testClusters {BiSeq} | R Documentation

## Tests CpG clusters

### Description

CpG clusters are tested with a cluster-wise FDR level.

### Usage

    testClusters(locCor, FDR.cluster)

### Arguments

`locCor` | Output of `estLocCor`.
`FDR.cluster` | A `numeric`. The WFDR (weighted FDR) level at which the CpG clusters should be tested. Default is 0.05.

### Details

CpG clusters containing at least one differentially methylated location are detected.

### Value

A list is returned:

`FDR.cluster` | Chosen WFDR (weighted FDR) for clusters.
`CpGs.clust.reject` | A `list` of the CpG sites together with test results within clusters that were rejected.
`CpGs.clust.not.reject` | A `list` of the CpG sites together with test results within clusters that were not rejected.
`clusters.reject` | A `GRanges` of the clusters that were rejected.
`clusters.not.reject` | A `GRanges` of the clusters that were not rejected.
`sigma.clusters.reject` | The standard deviations for z-scores within each rejected cluster.
`variogram` | The variogram matrix.
`m` | Number of clusters tested.
`k` | Number of clusters rejected.
`u.1` | Cutoff point of the largest P value rejected.

### 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

`estLocCor`, `trimClusters`

### 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)

[Package BiSeq version 1.32.0 Index]