refer to the following paper: Johnson et al. Model-based analysis of tiling-arrays for ChIP-chip. Proc Natl Acad Sci USA (2006) vol. 103 (33) pp. 12457-62

Author(s)

Charles Cheung, [e-mail] and Raphael Gottardo, [e-mail] Arnaud Droit, [e-mail]

See Also

NormalizeProbes, computeMATScore.

Examples

	
####################################################
#The data are in inst/doc folder in rMAT package.
####################################################


pwd<-"" #INPUT FILES- BPMAP, ARRAYS, etc.
path<- system.file("extdata", "Sc03b_MR_v04_10000.bpmap",package="rMAT")

bpmapFile<-paste(pwd,path,sep="")

pathCEL<- system.file("extdata", "Swr1WTIP_Short.CEL",package="rMAT")
arrayFile<-paste(pwd,c(pathCEL),sep="")


# Show the all the different sequences
ReadBPMAPAllSeqHeader(bpmapFile)

# create a tiling Set from the corresponding data
# This will only grep the sequences with Sc
ScSet<-BPMAPCelParser(bpmapFile, arrayFile, verbose=FALSE,groupName="Sc")     

# show the object
show(ScSet)

# summarize its content
summary(ScSet)

ScSetNorm<-NormalizeProbes(ScSet, method="MAT",robust=FALSE, all=FALSE, standard=TRUE, verbose=FALSE)
	
RD<-computeMATScore(ScSetNorm,cName=NULL, dMax=600, verbose=TRUE) 
Enrich<-callEnrichedRegions(RD,dMax=600, dMerge=300, nProbesMin=8, method="score", threshold=1, verbose=FALSE)  
	

rMAT documentation built on May 6, 2019, 2:10 a.m.