### Install ggcoverage from GitHub

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/index.html

Install the ggcoverage package directly from its GitHub repository for the latest updates. Ensure 'remotes' is installed first if needed.

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

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

### Install ggcoverage Package

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Install the ggcoverage package from CRAN or GitHub. GitHub installation is recommended for the latest updates.

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

```r
install.package("remotes")
remotes::install_github("showteeth/ggcoverage")
```

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

### Create Coverage Plot Example

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/ggcoverage.html

Example demonstrating how to load track data and create a coverage plot using ggcoverage. Ensure necessary libraries are loaded and data is prepared with LoadTrackFile.

```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")
```

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

### Example Usage of ggprotein

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/ggprotein.html

Demonstrates how to use the ggprotein function with example file paths and a protein ID. Ensure the necessary library is loaded and the file paths are correctly set.

```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"
# ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
```

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

### Install ggcoverage from GitHub

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/ggcoverage.html

Install the ggcoverage package from its GitHub repository for the latest updates. It's recommended over CRAN for more timely updates. Ensure GenomeMatrix is installed if dependency errors occur.

```R
# 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")
```

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

### Get Consensus Peak from Replicates

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/GetConsensusPeak.html

This example demonstrates how to use the GetConsensusPeak function to obtain consensus peaks from a specified peak file. Ensure the ggcoverage library is loaded.

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

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

### Load and Prepare FASTA Data

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/ggcoverage.html

Loads protein sequences from a FASTA file using Biostrings and prepares them for coverage analysis. Ensure Biostrings is installed.

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

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

### Example Usage of geom_cnv with ggcoverage

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/geom_cnv.html

Demonstrates how to prepare CNV and track data, then plot it using ggcoverage with geom_cnv, geom_gc, and geom_ideogram for comprehensive visualization.

```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)
```

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

### Install ggcoverage from CRAN

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/index.html

Install the ggcoverage package from the Comprehensive R Archive Network (CRAN).

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

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

### Load ggcoverage and dependencies

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/index.html

Load the ggcoverage package along with necessary libraries like rtracklayer and ggpattern after installation.

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

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

### ggcoverage with geom_base Example

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/geom_base.html

Demonstrates how to use geom_base to add base and amino acid annotations to a ggcoverage plot. This example loads track data, creates a base ggcoverage plot, and then overlays the base annotations using geom_base with a specified BAM file and BSgenome.

```R
# library(ggcoverage)
# library("BSgenome.Hsapiens.UCSC.hg19")
# get sample metadata
# sample.meta <- data.frame(
#   SampleName = c("tumorA.chr4.selected"),
#   Type = c("tumorA"), Group = c("tumorA")
# )
# get bam file
# bam.file <- system.file("extdata", "DNA-seq", "tumorA.chr4.selected.bam", package = "ggcoverage")
# load bam file
# track.df <- LoadTrackFile(
#   track.file = bam.file,
#   meta.info = sample.meta, single.nuc = TRUE,
#   single.nuc.region = "chr4:62474235-62474295"
# )
# ggcoverage(
#   data = track.df, color = "grey", range.position = "out",
#   single.nuc = TRUE, rect.color = "white"
# ) +
#   geom_base(
#     bam.file = bam.file,
#     bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19
#   )
```

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

### Load Libraries for ggcoverage

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/CustomizeThePlot.html

Load necessary libraries for plotting and data manipulation with ggcoverage. Ensure these are installed before running.

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

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

### Basic Protein Coverage Plot

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/geom_protein.html

This example demonstrates how to create a basic protein coverage plot using geom_protein. Ensure the necessary libraries are loaded and file paths are correctly specified.

```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|
# ggplot() +
#     geom_peptide(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
```

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

### Load ggcoverage Package

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Load the ggcoverage package into your R session after installation.

```r
library("ggcoverage")
```

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

### Basic Coverage Plot with geom_coverage

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/geom_coverage.html

This example demonstrates how to create a basic coverage plot using geom_coverage. It requires loading track data and then using ggplot to add the coverage layer. Ensure the necessary libraries are 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)
```

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

### Prepare Mark Regions for Plotting

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/index.html

Creates a data frame defining specific regions with start, end, and label for marking on coverage plots.

```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
```

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

### Get consensus peaks from replicates

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/ggcoverage.html

Load peak files and use `GetConsensusPeak` to obtain consensus peaks from ChIP-seq replicates. This step requires the MSPC package.

```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 Mark Region Data Frame

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Creates a data frame to define specific genomic regions for marking on the plot. Ensure the 'start', 'end', and 'label' columns are correctly populated.

```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
```

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

### FormatTrack()

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/index.html

Prepares input data for creating coverage plots.

```APIDOC
## FormatTrack()

### Description
Prepare Input for Creating Coverage Plot.

### Function Signature
FormatTrack()
```

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

### R Session Information

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/ggcoverage.html

Displays the R version, platform, locale, attached base packages, and other attached packages, including their versions.

```R
sessionInfo()
```

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

### List files in test directory

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/TimeAndMemory.html

Lists files in the 'test' directory, typically used to inspect data for sequential normalization.

```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
```

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

### Basic Coverage Plot with GC Annotation

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/geom_gc.html

Demonstrates how to create a basic coverage plot and add GC content annotation using geom_gc. Requires loading necessary libraries and preparing track data.

```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 Genome Data for GC Annotations

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Loads the BSgenome.Hsapiens.UCSC.hg19 package, which is required for plotting GC content annotations. This package needs to be installed separately.

```r
# load genome data
library("BSgenome.Hsapiens.UCSC.hg19")
```

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

### List files in test2 directory

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/TimeAndMemory.html

Lists files in the 'test2' directory, typically used to inspect data for parallel normalization.

```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
```

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

### Prepare mark region for ChIP-seq data

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/ggcoverage.html

Create a data frame defining a region to be marked on the coverage plot, including start, end, and a label.

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

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

### Load and Prepare Hi-C Bin Table

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Reads Hi-C bin data from a tab-separated file, assigns column names, and converts it into a GRanges object.

```r
## bin
hic_bin_file <-
  system.file("extdata", "HiC", "HiC_bin.txt", package = "ggcoverage")
hic_bin <- read.table(file = hic_bin_file, sep = "\t")
colnames(hic_bin) <- c("chr", "start", "end")
hic_bin_gr <- GenomicRanges::makeGRangesFromDataFrame(df = hic_bin)
```

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

### Define and Prepare Mark Region

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/CustomizeThePlot.html

Define a specific genomic region for marking on the plot, including start and end coordinates and a label. This region can be highlighted in the coverage plot.

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

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

### Parallel normalization using LoadTrackFile

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/TimeAndMemory.html

Performs parallel normalization of BAM files using LoadTrackFile with multiple cores. This method is significantly faster than sequential normalization. Requires sample metadata and specifies the track folder, format, normalization method, region, bamCoverage path, extension, and the number of cores.

```r
# prepare sample metadata
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
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
))

#> user       system  elapsed
#> 1191.628   36.818  424.143
```

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

### Create and Customize Patchwork Object

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/CustomizeThePlot.html

Combine multiple ggcoverage plots into a single graphic using the patchwork package. This example adds gene and ideogram tracks to a base coverage plot.

```R
# get consensus peak file
peak.file = system.file("extdata", "ChIP-seq", "consensus.peak", package = "ggcoverage")
# example plot
chip.coverage = basic.coverage + labs(title = "chr18:76,822,285-76,900,000") +
  theme(plot.title=element_text(hjust=0.5)) +
  geom_gene(gtf.gr=gtf.gr, arrow.length = 0.04,arrow.size=0.25,
            gene.size = 0.75,
            utr.size = 1.5,
            exon.size = 2.5,label.size = 2.5, plot.height = 0.3) +
  geom_peak(bed.file = peak.file, plot.height = 0.1) +
  geom_ideogram(genome = "hg19",plot.space = 0, plot.height = 0.15)
```

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

### geom_gc Function Definition

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/geom_gc.html

Defines the geom_gc function for adding GC content annotation. It accepts parameters for FASTA files, BSgenome objects, splitting characters, guide lines, colors, and plot dimensions.

```R
geom_gc(
  fa.file = NULL,
  bs.fa.seq = NULL,
  chr.split = "\[\[:space:\]\]",
  guide.line = NULL,
  line.color = "black",
  guide.line.color = "red",
  guide.line.type = "dashed",
  plot.space = 0.1,
  plot.height = 0.2
)
```

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

### Add GC content annotation with geom_gc

Source: https://context7.com/showteeth/ggcoverage/llms.txt

Appends a GC content line plot. Can use BSgenome objects or local FASTA files. A guide line can be explicitly set or defaults to the mean GC content.

```r
if (requireNamespace("BSgenome.Hsapiens.UCSC.hg19", quietly = TRUE)) {
  library("BSgenome.Hsapiens.UCSC.hg19")

  bw_file  <- system.file("extdata", "DNA-seq", "SRR054616.bw", package = "ggcoverage")
  dna_df   <- LoadTrackFile(track.file = bw_file, format = "bw", region = "4:1-160000000")
  dna_df$seqnames <- paste0("chr", dna_df$seqnames)

  dna_coverage <- ggcoverage(
    data = dna_df, color = "grey", mark.region = NULL, range.position = "out"
  )

  # GC annotation using BSgenome
  dna_coverage +
    geom_gc(
      bs.fa.seq        = BSgenome.Hsapiens.UCSC.hg19,
      guide.line       = 0.5,           # explicit guide line at 50% GC
      line.color       = "black",
      guide.line.color = "red",
      plot.height      = 0.2
    )

  # GC annotation using a local FASTA file
  dna_coverage +
    geom_gc(fa.file = "/path/to/hg19.fa", guide.line = NULL)  # NULL = use mean GC
}
```

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

### Sequential normalization using LoadTrackFile

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/TimeAndMemory.html

Performs sequential normalization of BAM files using the LoadTrackFile function. This method is typically slower than parallel normalization. Requires sample metadata and specifies the track folder, format, normalization method, region, bamCoverage path, and extension.

```r
# prepare sample metadata
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
track.folder = "./test"

# run
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
  ))

#> user       system elapsed
#> 1169.767   35.005 1208.704
```

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

### Add Contact Map to Coverage Plot with geom_tad2

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/geom_tad2.html

This example demonstrates how to add a contact map to a ggcoverage plot. It requires loading the ggcoverage and HiCDataHumanIMR90 libraries, preparing a matrix and granges object, and then combining ggcoverage with geom_tad2.

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

# 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"))
```

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

### ggcoverage()

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/index.html

Creates a coverage plot.

```APIDOC
## ggcoverage()

### Description
Create Coverage Plot.

### Function Signature
ggcoverage()
```

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

### Add Peak Annotation to Coverage Plot

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/geom_peak.html

This example demonstrates how to add peak annotations to a coverage plot. It requires loading track files and optionally GTF files for gene annotations. Ensure the necessary libraries are loaded.

```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)
```

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

### Define Sample Metadata

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/CustomizeThePlot.html

Create a data frame containing metadata for sequencing samples, including sample names, types, and experimental groups. This is used to organize and label 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
```

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

### theme_protein()

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/theme_protein.html

Applies a theme suitable for protein visualizations.

```APIDOC
## theme_protein()

### Description
Applies a theme suitable for protein visualizations.

### Value
List of layers.
```

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

### Add Protein Feature Annotations

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/ggcoverage.html

Adds custom annotations to a protein coverage plot based on protein features obtained from sources like UniProt. The features are defined in a data frame with ProteinID, start, end, and Type.

```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_protein_feature(data = protein.feature.df)
```

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

### theme_base()

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/index.html

Applies a base theme for geom_base.

```APIDOC
## theme_base()

### Description
Theme for geom_base.

### Function Signature
theme_base()
```

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

### Add Protein Feature Annotations

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Adds custom annotations to the protein coverage plot. Features are defined in a dataframe with ProteinID, start, end, and Type. This is useful for highlighting specific regions like signal peptides or propeptides.

```r
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")
)
```

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

### Create Mass Spectrometry Protein Coverage Plot with ggprotein

Source: https://context7.com/showteeth/ggcoverage/llms.txt

Builds a protein peptide coverage plot from Proteome Discoverer output and a reference FASTA file. Displays covered and uncovered regions with an optional summary table. Requires the openxlsx package.

```r
if (requireNamespace("openxlsx", quietly = TRUE)) {
  library(openxlsx)

  coverage_file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx",
                                package = "ggcoverage")
  coverage_df   <- read.xlsx(coverage_file, sheet = "Sheet1")

  fasta_file  <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta",
                              package = "ggcoverage")
  protein_id  <- "sp|P02769|ALBU_BOVIN"

  protein_coverage <- ggprotein(
    coverage.df     = coverage_df,
    fasta.file      = fasta_file,
    protein.id      = protein_id,
    XCorr.threshold = 2,           # minimum XCorr to keep a peptide
    confidence      = "High",
    contaminant     = NULL,        # set TRUE to remove contaminant peptides
    color           = "grey",
    mark.bare       = TRUE,        # highlight uncovered regions in red
    mark.color      = "red",
    show.table      = TRUE,        # show coverage summary table
    table.position  = "top_right",
    range.position  = "out"
  )
  protein_coverage
}
```

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

### theme_tad()

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/index.html

Applies a theme for geom_tad.

```APIDOC
## theme_tad()

### Description
Theme for geom_tad.

### Function Signature
theme_tad()
```

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

### Load Proteome Discoverer Coverage Data

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/index.html

Reads coverage data from an Excel file exported from Proteome Discoverer. Ensure the 'openxlsx' package is installed. The `head()` function is used to display the first few rows of the loaded data frame.

```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)
```

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

### Load Bigwig Track Files

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/index.html

Loads bigwig files for specified regions, extending the region by a given amount. Requires metadata information for sample identification.

```R
# 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)
```

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

### Add a Custom Peak Layer to a Plot

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/CustomizeThePlot.html

Create a new peak layer using pseudo-peak data. When using your own data, ensure it's used as a region constraint. This example demonstrates adding a red segment representing a peak.

```r
# create pseudo-peak, you can load your peak file instead (be aware of 0-based/1-based)
pseudo_peak = data.frame(chr="chr18", start = 76840533, end = 76842533)
# get region constraint
plot.region.start <- coverage.data[1, "start"]
plot.region.end <- coverage.data[nrow(coverage.data), "end"]

# create plot
peak.plot = ggplot() + 
  geom_segment(
      data = pseudo_peak,
      aes_string(x = "start", xend = "end", y = "1", yend = "1"),
      color = "red", size = 5
    ) + 
  labs(y="Peak") + 
  theme_peak(margin.len = 0.1, x.range = c(plot.region.start, plot.region.end))
peak.plot
```

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

### Highlight Base Position with SNV

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Creates a ggcoverage plot with base annotation, highlighting SNV positions using the 'highlight' type. Requires ggcoverage, BAM file, and BSgenome.

```r
# highlight one base
ggcoverage(
  data = track_df,
  color = "grey",
  range.position = "out",
  single.nuc = TRUE,
  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 Features with geom_feature

Source: https://context7.com/showteeth/ggcoverage/llms.txt

Appends protein feature annotations (e.g., domains, signal peptides) to a protein coverage plot. Requires a dataframe with ProteinID, start, end, and Type columns. Features are colored distinctly based on type.

```r
if (requireNamespace("openxlsx", quietly = TRUE)) {
  library(openxlsx)

  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.df = read.xlsx(coverage_file),
    fasta.file  = fasta_file,
    protein.id  = protein_id,
    range.position = "out"
  )

  # Features obtained from UniProt annotation
  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"),
      feature.size  = 5,
      plot.height   = 0.1
    )
}
```

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

### LoadTrackFile()

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/index.html

Loads track data from a file into a dataframe.

```APIDOC
## LoadTrackFile()

### Description
Load Track File to Dataframe.

### Function Signature
LoadTrackFile()
```

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

### Add Protein Feature Annotation

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/index.html

Adds feature annotations to an existing protein coverage plot. The feature data frame should include protein ID, start and end positions, and feature type. Custom colors can be applied to different feature types.

```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"))
```

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

### Load Hi-C Link File

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Specifies the file path for Hi-C link data in BEDPE format.

```r
# prepare arcs
link_file <-
  system.file("extdata", "HiC", "HiC_link.bedpe", package = "ggcoverage")
```

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

### Add Feature Annotation to Coverage Plot

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/geom_feature.html

Use geom_feature to add custom feature annotations to a protein coverage plot. Specify features using a data frame with 'ProteinID', 'start', 'end', and 'Type' columns. Customize feature colors using a vector.

```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"))
```

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

### Create Basic Coverage Plot

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Generates a basic coverage plot with GC and ideogram annotations. Requires BSgenome and GTF data.

```r
basic_coverage +
  geom_gc(bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19) +
  geom_gene(gtf.gr = gtf_gr) +
  geom_ideogram(genome = "hg19")
```

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

### Add Gene Annotation to Coverage Plot using geom_gene

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/geom_gene.html

This example demonstrates how to add gene annotations to a basic coverage plot. It requires loading track data, importing a GTF file into a Granges object, creating a basic coverage plot, and then adding the geom_gene layer with the GTF data.

```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)
```

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

### theme_aa()

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/index.html

Applies a theme for geom_base with amino acid display.

```APIDOC
## theme_aa()

### Description
Theme for geom_base with Amino Acid.

### Function Signature
theme_aa()
```

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

### ggprotein()

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/index.html

Creates a mass spectrometry protein coverage plot.

```APIDOC
## ggprotein()

### Description
Create Mass Spectrometry Protein Coverage Plot.

### Function Signature
ggprotein()
```

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

### Create Facet View Coverage Plot

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/ggcoverage.html

Generates a basic facet view coverage plot. Requires 'track.df' data, 'mark.region', and 'range.position' to be set.

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

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

### theme_coverage2()

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/index.html

Applies a theme for geom_coverage.

```APIDOC
## theme_coverage2()

### Description
Theme for geom_coverage.

### Function Signature
theme_coverage2()
```

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

### Load Mass Spectrometry Protein Coverage Data

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Load protein coverage data from an Excel file using the openxlsx package. This snippet prepares a dataframe for mass spectrometry analysis.

```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, sheet = "Sheet1")
```

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

### Load and Inspect Coverage Data

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Loads and displays the first few rows of the coverage_df dataframe to inspect its structure and content.

```r
head(coverage_df)
```

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

### theme_coverage()

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/index.html

Applies a theme for geom_coverage.

```APIDOC
## theme_coverage()

### Description
Theme for geom_coverage.

### Function Signature
theme_coverage()
```

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

### Prepare Protein Sequence Data

Source: https://github.com/showteeth/ggcoverage/blob/main/README.md

Reads a protein FASTA file into an AAStringSet object for use with ggcoverage. Ensure the FASTA file path is correct.

```r
fasta_file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
protein_set <- Biostrings::readAAStringSet(fasta_file)
protein_set
```

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

### Load Hi-C Bin Data

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/index.html

Loads Hi-C bin data from a tab-separated file and converts it into a GenomicRanges object. Column names are set for clarity.

```R
## bin
hic.bin.file = system.file("extdata", "HiC", "HiC_bin.txt", package = "ggcoverage")
hic.bin = read.table(file = hic.bin.file, sep = "\t")
colnames(hic.bin) = c("chr", "start", "end")
hic.bin.gr = GenomicRanges::makeGRangesFromDataFrame(df = hic.bin)
```

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

### Generate Basic Protein Coverage Plot

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/ggcoverage.html

Creates a basic protein coverage plot using ggprotein. Requires coverage and FASTA files, and a specific protein ID. The range.position argument controls how ranges are displayed.

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

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

### theme_tad2()

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/reference/index.html

Applies a theme for geom_tad2.

```APIDOC
## theme_tad2()

### Description
Theme for geom_tad2.

### Function Signature
theme_tad2()
```

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

### Prepare Mark Regions

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/ggcoverage.html

Create a data frame defining specific genomic regions to be marked or highlighted in subsequent visualizations. This is useful for annotating specific areas of interest.

```R
# create mark region
mark.region=data.frame(start=c(21678900,21732001,21737590),
```

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

### Create Basic Coverage Plot

Source: https://github.com/showteeth/ggcoverage/blob/main/docs/articles/TimeAndMemory.html

Generates a basic coverage plot from a pre-loaded track data frame. This operation is very fast. The resulting plot can be viewed more clearly when saved as a PDF.

```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))

##  用户  系统  流逝 
## 0.048 0.001 0.048
```