summariseByFeaturePairs: Summarise the number of interactions between two sets of...

In ComputationalRegulatoryGenomicsICL/GenomicInteractions-new: Utilities for handling genomic interaction data

Summarise the number of interactions between two sets of features.

Description

This function will calculate the number of observed interactions between two sets of features provided by the end-user. This allows the summarisation of the number of features of a specific type a particular region is involved in and how many interactions exist between them.

Usage

summariseByFeaturePairs(
  GIObject,
  features.one,
  feature.name.one,
  features.two,
  feature.name.two
)

## S4 method for signature 'GInteractions'
summariseByFeaturePairs(
  GIObject,
  features.one,
  feature.name.one,
  features.two,
  feature.name.two
)

Arguments

GIObject	An annotated GInteractions object
features.one	A GRanges object containing the feature set of interest
feature.name.one	The name of the first feature set of interest
features.two	A GRanges object containing the second feature set of interest
feature.name.two	The name of the second feature set of interest

Value

A data frame with one line for each range in ‘features’

Examples

data('hic_example_data')
data('mm9_refseq_promoters')
data('thymus_enhancers')
annotateInteractions(hic_example_data, list(promoter = mm9_refseq_promoters, enhancer = thymus_enh))
# can be slow so subset of features used for examples
p <- unique(unlist(head(regions(hic_example_data)$promoter.id)))
e <- unique(unlist(head(regions(hic_example_data)$enhancer.id)))
p <- p[!is.na(p)]
p <- mm9_refseq_promoters[p]
e <- e[!is.na(e)]
e <- thymus_enh[e]
ep_summary <- summariseByFeaturePairs(hic_example_data, p, 'promoter', e, 'enhancer')

ComputationalRegulatoryGenomicsICL/GenomicInteractions-new documentation built on April 10, 2022, 5:39 p.m.