In LTLA/GenomicInteractions: Classes and Methods for Handling Genomic Interaction Data

require(knitr)
opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE)
library(BiocStyle)
require(GenomicInteractions)

Introduction

The r Biocpkg("GenomicInteractions") package provides methods to overlap interactions based on their genomic coordinates. This is particularly useful for identifying interactions that are anchored at genomic intervals of interest. or for identifying interactions that link two intervals of interest. To demonstrate, we will create a mock GenomicInteractions object below:

set.seed(9999)
all.regions <- GRanges(rep(c("chrA", "chrB"), c(10, 5)), 
    IRanges(c(0:9*10+1, 0:4*5+1), c(1:10*10, 1:5*5)))
index.1 <- sample(length(all.regions), 20, replace=TRUE)
index.2 <- sample(length(all.regions), 20, replace=TRUE)
gi <- GenomicInteractions(index.1, index.2, all.regions)
gi

One-dimensional interactions

Say we want to identify all interactions with at least one anchor region lying within a region of interest (e.g., a known promoter or gene). This is achieved with the findOverlaps method:

of.interest <- GRanges("chrA", IRanges(30, 60))
olap <- findOverlaps(gi, of.interest)
olap

This returns a Hits object containing pairs of indices, where each pair represents an overlap between the interaction (query) with a genomic interval (subject). Here, each reported interaction has at least one anchor region overlapping the interval specified in of.interest:

anchors(gi[queryHits(olap)])

Longer GRanges can be specified if there are several regions of interest:

of.interest2 <- GRanges(c("chrA", "chrB"),
    IRanges(c(30, 20), c(60, 30)))
findOverlaps(gi, of.interest2)

These overlaps are considered to be "one-dimensional" as they based on the linear genome. By default, one-dimensional overlaps are considered for both anchor regions of each interaction. The use.region argument can be set to specify which anchors in the GenomicInteractions object are to be overlapped. For example, to only identify interactions where the first anchor overlaps of.interest:

findOverlaps(gi, of.interest, use.region="first")

Standard arguments can be supplied to findOverlaps to modify its behaviour, e.g., type, minoverlap. The overlapsAny, countOverlaps and subsetByOverlaps methods are also available and behave as expected.

Two-dimensional interactions

A more complex situation involves identifying overlapping interactions in the two-dimensional interaction space. Say we are interested in an interaction betweeen two regions, represented by an GenomicInteractions object named paired.interest. We want to determine if any of our interactions in gi overlap with the existing interaction, e.g., to identify corresponding interactions between data sets.

This is achieved by passing both GenomicInteractions objects to findOverlaps(). The function will only consider two interactions to overlap if each anchor region of the new interaction overlaps a corresponding anchor region of the existing interaction. To illustrate:

paired.interest <- GenomicInteractions(of.interest, 
    GRanges("chrB", IRanges(10, 40)))
paired.interest
olap <- findOverlaps(gi, paired.interest)
olap

If we inspect the overlapping interactions, it is clear that interactions are only reported as overlapping if both of its anchors overlap the two anchors of paired.interest. Specifically, the first gi anchor overlaps the first paired.interest anchor and the second gi anchor overlaps the second paired.interest anchor; or the first gi anchor overlaps the second paired.interest anchor and the first gi anchor overlaps the second paired.interest anchor.

gi[queryHits(olap)]

The use.region argument controls the regions that are used in the two-dimensional overlaps. For example, setting use.region="match" ensures that only the first anchors are allowed to overlap each other, and only the second anchors are allowed to overlap each other:

findOverlaps(gi, paired.interest, use.region="match")

Again, arguments can be supplied to findOverlaps to tune its behaviour. The overlapsAny, countOverlaps and subsetByOverlaps methods are also available for these two-dimensional overlaps.

Linking sets of regions

A slightly different problem involves finding interactions that link any entries across two sets of regions. For example, we might be interested in identifying interactions between a set of genes and a set of enhancers. Using findOverlaps() to perform 2D overlaps would be tedious, as we would have to manually specify every possible gene-enhancer combination. Instead, our aim can be achieved using the linkOverlaps function:

all.genes <- GRanges("chrA", IRanges(0:9*10, 1:10*10))
all.enhancers <- GRanges("chrB", IRanges(0:9*10, 1:10*10))
out <- linkOverlaps(gi, all.genes, all.enhancers)
out

This returns a DataFrame object where each row species an interaction in query, the region it overlaps in subject1 (i.e., the gene), and the overlapping region in subject2 (i.e., the enhancer). If there are multiple overlaps to either set, all combinations of two overlapping regions are reported. One can also identify interactions within a single set by doing:

out <- linkOverlaps(gi, all.genes)
out

Here, both subject1 and subject2 refer to linked entries in all.genes.

Wrapping up

Details for any given method can be found through the standard documentation, e.g., ?linkOverlaps to get the man page for the linkOverlaps() method. Further questions on r Biocpkg("GenomicInteractions") functionality should be asked on the Bioconductor support site.

sessionInfo()

LTLA/GenomicInteractions documentation built on June 24, 2019, 2:09 p.m.