scoreClusters: Compute scores for each cluster

In GabrielHoffman/decorate: Differential Epigenetic Coregulation Test

Compute scores for each cluster

Description

For each cluster compute summary statistics for the cluster to measure how strong the correlation structure is. Clusters with weak correlation structure can be dropped from downstream analysis.

Usage

scoreClusters(treeList, treeListClusters, BPPARAM = SerialParam())

Arguments

treeList	list of hclust objects
treeListClusters	from createClusters()
BPPARAM	parameters for parallel evaluation

Details

For each cluster, extract the correlation matrix and return the mean absolute correlation; the 75th, 90th and 95th quantile absolute correlation, and LEF, the leading eigen-value fraction which is the fraction of variance explained by the leading eigen value of the matrix abs(C).

Value

for all pairs of peaks within windowSize, report distance

Examples

library(GenomicRanges)
library(BiocParallel)

data('decorateData')

# Evaluate hierarchical clustering
treeList = runOrderedClusteringGenome( simData, simLocation ) 

# Choose cutoffs and return clusters
treeListClusters = createClusters( treeList )

# Evaluate score for each cluster
clstScore = scoreClusters(treeList, treeListClusters, BPPARAM = SerialParam() )

GabrielHoffman/decorate documentation built on May 23, 2023, 1:29 a.m.