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Last data update: 2014.03.03

R: Compute moving average statistics by incorporating the...

cmarrt.ma

R Documentation

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'.

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.

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).

Note

The p-values are obtained under the Gaussian approximation. Therefore, it is important to check the normal quantile-quantile plot if the Gaussian approximation is valid. The function also outputs the computation under independence (ignoring the correlation structure) for comparisons.

Author(s)

Pei Fen Kuan, Adam Hinz

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.

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')