In steveped/extraChIPs: Additional functions for working with ChIP-Seq data

knitr::opts_chunk$set(message = FALSE, warning = FALSE)

Introduction

This package is designed to enable the Gene Regulatory Analysis using Variable IP (GRAVI) workflow, as a method for detecting differential binding in ChIP-Seq datasets. As a workflow focussed on data integration, most functions provided by the package extraChIPs are designed to enable comparison across datasets. This vignette looks primarily at functions which work with GenomicRanges objects.

Installation

In order to use the package extraChIPs and follow this vignette, we recommend using the package BiocManager hosted on CRAN. Once this is installed, the additional packages required for this vignette (tidyverse, plyranges and Gviz) can also be installed.

if (!"BiocManager" %in% rownames(installed.packages()))
  install.packages("BiocManager")
BiocManager::install(c("tidyverse", "plyranges", "Gviz"))
BiocManager::install("extraChIPs")

Coercion

The advent of the tidyverse has led to tibble objects becoming a common alternative to data.frame or DataFrame objects. Simple functions within extraChIP enable coercion from GRanges, GInteractions and DataFrame objects to tibble objects, with list columns correctly handled. By default these coercion functions will coerce GRanges elements to a character vector. Similarly, GRanges represented as a character column can be coerced to the ranges element of a GRanges object.

First let's coerce from a tibble (or S3 data.frame) to a GRanges

library(tidyverse)
library(extraChIPs)
set.seed(73)
df <- tibble(
  range = c("chr1:1-10:+", "chr1:5-10:+", "chr1:5-6:+"),
  gene_id = "gene1",
  tx_id = paste0("transcript", 1:3),
  score = runif(3)
)
df
gr <- colToRanges(df, "range")
gr

Coercion back to a tibble will place the ranges as a character column by default. However, this can be turned off and the conventional coercion from as.data.frame will instead be applied, internally wrapped in as_tibble()

as_tibble(gr)
as_tibble(gr, rangeAsChar = FALSE)

A simple feature which may be useful for printing gene names using rmarkdown is contained in collapseGenes(). Here a character vector of gene names is collapsed into a glue object of length `, with gene names rendered in italics by default.

gn <- c("Gene1", "Gene2", "Gene3")
collapseGenes(gn)

Formation of Consensus Peaks

The formation of consensus peaks incorporating ranges from multiple replicates is a key part of many ChIP-Seq analyses. A common format returned by tools such as mcas2 callpeak is the narrowPeak (or broadPeak) format. Sets of these across multiple replicates can imported using the function importPeaks(), which returns a GRangesList()

fl <- system.file(
    c("extdata/ER_1.narrowPeak", "extdata/ER_2.narrowPeak"),
    package = "extraChIPs"
)
peaks <- importPeaks(fl)
names(peaks) <- c("ER_1", "ER_2")

In the above we have loaded the peaks from two replicates from the Estrogen Receptor (ER) in T-47D cells. To form consensus peaks we can place a restriction on the number of replicates an overlapping peak needs to appear in. By default, the function makeConsensus() sets the proportion to be zero (p = 0) so all peaks are retained. Note that a logical vector/column is returned for each replicate, along with the number of replicates the consensus peak is derived from.

makeConsensus(peaks)

However, we may wish for peaks to appear in all replicates, so we can set the argument p = 1 (or any other value $0 \leq p \leq 1$).

makeConsensus(peaks, p = 1)

In addition, we may wish to keep the values from the mcols as part of our consensus peaks, such as the qValue. This can be specified using the var argument, and will return a CompressedList as returned by reduceMC(), seen below.

makeConsensus(peaks, p = 1, var = "qValue")

We could even identify the peak centre and pass these to the set of consensus peaks for downstream peak re-centreing.

library(plyranges)
peaks %>% 
    endoapply(mutate, centre = start + peak) %>% 
    makeConsensus(p = 1, var = "centre")

We could then find the mean of the peak centres for an averaged centre.

peaks %>% 
    endoapply(mutate, centre = start + peak) %>% 
    makeConsensus(p = 1, var = "centre") %>% 
    mutate(centre = vapply(centre, mean, numeric(1)))

Simple Operations Retaining mcols()

The standard set operations implemented in the package GenomicRanges will always drop the mcols element by default. The extraChIPs functions reduceMC(), setdiffMC(), intersectMC() and unionMC() all produce the same output as their similarly-named functions, however, the mcols() elements in the query object are also returned. Where required, columns are coerced into CompressedList columns. This can particularly useful when needed to propagate the information contained in the initial ranges through to subsequent analytic steps

Simplifying single GRanges objects

The classical approach to defining TSS regions for a set of transcripts would be to use the function resize()`, setting the width as 1.

tss <- resize(gr, width = 1)
tss

As we can see, two transcripts start at position 5, so we may choose to reduce this, which would lose the mcols element. The alternative reduceMC() will retain all mcols.

GenomicRanges::reduce(tss)
reduceMC(tss)

By default, this function will attempt to coerce mcols to a new mcol of the appropriate type, however, when multiple values are inevitable such as for the tx_id column above, these will be coerced to a CompressedList. The simplification of the multiple values seen in the gene_id can also be turned off if desired should repeated values be important for downstream analysis.

reduceMC(tss, simplify = FALSE) 

This allows for simple integration with tidyverse nesting strategies.

reduceMC(tss, simplify = FALSE) %>% 
  as_tibble() %>% 
  unnest(everything())

Whilst reduceMC relies on the range-reduction as implemented in GenomicRanges::reduce(), some alternative approaches are included, such as chopMC(), which finds identical ranges and nests the mcols element as CompressedList objects.

chopMC(tss)

In the case of the object tss, this output is identical to reduceMC(), however, given that there are no identical ranges in gr the two functions would behave very differently on that object.

A final operation for simplifying GRanges objects would be distinctMC() which is a wrapper to dplyr]::distinct incorporating both the range and mcols. The columns to search can be called using <data-masking> approaches as detailed in the manual.

distinctMC(tss)
distinctMC(tss, gene_id)

Set Operations with Two GRanges Objects

Whilst reduce/reduceMC is applied to a single GRanges object, the set operation functions intersect, setdiff and union are valuable approaches for comparing ranges. Using the *MC() functions will retain mcols elements from the query range.

peaks <- GRanges(c("chr1:1-5", "chr1:9-12:*"))
peaks$peak_id <- c("peak1", "peak2")
GenomicRanges::intersect(gr, peaks, ignore.strand = TRUE)
intersectMC(gr, peaks, ignore.strand = TRUE)
setdiffMC(gr, peaks, ignore.strand = TRUE)
unionMC(gr, peaks, ignore.strand = TRUE)

There is a performance overhead to preparation of mcols as CompressedList objects and all mcols in the query object will be returned. If only wishing to retain a subset of mcols, these should be selected prior to passing to these functions.

gr %>% 
  select(tx_id) %>% 
  intersectMC(peaks, ignore.strand = TRUE)

Overlapping proportions

Whilst the functions findOverlaps() and overlapsAny() are extremely useful, the addition of propOverlap() returns a numeric vector with the proportion of each query range (x) which overlaps any range in the subject range (y)

propOverlap(gr, peaks)

This is also extended to enable comparison across multiple features and to classify each peak by which features that it overlaps the most strongly.

bestOverlap(gr, peaks, var = "peak_id")

More Complex Operations

Range Partitioning

In addition to standard set operations, one set of ranges can be used to partition another set of ranges, returning mcols from both ranges. Ranges from the query range (x) are returned after being partitioned by the ranges in the subject range (y). Subject ranges used for partitioning must be non-overlapping, and if overlapping ranges are provided, these will be reduced prior to partitioning.

This enables the identification of specific ranges from the query range (x) which overlap ranges from the subject range (y) Under this approach, mcols from both query and subject ranges will be returned to enable the clear ranges which are common and distinct within the two sets of ranges.

partitionRanges(gr, peaks)

Whilst this shares some similarity with intersectMC() the additional capabilities provide greater flexibility.

partitionRanges(gr, peaks) %>% 
  subset(is.na(peak_id))

Stitching Ranges

Using the function stitchRanges() we are able to join together sets of nearby ranges, but with the option of placing clear barriers between ranges, across which ranges cannot be joined. This may be useful if joining enhancers to form putative super-enhancers, but explicitly omitting defined promoter regions.

enh <- GRanges(c("chr1:1-10", "chr1:101-110", "chr1:181-200"))
prom <- GRanges("chr1:150:+")
se <- stitchRanges(enh, exclude = prom, maxgap = 100)
se

As a visualisation (below) ranges within 100bp were stitched together, however the region defined as a 'promoter' acted as a barrier and ranges were not stitched together across this barrier.

knitr::include_graphics("stitched.png")

Session Info

sessionInfo()

steveped/extraChIPs documentation built on Aug. 1, 2024, 12:36 a.m.