Starr (version 1.28.0)

cmarrt.ma: Compute moving average statistics by incorporating the correlation structure

Description

This function extends the moving average approach by incorporating the correlation structure. It also outputs the p-values of the standardized moving average statistics under the Gaussian approximation.

Usage

cmarrt.ma(eSet, probeAnno, chr=NULL, M=NULL,frag.length,window.opt='fixed.probe')

Arguments

eSet

ExpressionSet containing the normalized ratio

probeAnno

probeAnno object with mapping

chr

which chromosome should be analysed? If chr==NULL, all chromosome in the probeAnno object are taken.

M

rough estimate of the percentage of bound probes. If unknown, leave it NULL.

frag.length

average fragment length from sonication.

window.opt

option for sliding window, either "fixed.probe" or "fixed.gen.dist". Default is 'fixed.probe'.

Value

data.sort

datafile sorted by genomic position.

ma

unstandardized moving average(MA) statistics.

z.cmarrt

standardized MA under correlation structure.

z.indep

standardized MA under independence (ignoring correlation structure).

pv.cmarrt

p-values of probes under correlation.

pv.indep

p-values of probes under independence (ignoring correlation structure).

Details

Computation using window.opt = "fixed.probe" calculates the moving average statistics within a fixed number of probes and is more efficient. Use this option if the tiling array is regular with approximately constant resolution. window.opt="fixed.gen.dist" computes the moving average statistics over a fixed genomic distance.

References

P.F. Kuan, H. Chun, S. Keles (2008). CMARRT: A tool for the analysiz of ChIP-chip data from tiling arrays by incorporating the correlation structure. Pacific Symposium of Biocomputing13:515-526.

See Also

plotcmarrt,cmarrt.peak

Examples

# dataPath <- system.file("extdata", package="Starr")
# bpmapChr1 <- readBpmap(file.path(dataPath, "Scerevisiae_tlg_chr1.bpmap"))

# cels <- c(file.path(dataPath,"Rpb3_IP_chr1.cel"), file.path(dataPath,"wt_IP_chr1.cel"), 
# 	file.path(dataPath,"Rpb3_IP2_chr1.cel"))
# names <- c("rpb3_1", "wt_1","rpb3_2")
# type <- c("IP", "CONTROL", "IP")
# rpb3Chr1 <- readCelFile(bpmapChr1, cels, names, type, featureData=TRUE, log.it=TRUE)

# ips <- rpb3Chr1$type == "IP"
# controls <- rpb3Chr1$type == "CONTROL"

# rpb3_rankpercentile <- normalize.Probes(rpb3Chr1, method="rankpercentile")
# description <- c("Rpb3vsWT")
# rpb3_rankpercentile_ratio <- getRatio(rpb3_rankpercentile, ips, controls, description, fkt=median, featureData=FALSE)

# probeAnnoChr1 <- bpmapToProbeAnno(bpmapChr1)
# peaks <- cmarrt.ma(rpb3_rankpercentile_ratio, probeAnnoChr1, chr=NULL, M=NULL,250,window.opt='fixed.probe')