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README.md
In ggcoverage: Visualize Genome Coverage with Various Annotations
ggcoverage - Visualize and annotate genome coverage with ggplot2
Introduction
The goal of ggcoverage is simplify the process of visualizing genome
coverage. It contains three main parts:
- Load the data:
ggcoverage can load BAM, BigWig (.bw), BedGraph
files from various NGS data, including WGS, RNA-seq, ChIP-seq,
ATAC-seq, et al.
- Create genome coverage plot
- Add annotations:
ggcoverage supports six different
annotations:
- base and amino acid annotation: Visualize genome coverage at
single-nucleotide level with bases and amino acids.
- GC annotation: Visualize genome coverage with GC content
- gene annotation: Visualize genome coverage across whole gene
- transcription annotation: Visualize genome coverage across
different transcripts
- ideogram annotation: Visualize the region showing on whole
chromosome
- peak annotation: Visualize genome coverage and peak
identified
ggcoverage utilizes ggplot2 plotting system, so its usage is
ggplot2-style!
Installation
ggcoverage is an R package distributed as part of the
CRAN. To install the package, start R and
enter:
# install via CRAN
install.package("ggcoverage")
# install via Github
# install.package("remotes") #In case you have not installed it.
remotes::install_github("showteeth/ggcoverage")
In general, it is recommended to install from Github repository (update more timely).
Once ggcoverage is installed, it can be loaded by the following
command.
library("rtracklayer")
library("ggcoverage")
RNA-seq data
Load the data
The RNA-seq data used here are from Transcription profiling by high
throughput sequencing of HNRNPC knockdown and control HeLa
cells,
we select four sample to use as example: ERR127307_chr14,
ERR127306_chr14, ERR127303_chr14, ERR127302_chr14, and all bam files
are converted to bigwig file with
deeptools.
Load metadata:
# load metadata
meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
sample.meta = read.csv(meta.file)
sample.meta
#> SampleName Type Group
#> 1 ERR127302_chr14 KO_rep1 KO
#> 2 ERR127303_chr14 KO_rep2 KO
#> 3 ERR127306_chr14 WT_rep1 WT
#> 4 ERR127307_chr14 WT_rep2 WT
Load track files:
# track folder
track.folder = system.file("extdata", "RNA-seq", package = "ggcoverage")
# load bigwig file
track.df = LoadTrackFile(track.folder = track.folder, format = "bw",
meta.info = sample.meta)
# check data
head(track.df)
#> seqnames start end score Type Group
#> 1 chr14 21572751 21630650 0 KO_rep1 KO
#> 2 chr14 21630651 21630700 1 KO_rep1 KO
#> 3 chr14 21630701 21630800 4 KO_rep1 KO
#> 4 chr14 21630801 21657350 0 KO_rep1 KO
#> 5 chr14 21657351 21657450 1 KO_rep1 KO
#> 6 chr14 21657451 21663550 0 KO_rep1 KO
Prepare mark region:
# create mark region
mark.region=data.frame(start=c(21678900,21732001,21737590),
end=c(21679900,21732400,21737650),
label=c("M1", "M2", "M3"))
# check data
mark.region
#> start end label
#> 1 21678900 21679900 M1
#> 2 21732001 21732400 M2
#> 3 21737590 21737650 M3
Load GTF file:
gtf.file = system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
gtf.gr = rtracklayer::import.gff(con = gtf.file, format = 'gtf')
Basic coverage
basic.coverage = ggcoverage(data = track.df, color = "auto",
mark.region = mark.region, range.position = "out")
basic.coverage
You can also change Y axis style:
basic.coverage = ggcoverage(data = track.df, color = "auto",
mark.region = mark.region, range.position = "in")
basic.coverage
Add gene annotation
basic.coverage +
geom_gene(gtf.gr=gtf.gr)
Add transcript annotation
basic.coverage +
geom_transcript(gtf.gr=gtf.gr,label.vjust = 1.5)
Add ideogram
basic.coverage +
geom_gene(gtf.gr=gtf.gr) +
geom_ideogram(genome = "hg19",plot.space = 0)
#> Loading ideogram...
#> Loading ranges...
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
basic.coverage +
geom_transcript(gtf.gr=gtf.gr,label.vjust = 1.5) +
geom_ideogram(genome = "hg19",plot.space = 0)
#> Loading ideogram...
#> Loading ranges...
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
DNA-seq data
CNV
Load the data
The DNA-seq data used here are from Copy number work
flow,
we select tumor sample, and get bin counts with
cn.mops::getReadCountsFromBAM with WL 1000.
# track file
track.file = system.file("extdata", "DNA-seq", "CNV_example.txt", package = "ggcoverage")
track.df = read.table(track.file, header = TRUE)
# check data
head(track.df)
#> seqnames start end score Type Group
#> 1 chr4 61743001 61744000 17 tumor tumor
#> 2 chr4 61744001 61745000 14 tumor tumor
#> 3 chr4 61745001 61746000 13 tumor tumor
#> 4 chr4 61746001 61747000 16 tumor tumor
#> 5 chr4 61747001 61748000 25 tumor tumor
#> 6 chr4 61748001 61749000 24 tumor tumor
Basic coverage
basic.coverage = ggcoverage(data = track.df,color = NULL, mark.region = NULL,
region = 'chr4:61750000-62,700,000', range.position = "out")
basic.coverage
Add GC annotations
Add GC, ideogram and gene annotaions.
# load genome data
library("BSgenome.Hsapiens.UCSC.hg19")
#> Loading required package: BSgenome
#> Loading required package: Biostrings
#> Loading required package: XVector
#>
#> Attaching package: 'Biostrings'
#> The following object is masked from 'package:base':
#>
#> strsplit
# create plot
basic.coverage +
geom_gc(bs.fa.seq=BSgenome.Hsapiens.UCSC.hg19) +
geom_gene(gtf.gr=gtf.gr) +
geom_ideogram(genome = "hg19")
#> Loading ideogram...
#> Loading ranges...
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
Single-nucleotide level
Load the data
# prepare sample metadata
sample.meta <- data.frame(
SampleName = c("tumorA.chr4.selected"),
Type = c("tumorA"),
Group = c("tumorA")
)
# load bam file
bam.file = system.file("extdata", "DNA-seq", "tumorA.chr4.selected.bam", package = "ggcoverage")
track.df <- LoadTrackFile(
track.file = bam.file,
meta.info = sample.meta,
single.nuc=TRUE, single.nuc.region="chr4:62474235-62474295"
)
head(track.df)
#> seqnames start end score Type Group
#> 1 chr4 62474235 62474236 5 tumorA tumorA
#> 2 chr4 62474236 62474237 5 tumorA tumorA
#> 3 chr4 62474237 62474238 5 tumorA tumorA
#> 4 chr4 62474238 62474239 6 tumorA tumorA
#> 5 chr4 62474239 62474240 6 tumorA tumorA
#> 6 chr4 62474240 62474241 6 tumorA tumorA
Default color scheme
For base and amino acid annotation, we have following default color
schemes, you can change with nuc.color and aa.color parameters.
Default color scheme for base annotation is Clustal-style, more
popular color schemes is available
here.
# color scheme
nuc.color = c("A" = "#ff2b08", "C" = "#009aff", "G" = "#ffb507", "T" = "#00bc0d")
# create plot
graphics::par(mar = c(1, 5, 1, 1))
graphics::image(
1:length(nuc.color), 1, as.matrix(1:length(nuc.color)),
col = nuc.color,
xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n"
)
graphics::text(1:length(nuc.color), 1, names(nuc.color))
graphics::mtext(
text = "Base", adj = 1, las = 1,
side = 2
)
Default color scheme for amino acid annotation is from Residual
colours: a proposal for
aminochromography:
aa.color = c(
"D" = "#FF0000", "S" = "#FF2400", "T" = "#E34234", "G" = "#FF8000", "P" = "#F28500",
"C" = "#FFFF00", "A" = "#FDFF00", "V" = "#E3FF00", "I" = "#C0FF00", "L" = "#89318C",
"M" = "#00FF00", "F" = "#50C878", "Y" = "#30D5C8", "W" = "#00FFFF", "H" = "#0F2CB3",
"R" = "#0000FF", "K" = "#4b0082", "N" = "#800080", "Q" = "#FF00FF", "E" = "#8F00FF",
"*" = "#FFC0CB", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF"
)
graphics::par(mar = c(1, 5, 1, 1))
graphics::image(
1:5, 1:5, matrix(1:length(aa.color),nrow=5),
col = rev(aa.color),
xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n"
)
graphics::text(expand.grid(1:5,1:5), names(rev(aa.color)))
graphics::mtext(
text = "Amino acids", adj = 1, las = 1,
side = 2
)
Add base and amino acid annotation
ggcoverage(data = track.df, color = "grey", range.position = "out", single.nuc=T, rect.color = "white") +
geom_base(bam.file = bam.file,
bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19) +
geom_ideogram(genome = "hg19",plot.space = 0)
#> Loading ideogram...
#> Loading ranges...
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
ChIP-seq data
The ChIP-seq data used here are from
DiffBind,
I select four sample to use as example: Chr18_MCF7_input,
Chr18_MCF7_ER_1, Chr18_MCF7_ER_3, Chr18_MCF7_ER_2, and all bam
files are converted to bigwig file with
deeptools.
Create metadata:
# load metadata
sample.meta = data.frame(SampleName=c('Chr18_MCF7_ER_1','Chr18_MCF7_ER_2','Chr18_MCF7_ER_3','Chr18_MCF7_input'),
Type = c("MCF7_ER_1","MCF7_ER_2","MCF7_ER_3","MCF7_input"),
Group = c("IP", "IP", "IP", "Input"))
sample.meta
#> SampleName Type Group
#> 1 Chr18_MCF7_ER_1 MCF7_ER_1 IP
#> 2 Chr18_MCF7_ER_2 MCF7_ER_2 IP
#> 3 Chr18_MCF7_ER_3 MCF7_ER_3 IP
#> 4 Chr18_MCF7_input MCF7_input Input
Load track files:
# track folder
track.folder = system.file("extdata", "ChIP-seq", package = "ggcoverage")
# load bigwig file
track.df = LoadTrackFile(track.folder = track.folder, format = "bw",
meta.info = sample.meta)
# check data
head(track.df)
#> seqnames start end score Type Group
#> 1 chr18 76799701 76800000 439.316 MCF7_ER_1 IP
#> 2 chr18 76800001 76800300 658.974 MCF7_ER_1 IP
#> 3 chr18 76800301 76800600 219.658 MCF7_ER_1 IP
#> 4 chr18 76800601 76800900 658.974 MCF7_ER_1 IP
#> 5 chr18 76800901 76801200 0.000 MCF7_ER_1 IP
#> 6 chr18 76801201 76801500 219.658 MCF7_ER_1 IP
Prepare mark region:
# create mark region
mark.region=data.frame(start=c(76822533),
end=c(76823743),
label=c("Promoter"))
# check data
mark.region
#> start end label
#> 1 76822533 76823743 Promoter
Basic coverage
basic.coverage = ggcoverage(data = track.df, color = "auto", region = "chr18:76822285-76900000",
mark.region=mark.region, show.mark.label = FALSE)
basic.coverage
Add annotations
Add gene, ideogram and peak annotations. To create peak
annotation, we first get consensus peaks with
MSPC.
# get consensus peak file
peak.file = system.file("extdata", "ChIP-seq", "consensus.peak", package = "ggcoverage")
basic.coverage +
geom_gene(gtf.gr=gtf.gr) +
geom_peak(bed.file = peak.file) +
geom_ideogram(genome = "hg19",plot.space = 0)
#> Loading ideogram...
#> Loading ranges...
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
Code of Conduct
Please note that the ggcoverage project is released with a
Contributor Code of
Conduct.
By contributing to this project, you agree to abide by its terms.
Try the ggcoverage package in your browser
Any scripts or data that you put into this service are public.
ggcoverage documentation built on Sept. 6, 2022, 9:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
ggcoverage - Visualize and annotate genome coverage with ggplot2
Introduction
The goal of ggcoverage is simplify the process of visualizing genome
coverage. It contains three main parts:
- Load the data:
ggcoveragecan load BAM, BigWig (.bw), BedGraph files from various NGS data, including WGS, RNA-seq, ChIP-seq, ATAC-seq, et al. - Create genome coverage plot
- Add annotations:
ggcoveragesupports six different annotations:- base and amino acid annotation: Visualize genome coverage at single-nucleotide level with bases and amino acids.
- GC annotation: Visualize genome coverage with GC content
- gene annotation: Visualize genome coverage across whole gene
- transcription annotation: Visualize genome coverage across different transcripts
- ideogram annotation: Visualize the region showing on whole chromosome
- peak annotation: Visualize genome coverage and peak identified
ggcoverage utilizes ggplot2 plotting system, so its usage is
ggplot2-style!
Installation
ggcoverage is an R package distributed as part of the
CRAN. To install the package, start R and
enter:
# install via CRAN
install.package("ggcoverage")
# install via Github
# install.package("remotes") #In case you have not installed it.
remotes::install_github("showteeth/ggcoverage")
In general, it is recommended to install from Github repository (update more timely).
Once ggcoverage is installed, it can be loaded by the following
command.
library("rtracklayer")
library("ggcoverage")
RNA-seq data
Load the data
The RNA-seq data used here are from Transcription profiling by high throughput sequencing of HNRNPC knockdown and control HeLa cells, we select four sample to use as example: ERR127307_chr14, ERR127306_chr14, ERR127303_chr14, ERR127302_chr14, and all bam files are converted to bigwig file with deeptools.
Load metadata:
# load metadata
meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
sample.meta = read.csv(meta.file)
sample.meta
#> SampleName Type Group
#> 1 ERR127302_chr14 KO_rep1 KO
#> 2 ERR127303_chr14 KO_rep2 KO
#> 3 ERR127306_chr14 WT_rep1 WT
#> 4 ERR127307_chr14 WT_rep2 WT
Load track files:
# track folder
track.folder = system.file("extdata", "RNA-seq", package = "ggcoverage")
# load bigwig file
track.df = LoadTrackFile(track.folder = track.folder, format = "bw",
meta.info = sample.meta)
# check data
head(track.df)
#> seqnames start end score Type Group
#> 1 chr14 21572751 21630650 0 KO_rep1 KO
#> 2 chr14 21630651 21630700 1 KO_rep1 KO
#> 3 chr14 21630701 21630800 4 KO_rep1 KO
#> 4 chr14 21630801 21657350 0 KO_rep1 KO
#> 5 chr14 21657351 21657450 1 KO_rep1 KO
#> 6 chr14 21657451 21663550 0 KO_rep1 KO
Prepare mark region:
# create mark region
mark.region=data.frame(start=c(21678900,21732001,21737590),
end=c(21679900,21732400,21737650),
label=c("M1", "M2", "M3"))
# check data
mark.region
#> start end label
#> 1 21678900 21679900 M1
#> 2 21732001 21732400 M2
#> 3 21737590 21737650 M3
Load GTF file:
gtf.file = system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
gtf.gr = rtracklayer::import.gff(con = gtf.file, format = 'gtf')
Basic coverage
basic.coverage = ggcoverage(data = track.df, color = "auto",
mark.region = mark.region, range.position = "out")
basic.coverage
You can also change Y axis style:
basic.coverage = ggcoverage(data = track.df, color = "auto",
mark.region = mark.region, range.position = "in")
basic.coverage
Add gene annotation
basic.coverage +
geom_gene(gtf.gr=gtf.gr)
Add transcript annotation
basic.coverage +
geom_transcript(gtf.gr=gtf.gr,label.vjust = 1.5)
Add ideogram
basic.coverage +
geom_gene(gtf.gr=gtf.gr) +
geom_ideogram(genome = "hg19",plot.space = 0)
#> Loading ideogram...
#> Loading ranges...
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
basic.coverage +
geom_transcript(gtf.gr=gtf.gr,label.vjust = 1.5) +
geom_ideogram(genome = "hg19",plot.space = 0)
#> Loading ideogram...
#> Loading ranges...
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
DNA-seq data
CNV
Load the data
The DNA-seq data used here are from Copy number work
flow,
we select tumor sample, and get bin counts with
cn.mops::getReadCountsFromBAM with WL 1000.
# track file
track.file = system.file("extdata", "DNA-seq", "CNV_example.txt", package = "ggcoverage")
track.df = read.table(track.file, header = TRUE)
# check data
head(track.df)
#> seqnames start end score Type Group
#> 1 chr4 61743001 61744000 17 tumor tumor
#> 2 chr4 61744001 61745000 14 tumor tumor
#> 3 chr4 61745001 61746000 13 tumor tumor
#> 4 chr4 61746001 61747000 16 tumor tumor
#> 5 chr4 61747001 61748000 25 tumor tumor
#> 6 chr4 61748001 61749000 24 tumor tumor
Basic coverage
basic.coverage = ggcoverage(data = track.df,color = NULL, mark.region = NULL,
region = 'chr4:61750000-62,700,000', range.position = "out")
basic.coverage
Add GC annotations
Add GC, ideogram and gene annotaions.
# load genome data
library("BSgenome.Hsapiens.UCSC.hg19")
#> Loading required package: BSgenome
#> Loading required package: Biostrings
#> Loading required package: XVector
#>
#> Attaching package: 'Biostrings'
#> The following object is masked from 'package:base':
#>
#> strsplit
# create plot
basic.coverage +
geom_gc(bs.fa.seq=BSgenome.Hsapiens.UCSC.hg19) +
geom_gene(gtf.gr=gtf.gr) +
geom_ideogram(genome = "hg19")
#> Loading ideogram...
#> Loading ranges...
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
Single-nucleotide level
Load the data
# prepare sample metadata
sample.meta <- data.frame(
SampleName = c("tumorA.chr4.selected"),
Type = c("tumorA"),
Group = c("tumorA")
)
# load bam file
bam.file = system.file("extdata", "DNA-seq", "tumorA.chr4.selected.bam", package = "ggcoverage")
track.df <- LoadTrackFile(
track.file = bam.file,
meta.info = sample.meta,
single.nuc=TRUE, single.nuc.region="chr4:62474235-62474295"
)
head(track.df)
#> seqnames start end score Type Group
#> 1 chr4 62474235 62474236 5 tumorA tumorA
#> 2 chr4 62474236 62474237 5 tumorA tumorA
#> 3 chr4 62474237 62474238 5 tumorA tumorA
#> 4 chr4 62474238 62474239 6 tumorA tumorA
#> 5 chr4 62474239 62474240 6 tumorA tumorA
#> 6 chr4 62474240 62474241 6 tumorA tumorA
Default color scheme
For base and amino acid annotation, we have following default color
schemes, you can change with nuc.color and aa.color parameters.
Default color scheme for base annotation is Clustal-style, more
popular color schemes is available
here.
# color scheme
nuc.color = c("A" = "#ff2b08", "C" = "#009aff", "G" = "#ffb507", "T" = "#00bc0d")
# create plot
graphics::par(mar = c(1, 5, 1, 1))
graphics::image(
1:length(nuc.color), 1, as.matrix(1:length(nuc.color)),
col = nuc.color,
xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n"
)
graphics::text(1:length(nuc.color), 1, names(nuc.color))
graphics::mtext(
text = "Base", adj = 1, las = 1,
side = 2
)
Default color scheme for amino acid annotation is from Residual colours: a proposal for aminochromography:
aa.color = c(
"D" = "#FF0000", "S" = "#FF2400", "T" = "#E34234", "G" = "#FF8000", "P" = "#F28500",
"C" = "#FFFF00", "A" = "#FDFF00", "V" = "#E3FF00", "I" = "#C0FF00", "L" = "#89318C",
"M" = "#00FF00", "F" = "#50C878", "Y" = "#30D5C8", "W" = "#00FFFF", "H" = "#0F2CB3",
"R" = "#0000FF", "K" = "#4b0082", "N" = "#800080", "Q" = "#FF00FF", "E" = "#8F00FF",
"*" = "#FFC0CB", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF"
)
graphics::par(mar = c(1, 5, 1, 1))
graphics::image(
1:5, 1:5, matrix(1:length(aa.color),nrow=5),
col = rev(aa.color),
xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n"
)
graphics::text(expand.grid(1:5,1:5), names(rev(aa.color)))
graphics::mtext(
text = "Amino acids", adj = 1, las = 1,
side = 2
)
Add base and amino acid annotation
ggcoverage(data = track.df, color = "grey", range.position = "out", single.nuc=T, rect.color = "white") +
geom_base(bam.file = bam.file,
bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19) +
geom_ideogram(genome = "hg19",plot.space = 0)
#> Loading ideogram...
#> Loading ranges...
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
ChIP-seq data
The ChIP-seq data used here are from DiffBind, I select four sample to use as example: Chr18_MCF7_input, Chr18_MCF7_ER_1, Chr18_MCF7_ER_3, Chr18_MCF7_ER_2, and all bam files are converted to bigwig file with deeptools.
Create metadata:
# load metadata
sample.meta = data.frame(SampleName=c('Chr18_MCF7_ER_1','Chr18_MCF7_ER_2','Chr18_MCF7_ER_3','Chr18_MCF7_input'),
Type = c("MCF7_ER_1","MCF7_ER_2","MCF7_ER_3","MCF7_input"),
Group = c("IP", "IP", "IP", "Input"))
sample.meta
#> SampleName Type Group
#> 1 Chr18_MCF7_ER_1 MCF7_ER_1 IP
#> 2 Chr18_MCF7_ER_2 MCF7_ER_2 IP
#> 3 Chr18_MCF7_ER_3 MCF7_ER_3 IP
#> 4 Chr18_MCF7_input MCF7_input Input
Load track files:
# track folder
track.folder = system.file("extdata", "ChIP-seq", package = "ggcoverage")
# load bigwig file
track.df = LoadTrackFile(track.folder = track.folder, format = "bw",
meta.info = sample.meta)
# check data
head(track.df)
#> seqnames start end score Type Group
#> 1 chr18 76799701 76800000 439.316 MCF7_ER_1 IP
#> 2 chr18 76800001 76800300 658.974 MCF7_ER_1 IP
#> 3 chr18 76800301 76800600 219.658 MCF7_ER_1 IP
#> 4 chr18 76800601 76800900 658.974 MCF7_ER_1 IP
#> 5 chr18 76800901 76801200 0.000 MCF7_ER_1 IP
#> 6 chr18 76801201 76801500 219.658 MCF7_ER_1 IP
Prepare mark region:
# create mark region
mark.region=data.frame(start=c(76822533),
end=c(76823743),
label=c("Promoter"))
# check data
mark.region
#> start end label
#> 1 76822533 76823743 Promoter
Basic coverage
basic.coverage = ggcoverage(data = track.df, color = "auto", region = "chr18:76822285-76900000",
mark.region=mark.region, show.mark.label = FALSE)
basic.coverage
Add annotations
Add gene, ideogram and peak annotations. To create peak annotation, we first get consensus peaks with MSPC.
# get consensus peak file
peak.file = system.file("extdata", "ChIP-seq", "consensus.peak", package = "ggcoverage")
basic.coverage +
geom_gene(gtf.gr=gtf.gr) +
geom_peak(bed.file = peak.file) +
geom_ideogram(genome = "hg19",plot.space = 0)
#> Loading ideogram...
#> Loading ranges...
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.
Code of Conduct
Please note that the ggcoverage project is released with a
Contributor Code of
Conduct.
By contributing to this project, you agree to abide by its terms.
Try the ggcoverage package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
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