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:
- General Metrics: Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples.
- Peak Overlap: Frequency, percentage, statistical significance of overlapping and non-overlapping peaks. This also includes Upset, precision-recall and correlation plots.
- 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.
To install EpiCompare use:
if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("EpiCompare") 👈 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
👈 Details
To install the development version of EpiCompare, use:
if (!require("remotes")) install.packages("remotes")
remotes::install_github("neurogenomics/EpiCompare")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
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
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 outputPrepare 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())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 usinglist(). 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 thepeakfilesinput. Assumes genome build is the same for each element in thepeakfileslist.reference: Genome build for thereferenceinput.blacklist: Genome build for theblacklistinput.
E.g.genome_build = list(peakfiles="hg38", reference="hg19", blacklist="hg19")
genome_build_outputGenome 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 datadata(hg19_blacklist)anddata(hg38_blacklist)respectively. For mouse mm10 genome, use built-in datadata(mm10_blacklist).output_dir: Please specify the path to directory, where allEpiCompareoutputs will be saved.
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 usinglist()and named usingnames(). To import Picard duplication metrics (.txt file) into R as data frame, usepicard <- read.table("/path/to/picard/output", header = TRUE, fill = TRUE).reference: Reference peak file(s) is used instat_plotandchromHMM_plot. File must be inGRangesobject, listed and named usinglist("reference_name" = GRanges_obect). If more than one reference is specified,EpiCompareoutputs individual reports for each reference. However, please note that this can take awhile.
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 areferencedataset is provided. The plot shows statistical significance (p/q-values) of sample peaks that are overlapping/non-overlapping with thereferencedataset.chromHMM_plot: ChromHMM annotation of peaks. If areferencedataset is provided, ChromHMM annotation of overlapping and non-overlapping peaks with thereferenceis 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).
👈 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. Ifinteract=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.
EpiCompare outputs the following:
- HTML report: A summary of all analyses saved in specified
output_dir - EpiCompare_file: if
save_output=TRUE, all plots generated byEpiComparewill be saved in EpiCompare_file directory also in specifiedoutput_dir
An example report comparing ATAC-seq and DNase-seq can be found here
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.
👈 Details
utils::sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
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## attached base packages:
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## loaded via a namespace (and not attached):
## [1] pillar_1.8.1 compiler_4.2.1 RColorBrewer_1.1-3
## [4] BiocManager_1.30.20 bitops_1.0-7 yulab.utils_0.0.6
## [7] tools_4.2.1 digest_0.6.31 jsonlite_1.8.4
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## [43] magrittr_2.0.3 BiocGenerics_0.44.0 biocViews_1.66.2
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## [52] utf8_1.2.3 RCurl_1.98-1.10 munsell_0.5.0
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