### Install ggcoverage from GitHub

Source: https://showteeth.github.io/ggcoverage/index.html

Installs the ggcoverage package directly from its GitHub repository. This is recommended for the most up-to-date version.

```R
# install.package("remotes")   #In case you have not installed it.
remotes::install_github("showteeth/ggcoverage")
```

--------------------------------

### Example: Loading data and adding gene annotation

Source: https://showteeth.github.io/ggcoverage/reference/geom_gene.html

This example demonstrates loading track files and GTF data, then combining a basic coverage plot with gene annotations using geom_gene.

```R
# library(ggcoverage)
# library(utils)
# library(rtracklayer)
# meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
# sample.meta <- utils::read.csv(meta.file)
# 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
# )
# gtf.file <- system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
# gtf.gr <- rtracklayer::import.gff(con = gtf.file, format = "gtf")
# basic.coverage <- ggcoverage(data = track.df, color = "auto", range.position = "out")
# basic.coverage + geom_gene(gtf.gr = gtf.gr)

```

--------------------------------

### Install ggcoverage from CRAN and GitHub

Source: https://showteeth.github.io/ggcoverage/articles/ggcoverage.html

Install the ggcoverage package from CRAN or the latest version from GitHub. It is recommended to install from GitHub for more timely updates. Ensure dependencies like GenomeMatrix are installed if needed.

```R
# install via CRAN (v0.7.1) # old version, it's better to install via Github
install.packages("ggcoverage")

# install via Github (v1.2.0)
# install.package("remotes")   #In case you have not installed it.
# BiocManager::install("areyesq89/GenomeMatrix") # In case of possible dependency error
remotes::install_github("showteeth/ggcoverage")
```

--------------------------------

### Basic ggprotein Plot Example

Source: https://showteeth.github.io/ggcoverage/reference/ggprotein.html

Example of how to use the ggprotein function with essential arguments. Ensure the library is loaded and file paths are correctly specified.

```R
# library(ggcoverage)
# coverage.file <- system.file("extdata", "ProteProteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
# fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
# protein.id = "sp|P02769|ALBU_BOVIN"
# ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)

```

--------------------------------

### Example Usage of GetConsensusPeak

Source: https://showteeth.github.io/ggcoverage/reference/GetConsensusPeak.html

This example demonstrates how to use the GetConsensusPeak function with a specified peak file. Ensure the ggcoverage library is loaded and the peak file path is correctly set.

```R
# library(ggcoverage)
# peak.file <- system.file("extdata", "ChIP-seq", "consensus.peak", package = "ggcoverage")
# peak.df <- GetConsensusPeak(peak.file = peak.file)
```

--------------------------------

### Install ggcoverage from CRAN

Source: https://showteeth.github.io/ggcoverage/index.html

Installs the ggcoverage package from the CRAN repository. Use this if you need a stable release.

```R
install.packages("ggcoverage")
```

--------------------------------

### Load Required Libraries

Source: https://showteeth.github.io/ggcoverage/index.html

Loads the necessary R libraries for using ggcoverage and related functionalities. Ensure these are installed before loading.

```R
library("rtracklayer")
library("ggcoverage")
library("ggpattern")
```

--------------------------------

### ggcoverage Example Usage

Source: https://showteeth.github.io/ggcoverage/reference/ggcoverage.html

Example of how to use the ggcoverage function with loaded track data. This demonstrates setting custom colors and adjusting the range position for the plot.

```R
# library(ggcoverage)
# library(utils)
# library(rtracklayer)
# meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
# sample.meta <- utils::read.csv(meta.file)
# track folder
# track.folder <- system.file("extdata", "RNA-seq", package = "ggcoverage")
# load bigwig file
# track.df <- LoadTrackFile(track.folder = track.folder, format = "bw",region = "chr14:21,677,306-21,737,601",
#                           extend = 2000, meta.info = sample.meta)
# gtf.file <- system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
# gtf.gr <- rtracklayer::import.gff(con = gtf.file, format = "gtf")
# ggcoverage(data = track.df, color = "auto", range.position = "out")

```

--------------------------------

### Load ggcoverage and other libraries

Source: https://showteeth.github.io/ggcoverage/articles/ggcoverage.html

Load the ggcoverage package along with other necessary libraries such as rtracklayer, graphics, and ggpattern after installation.

```R
library("rtracklayer")
library("graphics")
library("ggcoverage")
library("ggpattern")
```

--------------------------------

### Full Example: Plotting Coverage with CNV Annotation

Source: https://showteeth.github.io/ggcoverage/reference/geom_cnv.html

Demonstrates how to load CNV and coverage data, then plot them together using ggcoverage, geom_gc, geom_cnv, and geom_ideogram. Requires BSgenome and specific data files.

```R
# library(ggcoverage)
# library(utils)
# library("BSgenome.Hsapiens.UCSC.hg19")
# # prepare files
# cnv.file <- system.file("extdata", "DNA-seq", "SRR054616_copynumber.txt", package = "ggcoverage")
# track.file <- system.file("extdata", "DNA-seq", "SRR054616.bw", package = "ggcoverage")
# # read CNV
# cnv.df = read.table(file = cnv.file, sep = "\t", header = TRUE)
# # load track
# track.df = LoadTrackFile(track.file = track.file, format = "bw")
# track.df$seqnames = paste0("chr", track.df$seqnames)
# # plot
# ggcoverage(data = track.df, color = "grey", region = "chr4:1-160000000",
#            mark.region = NULL, range.position = "out") +
#   geom_gc(bs.fa.seq=BSgenome.Hsapiens.UCSC.hg19) +
#   geom_cnv(cnv.df = cnv.df, bin.col = 3, cn.col = 4) +
#   geom_ideogram(genome = "hg19",plot.space = 0, highlight.centromere = TRUE)

```

--------------------------------

### Basic geom_coverage Plot Example

Source: https://showteeth.github.io/ggcoverage/reference/geom_coverage.html

Demonstrates a basic usage of geom_coverage with loaded track data. Ensure track data is prepared using LoadTrackFile and ggplot2 is loaded.

```r
# library(ggcoverage)
# library(utils)
# library(ggplot2)
# meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
# sample.meta <- utils::read.csv(meta.file)
# 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
# )
# ggplot() +
#   geom_coverage(data = track.df, color = "auto", mark.region = NULL)

```

--------------------------------

### Example: Add links to a coverage plot

Source: https://showteeth.github.io/ggcoverage/reference/geom_link.html

Demonstrates how to use geom_link to add links to a coverage plot. Requires a data frame with genomic region information and a specified link file.

```R
library(ggcoverage)
# create test dataframe (random)
df <- data.frame(
  seqnames = "chr9", start = seq(from = 4000000, to = 5999000, by = 1000),
  end = seq(from = 4001000, to = 6000000, by = 1000), score = sample(1:100, 2000, replace = TRUE),
  Type = "Example", Group = "Example"
)
# create plot
ggcoverage(
  data = df, color = "grey", region = "chr9:4000000-6000000",
  mark.region = NULL, range.position = "out"
) +
  geom_link(link.file = link.file, file.type = "bedpe", show.rect = TRUE)

#> Error in ggcoverage(data = df, color = "grey", region = "chr9:4000000-6000000",     mark.region = NULL, range.position = "out"):
#>  参数没有用(region = "chr9:4000000-6000000")
```

--------------------------------

### Basic Protein Coverage Plot Example

Source: https://showteeth.github.io/ggcoverage/reference/geom_protein.html

Illustrates how to create a basic protein coverage plot using geom_peptide. Requires loading ggplot2 and ggcoverage libraries, specifying file paths for coverage and FASTA, and a unique protein ID.

```r
# library(ggplot2)
# library(ggcoverage)
# coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
# fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
# protein.id = "sp|P02769|ALBU_HUMAN"
# ggplot() +
#     geom_peptide(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
```

--------------------------------

### Prepare Mark Region for Plotting

Source: https://showteeth.github.io/ggcoverage/index.html

Creates a data frame defining a specific region to be marked on the plot. Includes start, end coordinates, and a label for the region.

```R
mark.region=data.frame(start=c(76822533),
                       end=c(76823743),
                       label=c("Promoter"))
# check data
mark.region
```

--------------------------------

### Extracting Plot Data with GetPlotData

Source: https://showteeth.github.io/ggcoverage/reference/GetPlotData.html

Example of how to use GetPlotData to extract data from a ggcoverage plot object. Ensure the plot object is created first.

```R
# cov.plot = ggcoverage(data = track.df, color = "auto", region = "chr18:76822285-76900000",
#                       range.position = "out", mark.region=mark.region, show.mark.label = TRUE)
# plot.data = GetPlotData(plot = cov.plot, layer.num=1)

```

--------------------------------

### Basic usage of geom_transcript with ggcoverage

Source: https://showteeth.github.io/ggcoverage/reference/geom_transcript.html

This example demonstrates how to integrate geom_transcript with a basic ggcoverage plot. It requires loading track data, importing GTF file into a Granges object, and then combining the coverage plot with the transcript annotation.

```R
library(ggcoverage)
library(utils)
library(rtracklayer)
meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
sample.meta <- utils::read.csv(meta.file)
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
)
gtf.file <- system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
gtf.gr <- rtracklayer::import.gff(con = gtf.file, format = "gtf")
basic.coverage <- ggcoverage(data = track.df, color = "auto", range.position = "out")
basic.coverage + geom_transcript(gtf.gr = gtf.gr, label.vjust = 1.5)
```

--------------------------------

### Example Usage of geom_tad2 with ggcoverage

Source: https://showteeth.github.io/ggcoverage/reference/geom_tad2.html

Demonstrates how to use geom_tad2 to overlay a Hi-C contact map onto a genomic coverage plot. Requires loading HiCDataHumanIMR90 and preparing dataframes for both coverage and the contact matrix.

```R
library(ggcoverage)
library(HiCDataHumanIMR90)
data(Dixon2012_IMR90, package = "HiCDataHumanIMR90")
mat <- as.matrix(hic_imr90_40@.Data[[1]]@intdata)

#> 
#> 载入需要的程辑包：HiTC
#> 载入需要的程辑包：IRanges
#> 载入需要的程辑包：BiocGenerics
#> 
#> 
#> 载入程辑包：'BiocGenerics'
#> The following objects are masked from 'package:stats':
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#> 
#>     anyDuplicated, append, as.data.frame, basename, cbind, colnames,
#>     dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
#>     grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
#>     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
#>     rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
#>     union, unique, unsplit, which.max, which.min
#> 载入需要的程辑包：S4Vectors
#> 载入需要的程辑包：stats4
#> 
#> 
#> 载入程辑包：'S4Vectors'
#> The following object is masked from 'package:base':
#> 
#>     expand.grid
#> 载入需要的程辑包：GenomicRanges
#> 载入需要的程辑包：GenomeInfoDb
granges <- hic_imr90_40@.Data[[1]]@xgi
# prepare coverage dataframe
df <- data.frame(
  seqnames = "chr1", start = seq(from = 50000000, to = 59999000, by = 1000),
  end = seq(from = 50001000, to = 60000000, by = 1000), score = sample(1:100, 10000, replace = TRUE),
  Type = "Example", Group = "Example"
)
# create plot
ggcoverage(
  data = df, color = "grey", region = "chr1:50000000-56000000",
  mark.region = NULL, range.position = "out"
) +
  geom_tad2(matrix = log2(mat + 1), granges = granges, zlim = 5, color.palette = c("blue", "red"))

#> Error in ggcoverage(data = df, color = "grey", region = "chr1:50000000-56000000",     mark.region = NULL, range.position = "out"): 参数没有用(region = "chr1:50000000-56000000")

```

--------------------------------

### Prepare Mark Region Data Frame

Source: https://showteeth.github.io/ggcoverage/articles/ggcoverage.html

Creates a data frame to define specific genomic regions for marking on the coverage plot. Includes start, end, and label for each region.

```R
# create mark region
mark.region=data.frame(start=c(76822533),
                       end=c(76823743),
                       label=c("Promoter"))
# check data
mark.region
```

--------------------------------

### Annotate Protein Coverage with Features

Source: https://showteeth.github.io/ggcoverage/reference/geom_feature.html

Example of using geom_feature to add annotations to a protein coverage plot. Requires pre-generated protein coverage plot and a feature dataframe.

```R
# library(ggcoverage)
# coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
# fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
# protein.id = "sp|P02769|ALBU_BOVIN"
# protein.coverage = ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
# feature.df = data.frame(ProteinID = protein.id, start = c(1, 19, 25), end = c(18, 24, 607),
#                         Type = c("Signal", "Propeptide", "Chain"))
# protein.coverage +
#    geom_feature(feature.df = feature.df, feature.color = c("#4d81be","#173b5e","#6a521d"))
```

--------------------------------

### Filter and Prepare Mark Region Data

Source: https://showteeth.github.io/ggcoverage/articles/CustomizeThePlot.html

Filter the loaded track data to include specific sample types and define a mark region with start, end, and label for annotation.

```R
track.df = track.df %>% dplyr::filter(Type %in% c("MCF7_ER_1", "MCF7_input"))
# create mark region
mark.region=data.frame(start=c(76822533),
                       end=c(76823743),
                       label=c("Promoter"))
# check data
mark.region
```

--------------------------------

### Get Consensus Peaks from ChIP-seq Data

Source: https://showteeth.github.io/ggcoverage/articles/ggcoverage.html

Retrieves consensus peaks from ChIP-seq data using a peak file. This function requires the MSPC package and its installation path to be specified.

```R
# load peak file
peak.file <- system.file("extdata", "ChIP-seq", "consensus.peak", package = "ggcoverage")
# get consensus peak (do nothing when there is only one file)
# notice: this step requires MSPC, specific the installation path with mspc.path
peak.df <- GetConsensusPeak(peak.file = peak.file)
```

--------------------------------

### Prepare sample metadata

Source: https://showteeth.github.io/ggcoverage/articles/TimeAndMemory.html

Prepares a data frame with sample metadata for use with ggcoverage. This is an R command.

```r
# prepare metadata
sample.meta = data.frame(SampleName=c('possorted_genome_bam'),
                         Type = c("possorted_genome_bam"),
                         Group = c("10x"))
sample.meta
```

--------------------------------

### List test data for sequential normalization

Source: https://showteeth.github.io/ggcoverage/articles/TimeAndMemory.html

Lists the files in the test directory for sequential normalization, showing BAM and BAI files. This is a shell command.

```bash
# test the sequential normalization
ls -lh ./test

#> total 2.0G
#> -rw-r--r--. 1 songyabing wanglab 3.9M May  26 16:44 SRR054616_rep3.bam.bai
#> -rw-r--r--. 1 songyabing wanglab 3.9M May  26 16:44 SRR054616_rep2.bam.bai
#> -rw-r--r--. 1 songyabing wanglab 3.9M May  26 16:44 SRR054616_rep1.bam.bai
#> -rw-r--r--. 1 songyabing wanglab 646M May  26 16:44 SRR054616_rep3.bam
#> -rw-r--r--. 1 songyabing wanglab 646M May  26 16:44 SRR054616_rep2.bam
#> -rw-r--r--. 1 songyabing wanglab 646M May  26 16:44 SRR054616_rep1.bam
```

--------------------------------

### Load ggcoverage library

Source: https://showteeth.github.io/ggcoverage/articles/TimeAndMemory.html

Loads the ggcoverage library. This is an R command.

```r
library(ggcoverage)
```

--------------------------------

### List test data for parallel normalization

Source: https://showteeth.github.io/ggcoverage/articles/TimeAndMemory.html

Lists the files in the test2 directory for parallel normalization, showing BAM and BAI files. This is a shell command.

```bash
# test the parallel normalization
ls -lh ./test2

#> total 2.0G
#> -rw-r--r--. 1 songyabing wanglab 3.9M May  26 16:44 SRR054616_rep3.bam.bai
#> -rw-r--r--. 1 songyabing wanglab 3.9M May  26 16:44 SRR054616_rep2.bam.bai
#> -rw-r--r--. 1 songyabing wanglab 3.9M May  26 16:44 SRR054616_rep1.bam.bai
#> -rw-r--r--. 1 songyabing wanglab 646M May  26 16:44 SRR054616_rep3.bam
#> -rw-r--r--. 1 songyabing wanglab 646M May  26 16:44 SRR054616_rep2.bam
#> -rw-r--r--. 1 songyabing wanglab 646M May  26 16:44 SRR054616_rep1.bam
```

--------------------------------

### FormatTrack

Source: https://showteeth.github.io/ggcoverage/reference/index.html

Prepares input data for creating coverage plots.

```APIDOC
## FormatTrack

### Description
Prepare Input for Creating Coverage Plot.

### Function Signature
FormatTrack()
```

--------------------------------

### Prepare Mark Regions for Annotation

Source: https://showteeth.github.io/ggcoverage/index.html

Creates a data frame defining specific genomic regions to be marked or annotated on the coverage plot. Each region has a start, end, and a label.

```R
# 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
```

--------------------------------

### Highlight Position with SNV

Source: https://showteeth.github.io/ggcoverage/index.html

Creates a genomic coverage plot highlighting specific positions with SNVs. Similar setup to star marks but uses 'highlight' mark type.

```R
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, 
            mark.type = "highlight") + 
  geom_ideogram(genome = "hg19",plot.space = 0)
```

--------------------------------

### Add Protein Feature Annotations

Source: https://showteeth.github.io/ggcoverage/index.html

Adds custom feature annotations to a protein coverage plot. Define features in a dataframe with start, end, and type, then use geom_feature to plot them.

```r
# protein feature obtained from UniProt
protein.feature.df = data.frame(ProteinID = "sp|P02769|ALBU_BOVIN", start = c(1, 19, 25), 
                                end = c(18, 24, 607), 
                                Type = c("Signal", "Propeptide", "Chain"))
# add annotation
protein.coverage + 
  geom_feature(feature.df = protein.feature.df, feature.color = c("#4d81be","#173b5e","#6a521d"))
```

--------------------------------

### Define Sample Metadata

Source: https://showteeth.github.io/ggcoverage/articles/CustomizeThePlot.html

Create a data frame for sample metadata, including SampleName, Type, and Group. This is used to organize and label data tracks.

```R
# sample 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
```

--------------------------------

### List BAM file size

Source: https://showteeth.github.io/ggcoverage/articles/TimeAndMemory.html

Displays the size of a large BAM file. This is a shell command.

```bash
ls -lh possorted_genome_bam.bam

#> -rw-r--r--. 1 songyabing wanglab 27G 8月  31 2021 possorted_genome_bam.bam
```

--------------------------------

### ggcoverage

Source: https://showteeth.github.io/ggcoverage/reference/index.html

Creates a coverage plot.

```APIDOC
## ggcoverage

### Description
Create Coverage Plot.

### Function Signature
ggcoverage()
```

--------------------------------

### Add GC Content Annotation to a Coverage Plot

Source: https://showteeth.github.io/ggcoverage/reference/geom_gc.html

Example of adding GC content annotation to an existing coverage plot using geom_gc. Requires BSgenome object for GC calculation.

```R
# library(ggcoverage)
# library(utils)
# library(rtracklayer)
# library("BSgenome.Hsapiens.UCSC.hg19")
# track folder
# track.file <- system.file("extdata", "DNA-seq", "CNV_example.txt", package = "ggcoverage")
# track.df <- utils::read.table(track.file, header = TRUE)
# gtf.file <- system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
# gtf.gr <- rtracklayer::import.gff(con = gtf.file, format = "gtf")
# basic.coverage <- ggcoverage(
#   data = track.df, color = NULL, mark.region = NULL,
#   region = "chr4:61750000-62,700,000", range.position = "out"
# )
# basic.coverage + geom_gc(bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19)
```

--------------------------------

### Load BAM File for Single-Nucleotide Analysis

Source: https://showteeth.github.io/ggcoverage/index.html

Loads a BAM file for single-nucleotide level analysis, specifying a region of interest. Requires sample metadata.

```R
# 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)
```

--------------------------------

### Create Joint View Plot - Every Sample

Source: https://showteeth.github.io/ggcoverage/index.html

Generates a line plot for every sample, faceting and coloring by sample type. Ensure 'track.df', 'mark.region' are defined.

```R
basic.coverage = ggcoverage(data = track.df, color = "auto", 
                            plot.type = "joint", facet.key = "Type", group.key = "Type",
                            mark.region = mark.region, range.position = "out")
basic.coverage
```

--------------------------------

### geom_gc Function

Source: https://showteeth.github.io/ggcoverage/reference/geom_gc.html

The geom_gc function adds GC content annotation to a coverage plot. It allows customization of the genome FASTA file, BSgenome object, chromosome splitting, guide lines, colors, and plot spacing.

```APIDOC
## geom_gc

### Description
Adds GC Content Annotation to Coverage Plot.

### Arguments
- **fa.file** (character) - Genome fasta file. Default: NULL.
- **bs.fa.seq** (BSgenome object) - BSgenome for species. Default: NULL.
- **chr.split** (character) - Split between chromosome name and description in `fa.file`. Default: "[[:space:]]".
- **guide.line** (numeric) - GC content guide line. Default: NULL (use mean GC content).
- **line.color** (character) - GC line color. Default: "black".
- **guide.line.color** (character) - The color of guide line. Default: "red".
- **guide.line.type** (character) - The line type of guide line. Default: "dashed".
- **plot.space** (numeric) - Top and bottom margin. Default: 0.1.
- **plot.height** (numeric) - The relative height of GC content annotation to coverage plot. Default: 0.2.

### Value
Plot.

### Examples
```R
# library(ggcoverage)
# library(utils)
# library(rtracklayer)
# library("BSgenome.Hsapiens.UCSC.hg19")
# track folder
# track.file <- system.file("extdata", "DNA-seq", "CNV_example.txt", package = "ggcoverage")
# track.df <- utils::read.table(track.file, header = TRUE)
# gtf.file <- system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
# gtf.gr <- rtracklayer::import.gff(con = gtf.file, format = "gtf")
# basic.coverage <- ggcoverage(
#   data = track.df, color = NULL, mark.region = NULL,
#   region = "chr4:61750000-62,700,000", range.position = "out"
# )
# basic.coverage + geom_gc(bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19)
```
```

--------------------------------

### Load Track File to Dataframe

Source: https://showteeth.github.io/ggcoverage/reference/LoadTrackFile.html

Demonstrates loading a bigwig file into a dataframe using LoadTrackFile. Requires specifying the track folder, format, region, and metadata.

```R
library(ggcoverage)
library(utils)
meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
sample.meta <- utils::read.csv(meta.file)
# track folder
track.folder <- system.file("extdata", "RNA-seq", package = "ggcoverage")
# load bigwig file
track.df <- LoadTrackFile(
  track.folder = track.folder, format = "bw", region = "chr14:21,677,306-21,737,601",
  extend = 2000, meta.info = sample.meta
)


```

--------------------------------

### Load and Inspect Coverage Data

Source: https://showteeth.github.io/ggcoverage/index.html

Loads and displays the first few rows of a protein coverage dataframe. Ensure the 'coverage.df' object is available in your environment.

```r
head(coverage.df)
```

--------------------------------

### LoadTrackFile

Source: https://showteeth.github.io/ggcoverage/reference/index.html

Loads track file data into a dataframe.

```APIDOC
## LoadTrackFile

### Description
Load Track File to Dataframe.

### Function Signature
LoadTrackFile()
```

--------------------------------

### Add peak annotation to a coverage plot

Source: https://showteeth.github.io/ggcoverage/reference/geom_peak.html

This example demonstrates how to add peak annotations to an existing coverage plot using geom_peak. It requires loading track files, optionally importing GTF data for gene annotations, and defining a coverage plot before adding the peak layer.

```R
# library(ggcoverage)
# library(rtracklayer)
# 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")
# )
# 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
# )
# gtf.file <- system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
# gtf.gr <- rtracklayer::import.gff(con = gtf.file, format = "gtf")
# create mark region
# mark.region <- data.frame(start = c(76822533), end = c(76823743), label = c("Promoter"))
# basic.coverage <- ggcoverage(
#   data = track.df, color = "auto", region = "chr18:76822285-76900000",
#   mark.region = mark.region, show.mark.label = FALSE
# )
# 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)
```

--------------------------------

### Load Genomic Tracks in Parallel

Source: https://showteeth.github.io/ggcoverage/articles/TimeAndMemory.html

Loads genomic tracks from BAM files using parallel processing with multiple cores. This demonstrates performance gains over sequential loading.

```R
sample.meta = data.frame(SampleName=c('SRR054616_rep1','SRR054616_rep2','SRR054616_rep3'),
                         Type = c("SRR054616_rep1","SRR054616_rep2","SRR054616_rep3"),
                         Group = c("rep1", "rep2", "rep3"))
sample.meta

track.folder = "./test2"

# run with three cores
system.time(track.df <- LoadTrackFile(
  track.folder = track.folder, format = "bam", norm.method = "RPKM",
  region = "14:21,677,306-21,737,601", bamcoverage.path = "~/anaconda3/bin/bamCoverage",
  extend = 2000, meta.info = sample.meta, n.cores = 3
))
```

--------------------------------

### Load Mass Spectrometry Coverage Data

Source: https://showteeth.github.io/ggcoverage/index.html

Loads protein coverage data exported from Proteome Discoverer using the openxlsx package. This prepares the data for quality assessment and analysis of protein coverage in mass spectrometry experiments.

```R
library(openxlsx)
# prepare coverage dataframe
coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
coverage.df <- openxlsx::read.xlsx(coverage.file)
```

--------------------------------

### Basic Coverage Plot for DNA-seq Data

Source: https://showteeth.github.io/ggcoverage/index.html

Creates a basic grey coverage plot for DNA-seq data. Ensure 'track.df' is loaded.

```R
basic.coverage = ggcoverage(data = track.df,color = "grey", mark.region = NULL,
                            range.position = "out")
basic.coverage
```

--------------------------------

### Display head of track data frame

Source: https://showteeth.github.io/ggcoverage/articles/TimeAndMemory.html

Displays the first few rows of the loaded track data frame. This is an R command.

```r
head(track.df)
```

--------------------------------

### Load Mass Spectrometry Protein Coverage Data

Source: https://showteeth.github.io/ggcoverage/articles/ggcoverage.html

Load protein coverage data from an Excel file using the openxlsx package. The head() function is used to display the first few rows of the loaded data.

```r
library(openxlsx)
# prepare coverage dataframe
coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
coverage.df <- openxlsx::read.xlsx(coverage.file)
# check the data
head(coverage.df)
```

--------------------------------

### ggprotein

Source: https://showteeth.github.io/ggcoverage/reference/index.html

Creates a mass spectrometry protein coverage plot.

```APIDOC
## ggprotein

### Description
Create Mass Spectrometry Protein Coverage Plot.

### Function Signature
ggprotein()
```

--------------------------------

### Load BAM track file for a specific region

Source: https://showteeth.github.io/ggcoverage/articles/TimeAndMemory.html

Loads a BAM track file for a specified region, optimizing for memory and time by not loading the entire file. The 'norm.method = "None"' indicates no normalization is applied. This is an R command.

```r
# prepare track folder
track.folder = '~/projects/ggcoverage'
# load the track
# region length: 3631
system.time(track.df <- LoadTrackFile(
  track.folder = track.folder, format = "bam", norm.method = "None",
  region = "chr11:118339075-118342705",
  extend = 0, meta.info = sample.meta
))
```

--------------------------------

### Add Base Annotation with Highlight

Source: https://showteeth.github.io/ggcoverage/articles/ggcoverage.html

Creates a ggcoverage plot with single nucleotide resolution, adding base annotations with a 'highlight' style. Requires BAM file and genome data.

```R
# highlight
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,
            mark.type = "highlight") +
  geom_ideogram(genome = "hg19",plot.space = 0)
```

--------------------------------

### Create basic coverage plot

Source: https://showteeth.github.io/ggcoverage/articles/TimeAndMemory.html

Generates a basic coverage plot from a track data frame. The running time is noted as very small. This is an R command.

```r
# create basic coverage plot
# the running time is very small
system.time(basic.coverage <- ggcoverage(data = track.df, color = "red", 
                                        range.position = "out", show.mark.label = FALSE))
```

--------------------------------

### Load DNA-seq Data from BigWig File

Source: https://showteeth.github.io/ggcoverage/index.html

Loads DNA-seq coverage data from a BigWig file using 'LoadTrackFile'. Handles potential missing metadata and adds 'chr' prefix to sequence names.

```R
# track file
track.file <- system.file("extdata", "DNA-seq", "SRR054616.bw", package = "ggcoverage")
# load track
track.df = LoadTrackFile(track.file = track.file, format = "bw", region = "4:1-160000000")
# add chr prefix
track.df$seqnames = paste0("chr", track.df$seqnames)
```

--------------------------------

### Load Libraries for ggcoverage

Source: https://showteeth.github.io/ggcoverage/articles/CustomizeThePlot.html

Load necessary libraries including ggplot2, patchwork, tidyverse, ggcoverage, and rtracklayer for data manipulation and plotting.

```R
library(ggplot2)
library(patchwork)
library(tidyverse)
library(ggcoverage)
library(rtracklayer)
```

--------------------------------

### Load BAM File for Single Nucleotide Analysis

Source: https://showteeth.github.io/ggcoverage/articles/ggcoverage.html

Loads a BAM file for single nucleotide analysis, specifying a region and metadata. Displays the head of the loaded track data.

```R
# 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
```

--------------------------------

### Load BAM track file for a larger region

Source: https://showteeth.github.io/ggcoverage/articles/TimeAndMemory.html

Loads a BAM track file for a larger specified region, demonstrating the time taken. The 'norm.method = "None"' indicates no normalization is applied. This is an R command.

```r
# region length: 203631
system.time(LoadTrackFile(
  track.folder = track.folder, format = "bam", norm.method = "None",
  region = "chr11:118339075-118542705",
  extend = 0, meta.info = sample.meta
))
```

--------------------------------

### Generate Protein Coverage Plot

Source: https://showteeth.github.io/ggcoverage/index.html

Creates a basic protein coverage plot using ggprotein. Requires specifying the coverage file, FASTA file, protein ID, and range position setting.

```r
protein.coverage = ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, 
                             protein.id = "sp|P02769|ALBU_BOVIN", range.position = "out")
protein.coverage
```

--------------------------------

### theme_protein2()

Source: https://showteeth.github.io/ggcoverage/reference/theme_protein2.html

Applies a predefined theme suitable for protein visualizations using geom_protein.

```APIDOC
## theme_protein2()

### Description
Applies a theme to geom_protein plots.

### Usage
```R
theme_protein2()
```

### Value
Returns a list of layers that constitute the theme.
```

--------------------------------

### Load BigWig Tracks with Metadata

Source: https://showteeth.github.io/ggcoverage/articles/CustomizeThePlot.html

Load BigWig files from a specified folder, filtering by region and applying sample metadata. This function prepares coverage data for plotting.

```R
# track folder
track.folder = system.file("extdata", "ChIP-seq", package = "ggcoverage")
# load bigwig file
track.df = LoadTrackFile(track.folder = track.folder, format = "bw", region = "chr18:76822285-76900000",
                         meta.info = sample.meta)
# check data
head(track.df)
```

--------------------------------

### FormatTrack Function

Source: https://showteeth.github.io/ggcoverage/reference/FormatTrack.html

Prepares input data for creating coverage plots by specifying the region, gene information, and extension length.

```APIDOC
## FormatTrack

### Description
Prepares input for creating coverage plot.

### Arguments
- **data** (dataframe) - Track dataframe loaded by `LoadTrackFile`.
- **region** (string) - Region used to create coverage plot, eg: chr14:21,677,306-21,737,601 or chr14:21,677,306. Default: "chr14:21,677,306-21,737,601".
- **gtf.gr** (Granges object) - Granges object of GTF, created with `import.gff`. Default: NULL.
- **gene.name** (string) - The name of gene. Default: "HNRNPC".
- **gene.name.type** (string) - Gene name type (filed of `gtf.gr`), chosen from gene_name and gene_id. Default: "gene_name".
- **extend** (integer) - Extend length of `region`. Default: 2000.

### Value
A dataframe.
```

--------------------------------

### theme_coverage2

Source: https://showteeth.github.io/ggcoverage/reference/index.html

Applies an alternative theme for geom_coverage.

```APIDOC
## theme_coverage2

### Description
Theme for geom_coverage.

### Function Signature
theme_coverage2()
```

--------------------------------

### Create Basic Coverage Plot

Source: https://showteeth.github.io/ggcoverage/index.html

Generates a basic coverage plot from a data frame. Use this for a quick overview of genomic coverage.

```R
basic.coverage = ggcoverage(data = track.df, color = "grey",
                            mark.region = NULL, range.position = "out")
basic.coverage
```

--------------------------------

### theme_coverage

Source: https://showteeth.github.io/ggcoverage/reference/index.html

Applies a theme for geom_coverage.

```APIDOC
## theme_coverage

### Description
Theme for geom_coverage.

### Function Signature
theme_coverage()
```

--------------------------------

### theme_tad2

Source: https://showteeth.github.io/ggcoverage/reference/index.html

Applies a theme for geom_tad2.

```APIDOC
## theme_tad2

### Description
Theme for geom_tad2.

### Function Signature
theme_tad2()
```

--------------------------------

### theme_aa

Source: https://showteeth.github.io/ggcoverage/reference/index.html

Applies a theme for geom_base with amino acid information.

```APIDOC
## theme_aa

### Description
Theme for geom_base with Amino Acid.

### Function Signature
theme_aa()
```

--------------------------------

### Load RNA-seq Metadata

Source: https://showteeth.github.io/ggcoverage/index.html

Loads the metadata file for RNA-seq samples. This file typically contains sample names, types, and groups.

```R
# load metadata
meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
sample.meta = read.csv(meta.file)
sample.meta
```

--------------------------------

### Prepare Protein Set from FASTA

Source: https://showteeth.github.io/ggcoverage/index.html

Reads protein sequences from a FASTA file into an AAStringSet object using the Biostrings package. This is a prerequisite for generating coverage plots.

```r
library(Biostrings)
fasta.file <- system.file("extdata", "Proteins", "MS_BSA_coverage.fasta", package = "ggcoverage")
protein.set <- Biostrings::readAAStringSet(fasta.file)
protein.set
```

--------------------------------

### Load ChIP-seq Track Files

Source: https://showteeth.github.io/ggcoverage/index.html

Loads bigwig files for ChIP-seq analysis into a data frame. Specifies the track folder, format, and genomic region. Requires sample metadata.

```R
track.folder = system.file("extdata", "ChIP-seq", package = "ggcoverage")
# load bigwig file
track.df = LoadTrackFile(track.folder = track.folder, format = "bw", region = "chr18:76822285-76900000",
                         meta.info = sample.meta)
# check data
head(track.df)
```

--------------------------------

### Load Protein FASTA Sequence

Source: https://showteeth.github.io/ggcoverage/articles/ggcoverage.html

Load protein sequences from a FASTA file using the Biostrings package. This prepares the protein set for further analysis.

```r
library(Biostrings)
# prepare track dataframe
protein.set <- Biostrings::readAAStringSet(fasta.file)
```

--------------------------------

### Create Metadata Data Frame

Source: https://showteeth.github.io/ggcoverage/articles/ggcoverage.html

Creates a data frame to store metadata for ChIP-seq samples, including sample names, types, and groups. This is a prerequisite for loading track files.

```R
# 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
```

--------------------------------

### Display Amino Acid Annotation Color Scheme

Source: https://showteeth.github.io/ggcoverage/articles/ggcoverage.html

Visualizes a default color scheme for amino acid annotations based on Residual colours. Uses base R graphics for image display.

```R
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
)
```

```R
# reset par default
graphics::par(opar)
```