Description Usage Arguments Value See Also Examples

View source: R/extractCorrelationScores.R

Extract sample-level correlation scores for each cluster

1 2 3 4 5 6 7 8 9 10 | ```
extractCorrelationScores(
epiSignal,
gRanges,
clustList,
method = c("deltaSLE", "Delaneau"),
method.corr = c("pearson", "kendall", "spearman"),
BPPARAM = bpparam(),
rho = 0.1,
sumabs = 1
)
``` |

`epiSignal` |
matrix or EList of epigentic signal. Rows are features and columns are samples |

`gRanges` |
GenomciRanges corresponding to the rows of epiSignal |

`clustList` |
list of cluster assignments |

`method` |
"deltaSLE", "Delaneau" |

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

`BPPARAM` |
parameters for parallel evaluation |

`rho` |
used only for sle.score(). A positive constant such that cor(Y) + diag(rep(rho,p)) is positive definite. See sLED::sLED() |

`sumabs` |
used only for sle.score(). regularization paramter. Value of 1 gives no regularization, sumabs*sqrt(p) is the upperbound of the L_1 norm of v, controlling the sparsity of solution. Must be between 1/sqrt(p) and 1. See sLED::sLED() |

matrix of scores of each sample for each cluster

sle.score delaneau.score

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
library(GenomicRanges)
# load data
data('decorateData')
# Evaluate hierarchical clustering
# adjacentCount is the number of adjacent peaks considered in correlation
treeList = runOrderedClusteringGenome( simData, simLocation)
# Choose cutoffs and return cluster
treeListClusters = createClusters( treeList, method = "meanClusterSize", meanClusterSize=c( 10, 20, 30, 40, 50) )
# Evaluate strength of correlation for each cluster
clstScore = scoreClusters(treeList, treeListClusters )
# Filter to retain only strong clusters
clustInclude = retainClusters( clstScore, "LEF", 0.30 )
# get retained clusters
treeListClusters_filter = filterClusters( treeListClusters, clustInclude)
# collapse similar clusters
treeListClusters_collapse = collapseClusters( treeListClusters_filter, simLocation)
# get correlation scores for each sample for each cluster
corScores = extractCorrelationScores( simData, simLocation, treeListClusters_collapse )
``` |

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