cmarrt.ma {Starr} | 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 Biocomputing 13:515-526.

### 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')

[Package Starr version 1.43.0 Index]