trimClusters {BiSeq} | R Documentation

## Trims CpG clusters

### Description

CpG clusters rejected in a previous step are trimmed.

### Usage

    trimClusters(clusters.rej, FDR.loc)

### Arguments

`clusters.rej` | Output of `testClusters`.
`FDR.loc` | Location-wise FDR level. Default is 0.2.

### Details

Not differentially methylated CpG sites are removed within the CpG clusters rejected by `testClusters`.

### Value

A `data.frame` containing the differentially methylated CpG sites.

### Author(s)

Katja Hebestreit

### References

Yoav Benjamini and Ruth Heller (2007): False Discovery Rates for Spatial Signals. American Statistical Association, 102 (480): 1272-81.

### See Also

`testClusters`

### Examples

    ## Variogram under Null hypothesis (for resampled data):
    data(vario)

    plot(vario$variogram$v)
    vario.sm <- smoothVariogram(vario, sill=0.9)

    # auxiliary object to get the pValsList for the test
    # results of interest:
    data(betaResults)
    vario.aux <- makeVariogram(betaResults, make.variogram=FALSE)

    # Replace the pValsList slot:
    vario.sm$pValsList <- vario.aux$pValsList

    ## vario.sm contains the smoothed variogram under the Null hypothesis as
    ## well as the p Values that the group has an effect on DNA methylation.

    locCor <- estLocCor(vario.sm)

    clusters.rej <- testClusters(locCor, FDR.cluster = 0.1)

    clusters.trimmed <- trimClusters(clusters.rej, FDR.loc = 0.05)

[Package BiSeq version 1.27.3 Index]