testClusters: Tests CpG clusters
In BiSeq: Processing and analyzing bisulfite sequencing data

Description Usage Arguments Details Value Author(s) References See Also Examples

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

1	testClusters(locCor, FDR.cluster)

`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.

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

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.

Katja Hebestreit

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

estLocCor, trimClusters

## 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)