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All functions

adjustCovariateMatrix()
Use of matrix functions to adjust data via linear model
assignRegion2GeneDirection()
The results from all correlations are used to help assign direction using correlations that have a significant nominal pvalue
categorizeAndAnnotateCorrelationResults()
Annotation of genes that are significant and provide some additional downstream information for interpreting linked networks
corWithDistalPeak()
Iteration of correlations between distal peaks and other peaks residing near it with significant relationships with said gene
corWithDistalPeak_wrapper()
Wrapper function for corWithDistalPeak to iterate through the correlations
correlateByChromosome()
A wrapper function to carry out correlations in smaller chunks to help save on memory
createPeak2GeneObjects()
Create Peak-Gene Links Set up genomicranges objects for the data included in the correlation analysis. Data matrices must have unique feature IDs.
editCorResFields()
Annotation of significant promoter peak information
formatMatrixForCorrelation()
Format input
gene_counts
Example Gene Counts
matrixCorrelation()
Correlation of two matrices for more efficient calculations
peak_counts
Example Peak Counts
processCorrelations()
Process correlation results