scoreClusters: Compute scores for each cluster

Description Usage Arguments Details Value Examples

View source: R/local_correlations.R

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

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

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

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library(GenomicRanges)

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 )

GabrielHoffman/decorate documentation built on Aug. 8, 2019, 1:48 p.m.