View source: R/random_statistics.R

runPermutedData | R Documentation |

Run hierarchical clustering permuting features to get statistics under the null

```
runPermutedData(
X,
gr,
method = c("adjclust", "hclustgeo"),
quiet = FALSE,
alpha = 0.5,
adjacentCount = 500,
setNANtoZero = FALSE,
method.corr = c("pearson", "spearman"),
meanClusterSize = c(5, 10)
)
```

`X` |
data matrix were *rows* are features in sequential order |

`gr` |
GenomicRanges object with entries corresponding to the *rows* of X |

`method` |
'adjclust': adjacency constrained clustering. 'hclustgeo': incorporate data correlation and distance in bp |

`quiet` |
suppress messages |

`alpha` |
use by 'hclustgeo': mixture parameter weighing correlations (alpha=0) versus chromosome distances (alpha=1) |

`adjacentCount` |
used by 'adjclust': number of adjacent entries to compute correlation against |

`setNANtoZero` |
replace NAN correlation values with a zero |

`method.corr` |
Specify type of correlation: "pearson", "kendall", "spearman" |

`meanClusterSize` |
select target mean cluster size. Can be an array of values |

list of clusterScores and cutoff values at 5

```
library(GenomicRanges)
# load data
data('decorateData')
# First, analysis of original data
# Evaluate hierarchical clustering
treeList = runOrderedClusteringGenome( simData, simLocation )
# Choose cutoffs and return clusters
treeListClusters = createClusters( treeList, method='meanClusterSize', meanClusterSize=c(5, 10) )
# Evaluate score for each cluster
clstScore = scoreClusters(treeList, treeListClusters )
# Then, analysis of permuted data
# Evaluate hierarchical clustering
res = runPermutedData( simData, simLocation, meanClusterSize=c(5, 10) )
# LEF values for permuted data at 5% false positive rate
res$cutoffs$LEF
# Retain clusters that pass this criteria
clustInclude = retainClusters( clstScore, "LEF", res$cutoffs$LEF )
```

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