BiSeq (version 1.12.0)

testClusters: 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.

Value

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.

Details

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

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)