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 |

`FDR.cluster` |
A |

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 |

`CpGs.clust.not.reject ` |
A |

`clusters.reject ` |
A |

`clusters.not.reject ` |
A |

`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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## 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)
``` |

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