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support proteomics
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‎DESCRIPTION

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Package: ggcoverage
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Type: Package
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Title: Visualize Genome Coverage with Various Annotations
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Version: 1.1.0
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Title: Visualize Genome/Protein Coverage with Various Annotations
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Version: 1.2.0
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Authors@R:
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person(given = "Yabing",
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family = "Song",
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role = c("aut", "cre"),
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email = "[email protected]")
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Maintainer: Yabing Song <[email protected]>
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Description: The goal of 'ggcoverage' is to simplify the process of visualizing genome coverage. It contains functions to
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load data from BAM, BigWig or BedGraph files, create genome coverage plot, add various annotations to
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the coverage plot, including base and amino acid annotation, GC annotation, gene annotation, transcript annotation, ideogram annotation and peak annotation.
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Description: The goal of 'ggcoverage' is to simplify the process of visualizing genome/protein coverage. It contains functions to
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load data from BAM, BigWig, BedGraph or txt/xlsx files, create genome/protein coverage plot, add various annotations to
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the coverage plot, including base and amino acid annotation, GC annotation, gene annotation, transcript annotation, ideogram annotation,
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peak annotation, contact map annotation, link annotation and peotein feature annotation.
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License: MIT + file LICENSE
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Encoding: UTF-8
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RoxygenNote: 7.1.1
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ggforce,
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HiCBricks,
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ggpattern,
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BiocParallel
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BiocParallel,
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openxlsx,
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stringr,
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ggpp
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Suggests:
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rmarkdown,
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knitr,

‎NEWS.md

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# ggcoverage 1.2.0
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## Major changes
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* Supporting protein coverage and annotation plot (`ggprotein`, `geom_protein`).
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-------------
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# ggcoverage 1.1.0
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## Major changes
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* Mark SNV with twill (add twill to position with SNV), star (add star mark to position with SNV), and highlight (position without SNV is grey).

‎R/geom_protein.R

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#' Layer for Protein Coverage Plot.
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#'
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#' @param coverage.file Exported protein coverage file, should be in excel.
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#' @param fasta.file Input reference protein fasta file.
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#' @param protein.id The protein ID of exported coverage file. This should be unique and in \code{fasta.file}.
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#' @param XCorr.threshold The cross-correlation threshold. Default: 2.
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#' @param confidence The confidence level. Default: High.
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#' @param contaminant Whether to remove contaminant peptides. Default: NULL (not remove).
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#' @param remove.na Logical value, whether to remove NA value in Abundance column. Default: TRUE.
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#' @param color The fill color of coverage plot. Default: grey.
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#' @param mark.bare Logical value, whether to mark region where Abundance is zero or NA. Default: TRUE.
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#' @param mark.color The color used for the marked region. Default: red.
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#' @param mark.alpha The transparency used for the marked region. Default: 0.5.
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#' @param show.table Logical value, whether to show coverage summary table. Default: TRUE.
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#' @param table.position The position of the coverage summary table, choose from right_top, left_top, left_bottom, right_bottom.
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#' Default: right_top.
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#' @param table.size The font size of coverage summary table. Default: 4.
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#' @param table.color The font color of coverage summary table. Default: black.
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#' @param range.size The label size of range text, used when \code{range.position} is in. Default: 3.
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#' @param range.position The position of y axis range, chosen from in (move y axis in the plot) and
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#' out (normal y axis). Default: in.
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#'
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#' @return A ggplot2 object.
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#' @importFrom openxlsx read.xlsx
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#' @importFrom magrittr %>%
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#' @importFrom dplyr filter group_by summarise arrange
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#' @importFrom rlang .data
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#' @importFrom Biostrings readAAStringSet
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#' @importFrom stringr str_locate
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#' @importFrom GenomicRanges reduce GRanges setdiff
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#' @importFrom IRanges IRanges
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#' @importFrom ggplot2 ggplot geom_rect geom_text aes aes_string scale_x_continuous
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#' @importFrom ggpp annotate
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#' @importFrom scales scientific
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#' @export
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#'
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#' @examples
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#' # library(ggplot2)
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#' # library(ggcoverage)
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#' # coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
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#' # fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
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#' # protein.id = "sp|
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#' # ggplot() +
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#' # geom_peptide(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
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geom_protein = function(coverage.file, fasta.file, protein.id, XCorr.threshold = 2,
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confidence = "High", contaminant = NULL, remove.na = TRUE,
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color = "grey", mark.bare = TRUE, mark.color = "red", mark.alpha = 0.5,
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show.table = TRUE, table.position = c("right_top", "left_top", "left_bottom", "right_bottom"),
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table.size = 4, table.color = "black", range.size = 3, range.position = c("in", "out")){
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# check parameters
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table.position <- match.arg(arg = table.position)
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range.position <- match.arg(arg = range.position)
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# load coverage dataframe
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coverage.df = openxlsx::read.xlsx(coverage.file)
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# remove suffix and prefix string
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coverage.df$Annotated.Sequence = gsub(pattern = ".*\\.(.*)\\..*", replacement = "\\1", x = coverage.df$Annotated.Sequence)
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# filter converge according to confidence
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if(!is.null(confidence)){
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coverage.df = coverage.df[coverage.df[, "Confidence"] == confidence, ]
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}
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# filter converge according to contaminant
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if(!is.null(contaminant)){
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coverage.df = coverage.df[coverage.df[, "Contaminant"] == contaminant, ]
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}
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# filter converge according to cross-correlation
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if(!is.null(XCorr.threshold)){
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xcorr.index = grep(pattern = "XCorr", x = colnames(coverage.df))
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coverage.df = coverage.df[coverage.df[, xcorr.index] >= XCorr.threshold, ]
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}
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# get abundance cols
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abundance.col = grep(pattern = "Abundance", x = colnames(coverage.df), value = TRUE)
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# remove na abundance
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if(remove.na){
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coverage.df = coverage.df %>% dplyr::filter(!is.na(.data[[abundance.col]]))
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}
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# sum abundance of duplicated Annotated.Sequence
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coverage.df = coverage.df %>%
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dplyr::group_by(.data[["Annotated.Sequence"]]) %>%
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dplyr::summarise(Abundance = sum(.data[[abundance.col]])) %>%
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as.data.frame()
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colnames(coverage.df) = c("peptide", "abundance")
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# check the coverage dataframe
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if(nrow(coverage.df) == 0){
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stop("There is no valid peptide, please check!")
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}
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# load genome fasta
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aa.set = Biostrings::readAAStringSet(fasta.file)
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protein.index = which(names(aa.set) == protein.id)
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if(length(protein.index) == 1){
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aa.set.used = aa.set[protein.index]
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aa.seq.used = paste(aa.set.used)
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}else if(length(protein.index) > 1){
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stop("Please check the protein.id you provided, there is more than one in provided fasta file!")
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}else{
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stop("Please check the protein.id you provided, it can't be found in provided fasta file!")
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}
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# get the region
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aa.anno.region = sapply(coverage.df$peptide, function(x){
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stringr::str_locate(pattern =x, aa.seq.used)
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}) %>% t() %>% as.data.frame()
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colnames(aa.anno.region) = c("start", "end")
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# merge
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coverage.final = merge(coverage.df, aa.anno.region, by.x = "peptide", by.y = 0, all.x = TRUE)
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coverage.final = coverage.final %>% dplyr::arrange(.data[["start"]], .data[["end"]])
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# get coverage positions
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coverage.pos =
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GenomicRanges::reduce(GenomicRanges::GRanges(protein.id, IRanges::IRanges(coverage.final$start, coverage.final$end))) %>%
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as.data.frame()
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coverage.pos$strand = NULL
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colnames(coverage.pos) = c("ProteinID", "start", "end", "width")
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coverage.pos$Type = "covered"
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# get coverage rate
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coverage.rate = round(sum(coverage.pos$width)*100/nchar(aa.seq.used), 2)
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# non-cover position
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non.coverage.pos =
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GenomicRanges::setdiff(GenomicRanges::GRanges(protein.id, IRanges::IRanges(1, nchar(aa.seq.used))),
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GenomicRanges::GRanges(protein.id, IRanges::IRanges(coverage.final$start, coverage.final$end))) %>%
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as.data.frame()
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non.coverage.pos$strand = NULL
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colnames(non.coverage.pos) = c("ProteinID", "start", "end", "width")
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non.coverage.pos$Type = "bare"
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# coverage summary
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coverage.summary = rbind(coverage.pos, non.coverage.pos) %>% as.data.frame()
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# coverage rect
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coverage.rect = geom_rect(data = coverage.final, mapping = aes_string(xmin = "start", xmax = "end",
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ymin = "0", ymax = "abundance"),
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show.legend = FALSE, fill = color)
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plot.ele <- list(coverage.rect)
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# mark bare
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if(mark.bare){
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bare.rect = geom_rect(data = non.coverage.pos, mapping = aes_string(xmin = "start", xmax = "end",
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ymin = "0", ymax = "Inf"),
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show.legend = F, fill = mark.color, alpha = mark.alpha)
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plot.ele <- append(plot.ele, bare.rect)
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}
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# summary table
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if(show.table){
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# table position
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if(table.position == "left_top"){
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table.x = 0
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table.y = max(coverage.final[ , "abundance"])
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}else if(table.position == "right_top"){
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table.x = nchar(aa.seq.used)
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table.y = max(coverage.final[ , "abundance"])
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}else if(table.position == "left_bottom"){
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table.x = 0
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table.y = 0
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}else if(table.position == "right_bottom"){
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table.x = nchar(aa.seq.used)
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table.y = 0
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}
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summary.table = ggpp::annotate(geom = "table", label = list(coverage.summary), x= table.x, y=table.y,
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color = table.color, size = table.size)
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plot.ele <- append(plot.ele, summary.table)
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}
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# range position
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if (range.position == "in") {
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# prepare range
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max.abundance = CeilingNumber(max(coverage.final$abundance))
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abundance.range = data.frame(label = paste0("[0, ", scales::scientific(max.abundance, digits = 2), "]"))
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range.text = geom_text(
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data = abundance.range,
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mapping = aes(x = -Inf, y = Inf, label = label),
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hjust = 0,
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vjust = 1.5,
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size = range.size
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)
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plot.ele <- append(plot.ele, range.text)
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}
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# change x scale
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plot.ele <- append(plot.ele, scale_x_continuous(limits = c(1, nchar(aa.seq.used)), expand = c(0, 0)))
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return(plot.ele)
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}

‎R/ggprotein.R

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#' Create Mass Spectrometry Protein Coverage Plot.
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#'
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#' @param coverage.file Exported protein coverage file, should be in excel.
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#' @param fasta.file Input reference protein fasta file.
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#' @param protein.id The protein ID of exported coverage file. This should be unique and in \code{fasta.file}.
6+
#' @param XCorr.threshold The cross-correlation threshold. Default: 2.
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#' @param confidence The confidence level. Default: High.
8+
#' @param contaminant Whether to remove contaminant peptides. Default: NULL (not remove).
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#' @param remove.na Logical value, whether to remove NA value in Abundance column. Default: TRUE.
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#' @param color The fill color of coverage plot. Default: grey.
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#' @param mark.bare Logical value, whether to mark region where Abundance is zero or NA. Default: TRUE.
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#' @param mark.color The color used for the marked region. Default: red.
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#' @param mark.alpha The transparency used for the marked region. Default: 0.5.
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#' @param show.table Logical value, whether to show coverage summary table. Default: TRUE.
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#' @param table.position The position of the coverage summary table, choose from right_top, left_top, left_bottom, right_bottom.
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#' Default: right_top.
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#' @param table.size The font size of coverage summary table. Default: 4.
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#' @param table.color The font color of coverage summary table. Default: black.
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#' @param range.size The label size of range text, used when \code{range.position} is in. Default: 3.
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#' @param range.position The position of y axis range, chosen from in (move y axis in the plot) and
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#' out (normal y axis). Default: in.
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#'
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#' @return A ggplot2 object.
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#' @importFrom openxlsx read.xlsx
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#' @importFrom magrittr %>%
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#' @importFrom dplyr filter group_by summarise arrange
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#' @importFrom rlang .data
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#' @importFrom Biostrings readAAStringSet
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#' @importFrom stringr str_locate
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#' @importFrom GenomicRanges reduce GRanges setdiff
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#' @importFrom IRanges IRanges
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#' @importFrom ggplot2 ggplot geom_rect geom_text aes aes_string scale_x_continuous theme_classic theme
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#' element_blank annotate rel scale_y_continuous expansion
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#' @importFrom ggpp annotate
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#' @importFrom scales scientific
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#' @export
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#'
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#' @examples
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#' # library(ggcoverage)
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#' # coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
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#' # fasta.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.fasta", package = "ggcoverage")
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#' # protein.id = "sp|P02769|ALBU_BOVIN"
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#' # ggprotein(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id)
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ggprotein = function(coverage.file, fasta.file, protein.id, XCorr.threshold = 2,
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confidence = "High", contaminant = NULL, remove.na = TRUE,
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color = "grey", mark.bare = TRUE, mark.color = "red", mark.alpha = 0.5,
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show.table = TRUE, table.position = c("right_top", "left_top", "left_bottom", "right_bottom"),
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table.size = 4, table.color = "black", range.size = 3, range.position = c("in", "out"), plot.space = 0.2){
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# check parameters
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table.position <- match.arg(arg = table.position)
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range.position <- match.arg(arg = range.position)
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# ms protein plot
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protein.plot = ggplot() +
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geom_protein(coverage.file = coverage.file, fasta.file = fasta.file, protein.id = protein.id,
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XCorr.threshold = XCorr.threshold, confidence = confidence, contaminant = contaminant,
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remove.na = remove.na, color = color, mark.bare = mark.bare, mark.color = mark.color,
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mark.alpha = mark.alpha, show.table = show.table, table.position = table.position,
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table.size = table.size, table.color = table.color, range.size = range.size, range.position = range.position)
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# add theme
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if (range.position == "in") {
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protein.plot +
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theme_protein()
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} else if (range.position == "out") {
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protein.plot +
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theme_protein2()
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}
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}

‎R/theme_ggcoverage.R

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#'
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#' @return List of layers.
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#' @importFrom ggplot2 theme_classic theme unit element_blank annotate rel scale_y_continuous expansion
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#' scale_x_continuous coord_cartesian
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#' scale_x_continuous
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#' @importFrom scales comma
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#' @export
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#'
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#'
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#' @return List of layers.
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#' @importFrom ggplot2 scale_y_continuous expansion theme_classic theme unit element_blank annotate rel
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#' scale_x_continuous coord_cartesian
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#' scale_x_continuous
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#' @importFrom scales comma
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#' @export
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#'
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coord_cartesian(xlim = x.range)
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)
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}
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# theme for ggprotein: suitable for range position is in
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#' Theme for geom_protein.
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#'
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#' @return List of layers.
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#' @importFrom ggplot2 theme_classic theme unit element_blank annotate rel scale_y_continuous expansion
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#' scale_x_continuous
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#' @importFrom scales comma
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#' @export
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#'
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theme_protein <- function() {
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list(
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theme_classic(),
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theme(
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axis.line.y = element_blank(),
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axis.ticks.y = element_blank(),
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axis.text.y = element_blank(),
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axis.title = element_blank()
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),
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annotate("segment", x = -Inf, xend = Inf, y = -Inf, yend = -Inf, size = rel(1)),
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scale_y_continuous(expand = expansion(mult = c(0)))
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)
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}
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# theme for ggprotein: suitable for range position is out
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#' Theme for geom_protein.
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#'
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#' @return List of layers.
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#' @importFrom ggplot2 scale_y_continuous expansion theme_classic theme unit element_blank annotate rel
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#' scale_x_continuous
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#' @importFrom scales comma scientific
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#' @export
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#'
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theme_protein2 <- function() {
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list(
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scale_y_continuous(
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limits = ~ c(0, CeilingNumber(max(.x)), digits = 2),
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breaks = ~ .x[2],
512+
expand = expansion(mult = c(0)),
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labels = function(x) format(x, scientific = TRUE, digits = 2)
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),
515+
theme_classic(),
516+
theme(
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axis.title = element_blank()
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),
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annotate("segment", x = -Inf, xend = Inf, y = -Inf, yend = -Inf, size = rel(1))
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)
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}
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‎README.Rmd

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)
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```
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# ggcoverage - Visualize and annotate genome coverage with ggplot2
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# ggcoverage - Visualize and annotate omics coverage with ggplot2
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<img src = "man/figures/ggcoverage.png" align = "right" width = "200"/>
2020

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[![CODE_SIZE](https://img.shields.io/github/languages/code-size/showteeth/ggcoverage.svg)](https://github.com/showteeth/ggcoverage)
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## Introduction
26-
The goal of `ggcoverage` is simplify the process of visualizing genome coverage. It contains three main parts:
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The goal of `ggcoverage` is simplify the process of visualizing omics coverage. It contains three main parts:
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28-
* **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.
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* **Create genome coverage plot**
28+
* **Load the data**: `ggcoverage` can load BAM, BigWig (.bw), BedGraph, txt/xlsx files from various omics data, including WGS, RNA-seq, ChIP-seq, ATAC-seq, proteomics, et al.
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* **Create omics coverage plot**
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* **Add annotations**: `ggcoverage` supports six different annotations:
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* **base and amino acid annotation**: Visualize genome coverage at single-nucleotide level with bases and amino acids.
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* **GC annotation**: Visualize genome coverage with GC content
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* **peak annotation**: Visualize genome coverage and peak identified
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* **contact map annotation**: Visualize genome coverage with Hi-C contact map
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* **link annotation**: Visualize genome coverage with contacts
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* **peotein feature annotation**: Visualize protein coverage with features
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`ggcoverage` utilizes `ggplot2` plotting system, so its usage is **ggplot2-style**!
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