Evaluate the decay of correlation versus distance between features

For pairs of features evaluate the physical distance and the correlation

Usage

evaluateCorrDecay(treeList, gr, chromArray = seqlevels(gr), verbose = TRUE)

Arguments

treeList

list of hclust objects

gr

GenomicRanges object corresponding to features clustered in treeList

chromArray

Use this only this set of chromosmes. Can substantially reduce memory usage

verbose

show progress

Value

a data.frame of distance and correlation value for all pairs of features already evalauted in treeList. Note that runOrderedClusteringGenome() that returns treeList only evalutes correlation between a specified number of adjacent peaks

Examples

library(GenomicRanges)
library(ggplot2)

data('decorateData')

# Evaluate hierarchical clustering
treeList = runOrderedClusteringGenome( simData, simLocation ) 
#> 
Evaluating:chr20          
#> 

# Evaluate how correlation between features decays with distance
dfDist = evaluateCorrDecay( treeList, simLocation )
#> 
chr20      
#> 

# make plot
plotCorrDecay( dfDist )
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
#> ℹ Please use `after_stat(density)` instead.
#> ℹ The deprecated feature was likely used in the decorate package.
#>   Please report the issue at
#>   <https://github.com/DiseaseNeuroGenomics/decorate/issues>.
#> Warning: Removed 23 rows containing non-finite values (`stat_density2d()`).
#> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
#> Warning: Removed 23 rows containing non-finite values (`stat_smooth()`).
#> Warning: Removed 396 rows containing missing values (`geom_tile()`).
#> Warning: Removed 15 rows containing missing values (`geom_point()`).