⚖EpiCompare⚖
QC and Benchmarking of Epigenomic Datasets


download License: GPL-3

R build status

Authors: Sera Choi, Brian Schilder, Leyla Abbasova, Alan Murphy, Nathan Skene, Thomas Roberts, Hiranyamaya Dash

Updated: Oct-18-2024

Introduction

EpiCompare is an R package for comparing multiple epigenomic datasets for quality control and benchmarking purposes. The function outputs a report in HTML format consisting of three sections:

  1. General Metrics: Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples.
  2. Peak Overlap: Frequency, percentage, statistical significance of overlapping and non-overlapping peaks. This also includes Upset, precision-recall and correlation plots.
  3. Functional Annotation: Functional annotation (ChromHMM, ChIPseeker and enrichment analysis) of peaks. Also includes peak enrichment around Transcription Start Site.

Note: Peaks located in blacklisted regions and non-standard chromosomes are removed from the files prior to analysis.

Installation

Standard

To install EpiCompare use:

if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("EpiCompare")

All dependencies

👈 Details

Installing all Imports and Suggests will allow you to use the full functionality of EpiCompare right away, without having to stop and install extra dependencies later on.

To install these packages as well, use:

BiocManager::install("EpiCompare", dependencies=TRUE)

Note that this will increase installation time, but it means that you won’t have to worry about installing any R packages when using functions with certain suggested dependencies

Development

👈 Details

To install the development version of EpiCompare, use:

if (!require("remotes")) install.packages("remotes")
remotes::install_github("neurogenomics/EpiCompare")

Citation

If you use EpiCompare, please cite:

EpiCompare: R package for the comparison and quality control of epigenomic peak files (2022) Sera Choi, Brian M. Schilder, Leyla Abbasova, Alan E. Murphy, Nathan G. Skene, bioRxiv, 2022.07.22.501149; doi: https://doi.org/10.1101/2022.07.22.501149

Documentation

EpiCompare website

Docker/Singularity container

Bioconductor page

⚠️ Note on documentation versioning

The documentation in this README and the GitHub Pages website pertains to the development version of EpiCompare. Older versions of EpiCompare may have slightly different documentation (e.g. available functions, parameters). For documentation in older versions of EpiCompare, please see the Documentation section of the relevant version on Bioconductor

Usage

Load package and example datasets.

library(EpiCompare)
data("encode_H3K27ac") # example peakfile
data("CnT_H3K27ac") # example peakfile
data("CnR_H3K27ac") # example peakfile
data("CnT_H3K27ac_picard") # example Picard summary output
data("CnR_H3K27ac_picard") # example Picard summary output

Prepare input files:

# create named list of peakfiles 
peakfiles <- list("CnT"=CnT_H3K27ac, 
                  "CnR"=CnR_H3K27ac) 
# set ref file and name 
reference <- list("ENCODE_H3K27ac" = encode_H3K27ac) 
# create named list of Picard summary
picard_files <- list("CnT"=CnT_H3K27ac_picard, 
                     "CnR"=CnR_H3K27ac_picard)
👈 Tips on importing user-supplied files

EpiCompare::gather_files is helpful for identifying and importing peak or picard files.

# To import BED files as GRanges object
peakfiles <- EpiCompare::gather_files(dir = "path/to/peaks/", 
                                      type = "peaks.stringent")
# EpiCompare alternatively accepts paths (to BED files) as input 
peakfiles <- list(sample1="/path/to/peaks/file1_peaks.stringent.bed", 
                  sample2="/path/to/peaks/file2_peaks.stringent.bed")
# To import Picard summary output txt file as data frame
picard_files <- EpiCompare::gather_files(dir = "path/to/peaks", 
                                         type = "picard")

Run EpiCompare():

EpiCompare::EpiCompare(peakfiles = peakfiles,
                       genome_build = list(peakfiles="hg19",
                                           reference="hg38"),
                       genome_build_output = "hg19", 
                       picard_files = picard_files,
                       reference = reference,
                       run_all = TRUE
                       output_dir = tempdir())

Required Inputs

These input parameters must be provided:

👈 Details
  • peakfiles : Peakfiles you want to analyse. EpiCompare accepts peakfiles as GRanges object and/or as paths to BED files. Files must be listed and named using list(). E.g. list("name1"=peakfile1, "name2"=peakfile2).
  • genome_build : A named list indicating the human genome build used to generate each of the following inputs:
    • peakfiles : Genome build for the peakfiles input. Assumes genome build is the same for each element in the peakfiles list.
    • reference : Genome build for the reference input.
    • blacklist : Genome build for the blacklist input.
      E.g. genome_build = list(peakfiles="hg38", reference="hg19", blacklist="hg19")
  • genome_build_output Genome build to standardise all inputs to. Liftovers will be performed automatically as needed. Default is “hg19”.
  • blacklist : Peakfile as GRanges object specifying genomic regions that have anomalous and/or unstructured signals independent of the cell-line or experiment. For human hg19 and hg38 genome, use built-in data data(hg19_blacklist) and data(hg38_blacklist) respectively. For mouse mm10 genome, use built-in data data(mm10_blacklist).
  • output_dir : Please specify the path to directory, where all EpiCompare outputs will be saved.

Optional Inputs

The following input files are optional:

👈 Details
  • picard_files : A list of summary metrics output from Picard. Picard MarkDuplicates can be used to identify the duplicate reads amongst the alignment. This tool generates a summary output, normally with the ending .markdup.MarkDuplicates.metrics.txt. If this input is provided, metrics on fragments (e.g. mapped fragments and duplication rate) will be included in the report. Files must be in data.frame format and listed using list() and named using names(). To import Picard duplication metrics (.txt file) into R as data frame, use picard <- read.table("/path/to/picard/output", header = TRUE, fill = TRUE).
  • reference : Reference peak file(s) is used in stat_plot and chromHMM_plot. File must be in GRanges object, listed and named using list("reference_name" = GRanges_obect). If more than one reference is specified, EpiCompare outputs individual reports for each reference. However, please note that this can take awhile.

Optional Plots

By default, these plots will not be included in the report unless set to TRUE. To turn on all features at once, simply use the run_all=TRUE argument:

👈 Details
  • upset_plot : Upset plot of overlapping peaks between samples.
  • stat_plot : included only if a reference dataset is provided. The plot shows statistical significance (p/q-values) of sample peaks that are overlapping/non-overlapping with the reference dataset.
  • chromHMM_plot : ChromHMM annotation of peaks. If a reference dataset is provided, ChromHMM annotation of overlapping and non-overlapping peaks with the reference is also included in the report.
  • chipseeker_plot : ChIPseeker annotation of peaks.
  • enrichment_plot : KEGG pathway and GO enrichment analysis of peaks.
  • tss_plot : Peak frequency around (+/- 3000bp) transcriptional start site. Note that it may take awhile to generate this plot for large sample sizes.
  • precision_recall_plot : Plot showing the precision-recall score across the peak calling stringency thresholds.
  • corr_plot : Plot showing the correlation between the quantiles when the genome is binned at a set size. These quantiles are based on the intensity of the peak, dependent on the peak caller used (q-value for MACS2).

Other Options

👈 Details
  • chromHMM_annotation : Cell-line annotation for ChromHMM. Default is K562. Options are:
    • “K562” = K-562 cells
    • “Gm12878” = Cellosaurus cell-line GM12878
    • “H1hesc” = H1 Human Embryonic Stem Cell
    • “Hepg2” = Hep G2 cell
    • “Hmec” = Human Mammary Epithelial Cell
    • “Hsmm” = Human Skeletal Muscle Myoblasts
    • “Huvec” = Human Umbilical Vein Endothelial Cells
    • “Nhek” = Normal Human Epidermal Keratinocytes
    • “Nhlf” = Normal Human Lung Fibroblasts
  • interact : By default, all heatmaps (percentage overlap and ChromHMM heatmaps) in the report will be interactive. If set FALSE, all heatmaps will be static. N.B. If interact=TRUE, interactive heatmaps will be saved as html files, which may take time for larger sample sizes.
  • output_filename : By default, the report is named EpiCompare.html. You can specify the file name of the report here.
  • output_timestamp : By default FALSE. If TRUE, the filename of the report includes the date.

Outputs

EpiCompare outputs the following:

  1. HTML report: A summary of all analyses saved in specified output_dir
  2. EpiCompare_file: if save_output=TRUE, all plots generated by EpiCompare will be saved in EpiCompare_file directory also in specified output_dir

An example report comparing ATAC-seq and DNase-seq can be found here

Datasets

EpiCompare includes several built-in datasets:

👈 Details
  • encode_H3K27ac: Human H3K27ac peak file generated with ChIP-seq using K562 cell-line. Taken from ENCODE project. For more information, run ?encode_H3K27ac.
  • CnT_H3K27ac: Human H3K27ac peak file generated with CUT&Tag using K562 cell-line from Kaya-Okur et al., (2019). For more information, run ?CnT_H3K27ac.
  • CnR_H3K27ac: Human H3K27ac peak file generated with CUT&Run using K562 cell-line from Meers et al., (2019). For more details, run ?CnR_H3K27ac.

Contact

Neurogenomics Lab

UK Dementia Research Institute
Department of Brain Sciences
Faculty of Medicine
Imperial College London
GitHub
DockerHub

Session Info

👈 Details 
utils::sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] rmarkdown_2.28
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.5        jsonlite_1.8.9      renv_1.0.11        
##  [4] dplyr_1.1.4         compiler_4.4.1      BiocManager_1.30.25
##  [7] tidyselect_1.2.1    rvcheck_0.2.1       scales_1.3.0       
## [10] yaml_2.3.10         fastmap_1.2.0       here_1.0.1         
## [13] ggplot2_3.5.1       R6_2.5.1            generics_0.1.3     
## [16] knitr_1.48          yulab.utils_0.1.7   tibble_3.2.1       
## [19] desc_1.4.3          dlstats_0.1.7       rprojroot_2.0.4    
## [22] munsell_0.5.1       pillar_1.9.0        RColorBrewer_1.1-3 
## [25] rlang_1.1.4         utf8_1.2.4          badger_0.2.4       
## [28] xfun_0.48           fs_1.6.4            cli_3.6.3          
## [31] magrittr_2.0.3      rworkflows_1.0.2    digest_0.6.37      
## [34] grid_4.4.1          lifecycle_1.0.4     vctrs_0.6.5        
## [37] evaluate_1.0.1      glue_1.8.0          data.table_1.16.2  
## [40] fansi_1.0.6         colorspace_2.1-1    tools_4.4.1        
## [43] pkgconfig_2.0.3     htmltools_0.5.8.1