knitr::opts_chunk$set(message = FALSE, crop = NULL)

1 Introduction

The GRAVI workflow, for which this package is designed, uses sliding windows for differential signal analysis. However, the use of fixed-width windows, as is common under DiffBind-style (Ross-Innes et al. 2012) approaches is also possible with extraChIPs. This vignette focusses on using conventional peak calls and fixed-width approaches to replicate and extend these approaches.

The majority of examples below use heavily reduced datasets to provide general guidance on using the functions. Some results may appear trivial as a result, but will hopefully prove far more useful in a true experimental context. All data, along with this vignette are available here. Please place all contents of the data directory in a directory named data in your own working directory.

2 Setup

2.1 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, Rsamtools, csaw, BiocParallel and rtracklayer) can also be installed.

if (!"BiocManager" %in% rownames(installed.packages()))
pkg <- c(
  "tidyverse", "Rsamtools", "csaw", "BiocParallel", "rtracklayer", "edgeR", 
  "patchwork", "extraChIPs", "plyranges", "scales", "here", "quantro"
BiocManager::install(pkg, update = FALSE)

Once these packages are installed, we can load them easily


2.2 Data

All data for this vignette is expected to be in a sub-directory of the working directory named “data”, and all paths will be predicated on this. Please ensure you have all data in this location, obtained from here.

The data itself is ChIP-Seq data targeting the Estrogen Receptor (ER), and is taken from the cell-line ZR-75-1 cell-line using data from the BioProject , Pre-processing was performed using the prepareChIPs workflow, written in snakemake (Mölder et al. 2021) and all code is available at ER binding was assessed under Vehicle (E2) and DHT-stimulated (E2DHT) conditions. Using GRCh37 as the reference genome, a subset of regions found on chromosome 10 are included in this dataset for simplicity.

First we’ll define our sample data then define our two treatment groups. Defining a consistent colour palette for all plots is also a good habit to develop.

treat_levels <- c("E2", "E2DHT")
treat_colours <- setNames(c("steelblue", "red3"), treat_levels)
samples <- tibble(
  accession = paste0("SRR831518", seq(0, 5)),
  target = "ER",
  treatment = factor(rep(treat_levels, each = 3), levels = treat_levels)
## # A tibble: 6 × 3
##   accession  target treatment
##   <chr>      <chr>  <fct>    
## 1 SRR8315180 ER     E2       
## 2 SRR8315181 ER     E2       
## 3 SRR8315182 ER     E2       
## 4 SRR8315183 ER     E2DHT    
## 5 SRR8315184 ER     E2DHT    
## 6 SRR8315185 ER     E2DHT
accessions <- samples %>% 
  split(f = .$treatment) %>% 
  lapply(pull, "accession")

We’ll eventually be loading counts for differential signal analysis from a set of BamFiles, so first we’ll create a BamFileList with all of these files.

bfl <- here("data", "ER", glue("{samples$accession}.bam")) %>% 
  BamFileList() %>% 
  setNames(str_remove_all(names(.), ".bam"))

Seqinfo objects are the foundation of working with GRanges, so defining an object at the start of a workflow is good practice. This is simply enabled with extraChIPs by using defineSeqinfo() and specifying the appropriate genome.

sq <- defineSeqinfo("GRCh37")

Another key preparatory step for working with peaks is to define a set of regions as either blacklisted or grey-listed regions. The former are known problematic regions based on each genome, with data freely available from, whilst grey-listed regions are defined from potentially problematic regions as detected within the input sample. For our samples code for this is included in the previously provided repository (

greylist <- import.bed(here("data/chr10_greylist.bed"), seqinfo = sq)
blacklist <- import.bed( here("data/chr10_blacklist.bed"), seqinfo = sq)
omit_ranges <- c(greylist, blacklist)

3 Working With Peaks

The provided dataset includes six files produced by macs2 callpeak (Zhang et al. 2008) in the narrowPeak format, and these are able to be easily parsed using extraChIPs. We’ll immediately pass our black & grey-listed regions to our parsing function so we can exclude these regions right from the start. By passing the above seqinfo object to this function, we’re also telling importPeaks() to ignore any peaks not on the included chromosomes.

peaks <- here("data", "ER", glue("{samples$accession}_peaks.narrowPeak")) %>% 
  importPeaks(seqinfo = sq, blacklist = omit_ranges)

This will import the peaks from all files as a single GRangesList object, adding the file-name to each element by default. We can easily modify these names if we so wish.

names(peaks) <- str_remove_all(names(peaks), "_peaks.narrowPeak")

Once loaded, we can easily check how similar our replicates are using plotOverlaps(). When three or more sets of peaks are contained in the GRangesList, an UpSet plot will be drawn by default.

  peaks, min_size = 10, .sort_sets = FALSE, 
  set_col = treat_colours[as.character(samples$treatment)]

Optionally, specifying a column and a suitable function will produce an additional panel summarising that value. In the following, we’ll show the maximum score obtained, highlighting that for peaks identified in only one or two replicates, the overall signal intensity is generally lower, even in the sample with the strongest signal.

  peaks, min_size = 10, .sort_sets = FALSE, var = "score", f = "max",
   set_col = treat_colours[as.character(samples$treatment)]
*UpSet plot showing overlapping peaks across all replicates, with the maximum score across all replicates shown in the upper panel.*

Figure 1: UpSet plot showing overlapping peaks across all replicates, with the maximum score across all replicates shown in the upper panel.

A common task at this point may be to define consensus peaks within each treatment group, by retaining only the peaks found in 2 of the 3 replicates (p = 2/3). The default approach is to take the union of all ranges, with the returned object containing logical values for each sample, as well as the number of samples where an overlapping peak was found.

If we wish to retain any of the original columns, such as the macs2 callpeak score, we can simply pass the column names to makeConsensus()

consensus_e2 <- makeConsensus(peaks[accessions$E2], p = 2/3, var = "score")
consensus_e2dht <- makeConsensus(peaks[accessions$E2DHT], p = 2/3, var = "score")

Alternatively, we could find the centre of the peaks as part of this process, by averaging across the estimated peak centres for each sample. This is a very common step for ChIP-Seq data where the target is a transcription factor, and also forms a key step in the DiffBind workflow.

In the following code chunk, we first find the centre for each sample using the information provided by macs2, before retaining this column when calling makeConsensus(). This will return each of the individual centre-position estimates as a list for each merged range, and using vapply() we then take the mean position as our estimate for the combined peak centre.

consensus_e2 <- peaks[accessions$E2] %>% 
  endoapply(mutate, centre = start + peak) %>% 
  makeConsensus(p = 2/3, var = "centre") %>% 
  mutate(centre = vapply(centre, mean, numeric(1)))
## GRanges object with 164 ranges and 5 metadata columns:
##         seqnames            ranges strand |    centre SRR8315180 SRR8315181
##            <Rle>         <IRanges>  <Rle> | <numeric>  <logical>  <logical>
##     [1]    chr10 43048195-43048529      * |  43048362       TRUE       TRUE
##     [2]    chr10 43521739-43522260      * |  43522020       TRUE       TRUE
##     [3]    chr10 43540042-43540390      * |  43540272       TRUE      FALSE
##     [4]    chr10 43606238-43606573      * |  43606416       TRUE       TRUE
##     [5]    chr10 43851214-43851989      * |  43851719      FALSE       TRUE
##     ...      ...               ...    ... .       ...        ...        ...
##   [160]    chr10 99096784-99097428      * |  99097254       TRUE       TRUE
##   [161]    chr10 99168353-99168649      * |  99168502       TRUE       TRUE
##   [162]    chr10 99207868-99208156      * |  99207998      FALSE       TRUE
##   [163]    chr10 99331363-99331730      * |  99331595       TRUE       TRUE
##   [164]    chr10 99621632-99621961      * |  99621818      FALSE       TRUE
##         SRR8315182         n
##          <logical> <numeric>
##     [1]       TRUE         3
##     [2]       TRUE         3
##     [3]       TRUE         2
##     [4]       TRUE         3
##     [5]       TRUE         2
##     ...        ...       ...
##   [160]       TRUE         3
##   [161]       TRUE         3
##   [162]       TRUE         2
##   [163]       TRUE         3
##   [164]       TRUE         2
##   -------
##   seqinfo: 25 sequences from GRCh37 genome
consensus_e2dht <- peaks[accessions$E2DHT] %>% 
  endoapply(mutate, centre = start + peak) %>% 
  makeConsensus(p = 2/3, var = "centre") %>% 
  mutate(centre = vapply(centre, mean, numeric(1)))

We can also inspect these using plotOverlaps() provided we use a GRangesList for the input. Now that we only have two elements (one for each treatment) a VennDiagram will be generated instead of an UpSet plot.

GRangesList(E2 = granges(consensus_e2), E2DHT = granges(consensus_e2dht)) %>% 
  plotOverlaps(set_col = treat_colours[treat_levels])
*Overlap between consensus peaks identified in a treatment-specific manner*

Figure 2: Overlap between consensus peaks identified in a treatment-specific manner

We can now go one step further and define the set of peaks found in either treatment. Given we’re being inclusive here, we can leave p = 0 so any peak found in either treatment is included.

union_peaks <- GRangesList(
  E2 = select(consensus_e2, centre), 
  E2DHT = select(consensus_e2dht, centre)
) %>% 
  makeConsensus(var = c("centre")) %>% 
    centre = vapply(centre, mean, numeric(1)) %>% round(0)

Now we have a set of peaks, found in at least 2/3 of samples from either condition, with estimates of each peak’s centre. The next step would be to set all peaks as the same width based on the centre position, with a common width being 500bp.

In the following we’ll perform multiple operations in a single call mutate, so let’s make sure we know what’s happening.

  1. glue("{seqnames}:{centre}:{strand}") uses glue syntax to parse the seqnames, centre position and strand information as a character-like vector with a width of only 1, and using the estimated centre as the Range.
  2. We then coerce this to a GRanges object, before resizing to the desired width.
  3. We also add the original (un-centred) range as an additional column, retaining the GRanges structure, but discarding anything in the mcols() element, then
  4. Using colToRanges(), we take the centred ranges and place them as the core set of GRanges for this object.

This gives a GRanges object with all original information, but with centred peaks of a fixed width.

w <- 500
centred_peaks <- union_peaks %>% 
    centre = glue("{seqnames}:{centre}:{strand}") %>% 
      GRanges(seqinfo = sq) %>% 
      resize(width = w),
    union_peak = granges(.)
  ) %>% 

4 Counting Reads

Now we have our centred, fixed-width peaks, we can count reads using csaw::regionCounts() (Lun and Smyth 2016). We know our fragment length is about 200bp, so we can pass this to the function for a slightly more sophisticated approach to counting.

se <- regionCounts(bfl, centred_peaks, ext = 200)
## class: RangedSummarizedExperiment 
## dim: 188 6 
## metadata(2): final.ext param
## assays(1): counts
## rownames: NULL
## rowData names(4): E2 E2DHT n union_peak
## colnames(6): SRR8315180 SRR8315181 ... SRR8315184 SRR8315185
## colData names(4): bam.files totals ext rlen

The colData() element of the returned object as the columns bam.files, totals, ext and rlen, which are all informative and can be supplemented with our samples data frame. In the following, we’ll 1) coerce to a tibble, 2) left_join() the samples object, 3) add the accession as the sample column, 4) set the accession back as the rownames, then 5) coerce back to the required DataFrame() structure.

colData(se) <- colData(se) %>% 
  as_tibble(rownames = "accession") %>% 
  left_join(samples) %>% 
  mutate(sample = accession) %>% %>% 
  DataFrame(row.names = .$accession)
## DataFrame with 6 rows and 8 columns
##              accession              bam.files    totals       ext      rlen
##            <character>            <character> <integer> <integer> <integer>
## SRR8315180  SRR8315180 /home/steviep/github..    317845       200        75
## SRR8315181  SRR8315181 /home/steviep/github..    337623       200        75
## SRR8315182  SRR8315182 /home/steviep/github..    341998       200        75
## SRR8315183  SRR8315183 /home/steviep/github..    315872       200        75
## SRR8315184  SRR8315184 /home/steviep/github..    352908       200        75
## SRR8315185  SRR8315185 /home/steviep/github..    347709       200        75
##                 target treatment      sample
##            <character>  <factor> <character>
## SRR8315180          ER     E2     SRR8315180
## SRR8315181          ER     E2     SRR8315181
## SRR8315182          ER     E2     SRR8315182
## SRR8315183          ER     E2DHT  SRR8315183
## SRR8315184          ER     E2DHT  SRR8315184
## SRR8315185          ER     E2DHT  SRR8315185

For QC and visualisation, we can add an additional logCPM assay to our object as well.

assay(se, "logCPM") <- cpm(assay(se, "counts"), lib.size = se$totals, log = TRUE)

First we might like to check our distribution of counts

plotAssayDensities(se, assay = "counts", colour = "treat", trans = "log1p") +
  scale_colour_manual(values = treat_colours)