R/rawDiag.R
In protViz/rawDiag: Brings Orbitrap Mass Spectrometry Data to Life; Fast and Colorful
Defines functions
plotInjectionTime
plotCycleTime
.cycleTime
plotTicBasepeak
plotPrecursorHeatmap
plotLockMassCorrection
.plotMissingData
validate_readRaw
is.rawDiag
.rawDiagColumns
readRaw
Documented in
.cycleTime
is.rawDiag
plotCycleTime
plotInjectionTime
plotLockMassCorrection
plotPrecursorHeatmap
plotTicBasepeak
readRaw
#R
#' Reads selected raw file trailer information for rawDiag plot functions
#'
#' implements a wrapper function using the rawrr methods
#' \code{\link[rawrr]{readIndex}}, \code{\link[rawrr]{readTrailer}},
#' and \code{\link[rawrr]{readChromatogram}} to read
#' proprietary mass spectrometer generated data using third-party libraries.
#'
#' @note
#' The set up procedure for the rawrr package needs to be run in order to use
#' this package.
#'
#' @inheritParams rawrr::readIndex
#' @param msgFUN this function is used for logging information while composing
#' the resulting data.frame. It can also be used for shiny progress bar. The
#' default is using the \code{message}.
#' @return a \code{data.frame} containing the selected trailer information.
#' @author Christian Panse (2016-2023)
#' @aliases rawDiag readRaw
#' @examples
#' rawrr::sampleFilePath() |>
#' rawDiag::readRaw()
#' @importFrom rawrr readIndex readTrailer readChromatogram
#' @export
#' @references \doi{10.1021/acs.jproteome.8b00173}
readRaw <- function(rawfile, msgFUN = function(x){message(x)}){
message("reading index for ", basename(rawfile), "...")
t0 <- Sys.time()
rawfile |>
rawrr::readIndex() -> rawrrIndex
rawrrIndex$rawfile <- basename(rawfile)
rawfile |>
rawrr::readTrailer() -> trailerNames
msgFUN("determining ElapsedScanTimesec ...")
rawrrIndex$ElapsedScanTimesec <- c(diff(rawrrIndex$StartTime), NA)
if ("LM m/z-Correction (ppm):" %in% trailerNames){
msgFUN("reading trailer LM m/z-Correction (ppm) ...")
rawfile |>
rawrr::readTrailer("LM m/z-Correction (ppm):") |>
as.numeric() -> LMCorrection
rawrrIndex$LMCorrection <- LMCorrection
}else{
rawrrIndex$LMCorrection <- NA
}
if ("AGC:" %in% trailerNames){
msgFUN("reading trailer AGC ...")
rawfile |>
rawrr::readTrailer("AGC:") -> AGC
rawrrIndex$AGC <- AGC
}else{
rawrrIndex$AGC <- NA
}
if ("AGC PS Mode:" %in% trailerNames){
msgFUN("reading trailer AGC PS Mode ...")
rawfile |>
rawrr::readTrailer("AGC PS Mode:") -> PrescanMode
rawrrIndex$PrescanMode <- PrescanMode
}else{
rawrrIndex$PrescanMode <- NA
}
if ("FT Resolution:" %in% trailerNames){
msgFUN("reading trailer FT Resolution ...")
rawfile |>
rawrr::readTrailer("FT Resolution:") |>
as.numeric() -> FTResolution
rawrrIndex$FTResolution <- FTResolution
}else if ("Orbitrap Resolution:" %in% trailerNames){
msgFUN("reading trailer Orbitrap Resolution ...")
rawfile |>
rawrr::readTrailer("Orbitrap Resolution:") |>
as.numeric() -> FTResolution
rawrrIndex$FTResolution <- FTResolution
}else{
rawrrIndex$FTResolution <- NA
}
if ("Ion Injection Time (ms):" %in% trailerNames){
msgFUN("reading trailer Ion Injection Time (ms) ...")
rawfile |>
rawrr::readTrailer("Ion Injection Time (ms):") |>
as.numeric() -> IonInjectionTime
rawrrIndex$IonInjectionTime <- IonInjectionTime
}else{
rawrrIndex$IonInjectionTime <- NA
}
msgFUN("reading TIC ...")
rawrrIndex$TIC <- NA
rawfile |> rawrr::readChromatogram(type = 'tic') -> tic
rawrrIndex$TIC[rawrrIndex$MSOrder == "Ms"] <- tic$intensities
msgFUN("reading BasePeakIntensity ...")
rawrrIndex$BasePeakIntensity <- NA
rawfile |> rawrr::readChromatogram(type = 'bpc') -> bpc
rawrrIndex$BasePeakIntensity[rawrrIndex$MSOrder == "Ms"] <- bpc$intensities
t1 <- Sys.time()
td <- difftime(t1, t0, units = "secs") |>
round(3)
message("reading took", td, "seconds")
rawrrIndex |>
validate_readRaw()
}
.rawDiagColumns <- function(){
c("scan", "scanType", "StartTime", "precursorMass",
"MSOrder", "charge", "masterScan", "dependencyType",
"TIC", "BasePeakIntensity", "ElapsedScanTimesec", "rawfile", "AGC",
"LMCorrection", "PrescanMode", "FTResolution") |>
sort()
}
#' Is an Object an rawDiag Object?
#'
#' @inheritParams methods::is
#' @return a boolean
#' @author Christian Panse 2018
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() |> rawDiag::is.rawDiag()
#'
#' @export
is.rawDiag <- function(object){
cn <- .rawDiagColumns()
msg <- cn[! cn %in% colnames(object)]
if (length(msg) > 0){
message("missing column name(s):", paste(msg, collapse = ", "))
return(FALSE)
}
return(TRUE)
}
validate_readRaw <- function(x){
validateIndex <- TRUE
if (!is.data.frame(x)){
message("Object is not a 'data.frame'.")
valideIndex <- FALSE
}
IndexColNames <- .rawDiagColumns()
for (i in IndexColNames){
if (!(i %in% colnames(x))){
msg <- sprintf("Missing column %s.", i)
warning(msg)
valideIndex <- FALSE
}
}
# stopifnot(valideIndex)
return(x)
}
#' @importFrom ggplot2 geom_blank theme_void
.plotMissingData <- function(){
ggplot2::ggplot() +
ggplot2::geom_blank() +
ggplot2::theme_void() +
ggplot2::labs(
title = "Oops! Missing Data Detected",
subtitle = "It seems there are missing values in your data.",
caption = "Please handle missing data before creating the plot."
)
}
#' Lock Mass Correction Plot
#'
#' @param x a \code{data.frame} object adhering to the specified criteria for
#' the \code{is.rawDiag} function.
#' @param method specifying the plot method 'trellis' | 'violin' | 'overlay'.
#' The default is 'trellis'.
#'
#' @return a \code{\link[ggplot2]{ggplot}} object.
#'
#' @author Christian Trachsel (2017), Christian Panse (2023)
#'
#' @references
#' * rawDiag: \doi{10.1021/acs.jproteome.8b00173},
#' * rawrr: \doi{10.1021/acs.jproteome.0c00866}
#' @md
#' @examples
#' rawrr::sampleFilePath() |>
#' readRaw() |>
#' plotLockMassCorrection()
#'
#' @importFrom ggplot2 ggplot aes_string geom_hline geom_line labs scale_x_continuous facet_wrap theme_light
#' @export
#' @aliases PlotLockMassCorrection
plotLockMassCorrection <- function(x, method = 'trellis'){
if(isFALSE("LMCorrection" %in% colnames(x))) {return(.plotMissingData())}
x |>
base::subset(x['MSOrder'] == "Ms") -> x
if (method %in% c('trellis')){
x |>
ggplot2::ggplot(ggplot2::aes(x = .data$StartTime , y = .data$LMCorrection)) +
ggplot2::geom_hline(yintercept = c(-5, 5), colour = "red3", linetype = "longdash") +
ggplot2::geom_line(linewidth = 0.3) +
ggplot2::geom_line(stat = "smooth",
method= "gam",
formula = y ~ s(x, bs ="cs"),
colour = "deepskyblue3", se = FALSE) -> gp
}else if(method %in% c('overlay')){
x |>
ggplot2::ggplot(ggplot2::aes(x = .data$StartTime , y = .data$LMCorrection, colour = .data$rawfile)) +
ggplot2::geom_hline(yintercept = c(-5, 5), colour = "red3", linetype = "longdash") +
ggplot2::geom_line(linewidth = 0.3) +
ggplot2::geom_line(stat = "smooth",
method= "gam",
formula = y ~ s(x, bs ="cs"),
colour = "deepskyblue3", se = FALSE) -> gp
}else{
x |>
ggplot2::ggplot(ggplot2::aes(x = .data$StartTime , y = .data$LMCorrection)) +
ggplot2::geom_hline(yintercept = c(-5, 5), colour = "red3", linetype = "longdash") +
ggplot2::geom_violin() -> gp
}
gp +
ggplot2::labs(title = "Lock mass correction plot") +
ggplot2::labs(subtitle = "Plotting lock mass correction value versus retention time") +
ggplot2::labs(x = "Retention Time [min]", y = "Lock Mass Correction [ppm]") +
ggplot2::scale_x_continuous(breaks = base::pretty(8)) +
# scale_y_continuous(breaks = scales::pretty_breaks(8), limits = c(-10, 10)) +
ggplot2::facet_wrap(~ rawfile) +
ggplot2::theme_light() -> gp
gp
}
#' Precursor Mass versus StartTime MS2 based hexagons
#'
#' @inherit plotLockMassCorrection params return references author
#' @param bins number of bins in both vertical and horizontal directions. default is 80.
#' @author Christian Trachsel (2017)
#' @note TODO: define bin with dynamically as h= 2x IQR x n e-1/3 or number of bins (max-min)/h
#' @import hexbin
#' @importFrom ggplot2 ggplot aes_string geom_hex labs scale_fill_gradientn theme_light
#' @importFrom grDevices colorRampPalette
#' @examples
#' rawrr::sampleFilePath() |> readRaw() |> plotPrecursorHeatmap()
#' @export
#' @aliases PlotPrecursorHeatmap
plotPrecursorHeatmap <- function(x, method = 'overlay', bins = 80){
spectral <- c("#5E4FA2",
"#3288BD",
"#66C2A5",
"#ABDDA4",
"#E6F598",
"#FFFFBF",
"#FEE08B",
"#FDAE61",
"#F46D43",
"#D53E4F",
"#9E0142")
spectralramp <- colorRampPalette(spectral)
x |>
base::subset(x['MSOrder'] == "Ms2") |>
ggplot2::ggplot(ggplot2::aes_string(x = 'StartTime', y = 'precursorMass')) +
ggplot2::labs(x = "Retention Time [min]", y = "Precursor Mass [Da]") +
ggplot2::geom_hex(bins = bins) +
ggplot2::scale_fill_gradientn(colours = spectralramp(32)) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(15)) +
ggplot2::theme_light() -> gp
if (method == 'trellis'){
gp +
ggplot2::facet_wrap(~ rawfile) -> gp
}
gp +
ggplot2::labs(title = "precursorMass versus StartTime hexagons MS2")
}
#' Total Ion Count and Base Peak Plot
#'
#' displays the Total Ion Count (TIC) and the Base Peak Chromatogram
#' of a mass spectrometry measurement.
#' Multiple files are handled by faceting based on rawfile name.
#'
#' @inheritParams plotLockMassCorrection
#' @return a ggplot2 object for graphing the TIC and the Base Peak chromatogram.
#' @author Christian Trachsel (2017), Christian Panse (20231130) refactored
#' @importFrom ggplot2 ggplot aes_string geom_line labs scale_x_continuous facet_wrap theme_light
#' @importFrom reshape2 melt
#' @examples
#' rawrr::sampleFilePath() |> readRaw() |> plotTicBasepeak()
#' @export
#' @aliases PlotTicBasepeak
plotTicBasepeak <- function(x, method = 'trellis'){
stopifnot(method %in% c('trellis', 'violin', 'overlay'))
x[, c('StartTime', 'TIC', 'BasePeakIntensity', 'rawfile')] |>
base::subset(x$MSOrder == "Ms") |>
reshape2::melt(id.vars = c("StartTime", "rawfile")) -> xx
if (method == 'trellis'){
xx |>
ggplot2::ggplot(ggplot2::aes_string(x = "StartTime", y = "value")) +
ggplot2::geom_line(linewidth = 0.3) +
ggplot2::facet_wrap(rawfile ~ variable, scales = "free", ncol = 2) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) -> gp
}else if(method =='violin'){
xx |>
ggplot2::ggplot(ggplot2::aes(x = "rawfile", y = "value")) +
ggplot2::geom_violin() +
ggplot2::facet_grid(variable ~ ., scales = "free") +
#ggplot2::scale_y_continuous(trans = scales::log10_trans()) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else if (method == 'overlay'){
xx |>
ggplot2::ggplot(ggplot2::aes_string(x = "StartTime", y = "value", colour = "rawfile")) +
ggplot2::geom_line(size = 0.3) +
ggplot2::facet_grid(variable ~ ., scales = "free") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::theme(legend.position="top") -> gp
}else{NULL}
gp +
ggplot2::labs(title = "Total Ion Count and Base-Peak plot") +
ggplot2::labs(subtitle = "Plotting the TIC and base peak density for all mass spectrometry runs") +
ggplot2::labs(x = "Retention Time [min]", y = "Intensity Counts [arb. unit]") +
ggplot2::theme_light()
}
#' Calculate MS Cycle Time
#'
#' calculates the lock mass deviations along RT.
#'
#' @inheritParams plotLockMassCorrection
#'
#' @note TODO: quantile part needed? If no MS1 scan is present?
#' E.g., DIA take lowest window as cycle indicator?
#'
#' @importFrom stats na.omit
#' @author Christian Trachsel (2017), Christian Panse (20231201) refactored
#'
#' @return calculates the time of all ms cycles and the 95% quantile value there of.
#' the cycle time is defined as the time between two consecutive MS1 scans
.cycleTime <- function(x){
x |>
subset(x$MSOrder == "Ms") -> xx
split(xx, xx$rawfile) |>
lapply(function(o){
o$CycleTime <- c(NA, (o$StartTime * 60) |> diff())
o$quan <- quantile(o$CycleTime, probs = 0.95, na.rm = TRUE)
o[, c('StartTime', 'CycleTime', 'quan', 'rawfile')]
}) |>
Reduce(f = rbind) |>
na.omit()
}
#' Plot Cycle Time
#'
#' graphs the time difference between two consecutive MS1 scans
#' (cycle time) with respect to RT (scatter plots) or its density (violin).
#' A smooth curve graphs the trend. The 95th percentile is indicated by a red
#' dashed line.
#'
#' @inherit plotLockMassCorrection params return
#'
#' @importFrom ggplot2 ggplot aes geom_point geom_line scale_x_continuous scale_y_continuous geom_hline theme_light
#' @importFrom scales pretty_breaks
#' @importFrom dplyr left_join
#' @importFrom stats quantile na.omit
#' @importFrom rlang .data
#' @examples
#' rawrr::sampleFilePath() |> readRaw() |> plotCycleTime()
#' @export
#' @aliases PlotCycleTime
plotCycleTime <- function(x, method = 'trellis'){
xx <- .cycleTime(x)
if (method == 'trellis'){
xx |>
ggplot2::ggplot(ggplot2::aes(x = .data$StartTime, y = .data$CycleTime)) +
ggplot2::geom_point(shape = ".") +
ggplot2::geom_line(stat = "smooth", method = "gam", formula = y ~ s(x, bs= "cs"),
colour = "deepskyblue3", se = FALSE) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::geom_hline(ggplot2::aes(yintercept = .data$quan, group = .data$rawfile),
colour = "red3", linetype = "longdash") +
ggplot2::facet_grid(rawfile ~ ., scales = "free") +
ggplot2::labs(subtitle = "Plotting the caclulated cycle time of each cycle vs retention time") +
ggplot2::labs(x = "Retention Time [min]", y = "Cycle Time [sec]") -> gp
}else if (method == 'violin'){
xx |>
ggplot2::ggplot(ggplot2::aes(x = .data$rawfile, y = .data$CycleTime)) +
ggplot2::geom_violin() +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the cycle time density of all mass spectrometry runs") +
ggplot2::labs(x = "rawfile", y = "Cycle Time [sec]") +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
} else if(method == 'overlay'){
xx|>
ggplot2::ggplot(ggplot2::aes(x = .data$StartTime, y = .data$CycleTime, colour = .data$rawfile)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::geom_line(ggplot2::aes(group = .data$rawfile,
colour = .data$rawfile),
stat = "smooth", method = "gam",
formula = y ~ s(x, bs= "cs"), se = FALSE) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the caclulated cycle time of each cycle vs retention time") +
ggplot2::labs(x = "Retention Time [min]", y = "Cycle Time [sec]") +
ggplot2::theme(legend.position="top") -> gp
}else{NULL}
gp +
ggplot2::labs(title = "Cycle time plot") +
ggplot2::labs(x = "Retention Time [min]", y = "Cycle Time [sec]") +
ggplot2::theme_light()
}
#' Plot Injection Time
#'
#' shows the injection time density of each mass spectrometry file as a violin
#' plot. The higher the maximum number of MS2 scans is in the method,
#' the more the density is shifted towards the maximum injection time value.
#'
#' @inherit plotLockMassCorrection params return references author
#' @importFrom ggplot2 ggplot aes geom_point geom_line scale_x_continuous scale_y_continuous geom_hline theme_light
#' @importFrom rlang .data
#' @examples
#' rawrr::sampleFilePath() |> readRaw() |> plotInjectionTime()
#' @export
#' @aliases PlotInjectionTime
plotInjectionTime <- function(x, method = 'trellis'){
if (method == 'trellis'){
maxtimes <- x |>
dplyr::group_by(.data$rawfile, .data$MSOrder) |>
dplyr::summarise(maxima = max(.data$IonInjectionTime))
ggplot2::ggplot(x, ggplot2::aes(x = .data$StartTime,
y = .data$IonInjectionTime)) +
ggplot2::geom_hline(data = maxtimes,
ggplot2::aes(yintercept = .data$maxima),
colour = "red3", linetype = "longdash") +
ggplot2::geom_point(shape = ".") +
ggplot2::geom_line(stat = "smooth", method = "gam",
formula = y ~ s(x, bs= "cs"),
colour = "deepskyblue3", se = FALSE) +
ggplot2::facet_grid(rawfile ~ MSOrder, scales = "free") +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::labs(subtitle = "Plotting injection time against retention time for MS and MSn level") +
ggplot2::labs(x = "Retentione Time [min]", y = "Injection Time [ms]") -> gp
}else if (method == 'violin'){
ggplot2::ggplot(x, ggplot2::aes(x = .data$rawfile,
y = .data$IonInjectionTime)) +
ggplot2::geom_violin() +
ggplot2::facet_grid(MSOrder ~ .) +
ggplot2::labs(subtitle = "Plotting retention time resolved injection time density for each mass spectrometry run") +
ggplot2::labs(x = "rawfile", y = "Injection Time [ms]") +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else if(method == 'overlay'){
ggplot2::ggplot(x, ggplot2::aes(x = .data$StartTime,
y = .data$IonInjectionTime, colour = .data$rawfile)) +
ggplot2::geom_point(size = 0.5, alpha = 0.1) +
ggplot2::geom_line(ggplot2::aes(group = .data$rawfile, colour = .data$rawfile),
stat = "smooth",
method = "gam",
formula = y ~ s(x, bs= "cs"),
se = FALSE) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::facet_grid(~ .data$MSOrder, scales = "free") +
ggplot2::labs(subtitle = "Plotting injection time against retention time for MS and MSn level") +
ggplot2::labs(x = "Retentione Time [min]", y = "Injection Time [ms]") +
ggplot2::theme(legend.position = "top") -> gp
}else{NULL}
gp + ggplot2::labs(title = "Injection time plot") +
ggplot2::theme_light()
}
#' mZ Distribution Plot of Ms2 Scans
#'
#' draws precursor mass vs retention time for each MS2 scan in the raw file.
#'
#' @inherit plotLockMassCorrection params return references author
#' @aliases PlotMzDistribution
#' @importFrom rlang .data
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() -> S
#' plotMzDistribution(S)
#' @export
#' @aliases PlotMzDistribution
plotMzDistribution <- function(x, method='trellis'){
x |>
dplyr::filter(.data$MSOrder == "Ms2") -> xx
if (method == 'trellis'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime,
y = .data$precursorMass)) +
ggplot2::geom_point(shape = ".") +
ggplot2::facet_grid(rawfile ~ ., scales = "free") +
ggplot2::geom_line(stat = "smooth", method = "gam",
formula = y ~ s(x, bs= "cs"),
linewidth = 1.1, alpha = 0.6,
colour = "cornflowerblue", se = FALSE) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting retention time against m/z value of all selected precursors") +
ggplot2::labs(x = "Retention Time", y = "Presursor m/z value") -> gp
}else if (method == 'violin'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$rawfile,
y = .data$precursorMass)) +
ggplot2::geom_violin() +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the precursor m/z value density of all mass spectrometry runs") +
ggplot2::labs(x = "Filename", y = "Presursor m/z value [Da]") +
ggplot2::theme_light() +
ggplot2::theme(
axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else if (method == 'overlay'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime,
y = .data$precursorMass,
colour = .data$rawfile)) +
ggplot2::geom_point(size = 0.5, alpha = 0.3) +
ggplot2::geom_line(stat = "smooth",
method = "gam",
formula = y ~ s(x, bs= "cs"),
size = 1.1,
se = FALSE) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting retention time against m/z value of all selected precursors") +
ggplot2::labs(x = "Retention Time [min]", y = "Presursor m/z value [Da]") +
ggplot2::theme(legend.position="top") -> gp
}else{NULL}
gp +
ggplot2::theme_light() +
ggplot2::labs(title = "Retention Time to m/z correlation plot")
}
#' Mass Distribution Plot
#'
#' displays charge state resolved frequency of precursor masses.
#'
#' @description plots the mass frequency in dependency to the charge state
#' @inherit plotLockMassCorrection params return references author
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() |> rawDiag::plotMassDistribution('overlay')
#' @export
#' @aliases PlotMassDistribution
plotMassDistribution <- function(x, method = 'trellis'){
x[x['MSOrder'] == 'Ms2', c('MSOrder', 'charge','rawfile', 'precursorMass')] -> xx
xx$deconv <- round((xx$precursorMass - 1.00782) * xx$charge, 0)
xx$charge <- factor(xx$charge)
if (method == 'trellis'){
xx |>
ggplot2::ggplot(ggplot2::aes(x = .data$deconv, fill = .data$charge, colour = .data$charge)) +
ggplot2::geom_histogram(binwidth = 50, alpha = 0.3, position = "identity") +
ggplot2::labs(title = "Precursor mass to charge frequency plot ") +
ggplot2::labs(subtitle = "Plotting charge state resolved frequency of precursor masses") +
ggplot2::labs(x = "Precursor neutral mass [Da]", y = "Frequency [counts]") +
ggplot2::labs(fill = "Charge State", colour = "Charge State") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::coord_cartesian(xlim = range(xx$deconv)) +
ggplot2::facet_wrap(~ .data$rawfile) -> gp
}else if (method == 'violin'){ #mz.frequency.violin
xx |>
ggplot2::ggplot(ggplot2::aes(x = .data$charge, y = .data$deconv, fill = .data$rawfile)) +
ggplot2::geom_violin() +
ggplot2::labs(title = "Precursor mass to charge density plot ") +
ggplot2::labs(subtitle = "Plotting the charge state resolved precursor masse density for each mass spectrometry run") +
ggplot2::labs(x = "Charge State ", y = "Neutral Mass [Da]") +
ggplot2::theme(legend.position = "top") -> gp
}else if (method == 'overlay'){ #mz.frequency.overlay
xx |>
ggplot2::ggplot(ggplot2::aes(x = .data$deconv, colour = .data$rawfile)) +
ggplot2::geom_line(stat = "density") +
ggplot2::labs(title = "Precursor mass density plot ") +
ggplot2::labs(subtitle = "Plotting the precursor masse density for each mass spectrometry run") +
ggplot2::labs(x = "Precursor mass [neutral mass]", y = "Density") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(12)) +
ggplot2::coord_cartesian(xlim = range(xx$deconv)) +
ggplot2::theme(legend.position = "top") -> gp
}else{NULL}
gp +
ggplot2::theme_light()
}
#' Charge State Overview Plot
#'
#' graphs the number of occurrences of all selected precursor charge states.
#'
#' @inherit plotLockMassCorrection params return references author
#' @importFrom rlang .data
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() -> S
#'
#' S|>plotLockMassCorrection()
#' @export
#' @aliases PlotChargeState
plotChargeState <- function(x, method='trellis'){
x |>
dplyr::filter(.data$MSOrder == "Ms2") |>
dplyr::group_by_at(dplyr::vars(.data$rawfile)) |>
dplyr::count(.data$charge) |>
dplyr::ungroup() |>
dplyr::rename_at(dplyr::vars("n"), list(~ as.character("Counts"))) -> xx
xx$percentage <- (100 / sum(xx$Counts)) * xx$Counts
xbreaks <- unique(xx$charge)
if (method == 'trellis'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$charge, y = .data$percentage)) +
ggplot2::geom_bar(stat = "identity", fill = "cornflowerblue") +
ggplot2::geom_text(ggplot2::aes(label = .data$Counts),
vjust=-0.3, size=3.5) +
ggplot2::scale_x_continuous(breaks = xbreaks) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(15),
expand = c(0, 0),
limits = c(0, (max(xx$percentage)) + 3)) +
ggplot2::labs(subtitle = "Plotting the number of occurrences of all selected precursor charge states") +
ggplot2::labs(x = "Charge States", y = "Percent [%]") +
ggplot2::facet_wrap(~ rawfile) -> gp
}else if(method =='overlay'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$charge, y = .data$percentage,
fill = .data$rawfile)) +
ggplot2::geom_bar(stat = "identity", position = ggplot2::position_dodge(),
colour = "black") +
ggplot2::scale_x_continuous(breaks = xbreaks) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(15),
expand = c(0, 0),
limits = c(0, (max(xx$percentage)) + 3)) +
ggplot2::labs(subtitle = "Plotting the number of occurrences of all selected precursor charge states") +
ggplot2::labs(x = "Charge States", y = "Percent [%]") +
ggplot2::theme(legend.position = "top") -> gp
}else if (method =='violin'){
x |>
dplyr::filter(.data$MSOrder == "Ms2") -> xx
ggplot2::ggplot(xx, ggplot2::aes(x = .data$rawfile, y = .data$charge)) +
ggplot2::geom_violin() +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the precursor charge state density for each mass spectrometry run") +
ggplot2::labs(x = "Filename", y = "Charge State") +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else{NULL}
gp +
ggplot2::theme_light() +
ggplot2::labs(title = "Charge state plot")
}
.mapType <- function(x){
x |>
dplyr::mutate(Type = dplyr::case_when(grepl("FTMS [[:punct:]] c NSI Full ms", .data$scanType) == "TRUE" ~ "FT_Full_ms_c",
grepl("FTMS [[:punct:]] p NSI Full ms", .data$scanType) == "TRUE" ~ "FT_Full_ms_p",
grepl("FTMS [[:punct:]] c NSI Full lock ms", .data$scanType) == "TRUE" ~ "FT_Full_ms_c",
grepl("FTMS [[:punct:]] p NSI Full lock ms", .data$scanType) == "TRUE" ~ "FT_Full_ms_p",
grepl("FTMS [[:punct:]] c NSI d Full ms2", .data$scanType) == "TRUE" ~ "FT_d_Full_ms2_c",
grepl("FTMS [[:punct:]] p NSI d Full ms2", .data$scanType) == "TRUE" ~ "FT_d_Full_ms2_p",
grepl("FTMS [[:punct:]] c NSI SIM ms", .data$scanType) == "TRUE" ~ "FT_SIM_ms_c",
grepl("FTMS [[:punct:]] p NSI SIM ms", .data$scanType) == "TRUE" ~ "FT_SIM_ms_p",
grepl("FTMS [[:punct:]] p NSI SIM msx ms", .data$scanType) == "TRUE" ~ "FT_msxSIM_ms_p",
grepl("FTMS [[:punct:]] c NSI SIM msx ms", .data$scanType) == "TRUE" ~ "FT_msxSIM_ms_c",
grepl("FTMS [[:punct:]] c NSI Full ms2", .data$scanType) == "TRUE" ~ "FT_Full_ms2_c",
grepl("FTMS [[:punct:]] p NSI Full ms2", .data$scanType) == "TRUE" ~ "FT_Full_ms2_p",
grepl("ITMS [[:punct:]] c NSI r d Full ms2", .data$scanType) == "TRUE" ~ "IT_Full_ms2_c",
grepl("ITMS [[:punct:]] p NSI r d Full ms2", .data$scanType) == "TRUE" ~ "IT_Full_ms2_p"
)
)
}
#TODO: supply cases as list
#patterns <- list(
#x %% 35 == 0 ~ "fizz buzz",
#x %% 5 == 0 ~ "fizz",
#x %% 7 == 0 ~ "buzz",
#TRUE ~ as.character(x)
#)
#case_when(!!! patterns)
.calcTransient <- function(x){
x$MassAnalyzer <- NA
x$MassAnalyzer[grepl(x$scanType, pattern="^ITMS")] <- "ITMS"
x$MassAnalyzer[grepl(x$scanType, pattern="^FTMS")] <- "FTMS"
x |>
dplyr::mutate(transient = dplyr::case_when(FTResolution == 7500 ~ 16,
FTResolution == 15000 ~ 32,
FTResolution == 17500 ~ 64,
FTResolution == 30000 ~ 64,
FTResolution == 45000 ~ 96,
FTResolution == 50000 ~ 96,
FTResolution == 35000 ~ 128,
FTResolution == 60000 ~ 128,
FTResolution == 70000 ~ 256,
FTResolution == 120000 ~ 256,
FTResolution == 140000 ~ 512,
FTResolution == 240000 ~ 512
)
)
}
#' Scan Event Plot
#'
#' Plotting the elapsed scan time for each individual scan event.
#'
#' @inherit plotLockMassCorrection params return references author
#' @importFrom rlang .data
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() -> S
#'
#' S|> plotScanTime()
#' @export
#' @aliases PlotScanTime
plotScanTime <- function(x, method='trellis'){
x |>
.calcTransient() |>
dplyr::mutate(ElapsedScanTimesec = .data$ElapsedScanTimesec * 1000) |>
dplyr::select_at(dplyr::vars("StartTime", "scanType", "ElapsedScanTimesec", "rawfile", "MassAnalyzer", "MSOrder", "transient")) |>
na.omit() |>
.mapType()-> xx
if(method == 'trellis'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime, y = .data$ElapsedScanTimesec)) +
ggplot2::geom_point(shape = ".") +
ggplot2::facet_grid(rawfile ~ Type) +
ggplot2::geom_line(stat = "smooth", method = "gam",
formula = y ~ s(x), colour = "deepskyblue3", se = FALSE) +
ggplot2::labs(subtitle = "Plotting the elapsed scan time for each individual scan") +
ggplot2::labs(x = "Retentione Time [min]", y = "Elapsed Scan Time [ms]") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::geom_hline(data = xx, ggplot2::aes(yintercept = .data$transient), colour = "red3") -> gp
}else if (method == 'violin'){
xx |>
dplyr::mutate_at(dplyr::vars("ElapsedScanTimesec"), list(~ .*1000)) |>
dplyr::select_at(dplyr::vars("ElapsedScanTimesec", "rawfile", "MassAnalyzer", "MSOrder")) |>
na.omit() -> xxx
ggplot2::ggplot(xxx, ggplot2::aes(x = .data$rawfile, y = .data$ElapsedScanTimesec)) +
ggplot2::geom_violin() +
ggplot2::facet_grid(MSOrder + MassAnalyzer ~ ., scales = "free") +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the retention time resolved elapsed scan time density for each mass spectrometry run") +
ggplot2::labs(x = "rawfile", y = "Elapsed Scan Time [ms]") +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else if (method == 'overlay'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime, y = .data$ElapsedScanTimesec, colour = .data$rawfile)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::geom_line(ggplot2::aes(group = "rawfile", colour = "rawfile"), stat = "smooth", method = "gam", formula = y ~ s(x, bs= "cs"), se = FALSE) +
ggplot2::facet_grid(~ MSOrder + MassAnalyzer, scales = "free") +
ggplot2::labs(subtitle = "Plotting the elapsed scan time for each individual scan") +
ggplot2::labs(x = "Retentione Time [min]", y = "Elapsed Scan Time [ms]") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::theme(legend.position="top") -> gp
}else{NULL}
gp +
ggplot2::theme_light() +
ggplot2::labs(title = "Scan time plot")
}
#' Fill NA values with last previous value
#'
#' @param x a vector of values
#' @author Christian Trachsel
#' @return a vector with any NA values replaced with the last previous actuall value
#'
#' @examples
#' c(NA, 1, 2, 3, NA, 4, 5, NA, NA, NA, 6) |>
#' rawDiag:::.fillNAgaps()
.fillNAgaps <- function(x) {
goodVals <- c(NA, x[!is.na(x)])
fillIdx <- cumsum(!is.na(x))+1
res <- goodVals[fillIdx]
return(res)
}
#' Calculate Master Scan Number
#'
#' calculates the MS1 master scan number of an MS2 scan
#' and populates the MasterScanNumber with it
#'
#' @return a \code{data.frame} containing a \code{MasterScanNumber} column.
#' @inheritParams plotLockMassCorrection
#' @importFrom rlang .data
#'
#' @author Christian Trachsel
.calculatioMasterScan <- function(x){
x |>
dplyr::mutate(MasterScanNumber = dplyr::case_when(.data$MSOrder == "Ms" ~ .data$scan)) |>
dplyr::mutate(MasterScanNumber = .fillNAgaps(.data$MasterScanNumber)) |>
dplyr::mutate(MasterScanNumber = replace(.data$MasterScanNumber, .data$scan == .data$MasterScanNumber, NA)) |>
dplyr::pull(.data$MasterScanNumber) -> x$MasterScanNumber
x
}
#' Cycle Load Plot
#'
#' plotting the number of MS2 per MS1 (the duty cycle) scan versus retention
#' time. The deepskyblue colored loess curve shows the trend.
#'
#' @inherit plotLockMassCorrection params return references author
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() -> S
#'
#' S|> plotCycleLoad()
#' @export
#' @aliases PlotCycleLoad
plotCycleLoad <- function(x, method = 'trellis'){
x |>
.calculatioMasterScan() |>
dplyr::filter(.data$MSOrder == "Ms2") |>
dplyr::group_by_at(dplyr::vars("rawfile")) |>
dplyr::count(.data$MasterScanNumber) |>
dplyr::rename(scan = .data$MasterScanNumber) -> MS2
x [, c('StartTime', 'scan')] -> MS
xx <- dplyr::inner_join(MS, MS2, by = "scan")
if (method == 'trellis'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime, y = .data$n)) +
ggplot2::geom_point(shape = ".") +
ggplot2::geom_line(stat = "smooth",
method = "loess",
span = 0.2,
colour = "deepskyblue3",
se = FALSE) +
ggplot2::facet_wrap(~ rawfile) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the number of MS2 per MS1 scan versus retention time. The deepskyblue colored loess curve shows the trend.") -> gp
}else if(method == 'violin'){
xx <- dplyr::inner_join(MS, MS2, by = "scan") |>
dplyr::mutate_at(dplyr::vars("rawfile"), list(~ as.factor(.))) |>
dplyr::mutate_at(dplyr::vars("n"), list(~ as.numeric(.)))
ggplot2::ggplot(xx, ggplot2::aes(x = .data$rawfile, y = .data$n)) +
ggplot2::geom_violin() +
ggplot2::labs(subtitle = "Plotting the duty cycle resolved MS2 density for each mass spectrometry run") +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else if (method == 'overlay'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime,
y = .data$n,
colour = .data$rawfile)) +
ggplot2::geom_point(shape = ".") +
ggplot2::geom_line(ggplot2::aes(group = .data$rawfile,
colour = .data$rawfile),
stat = "smooth", method = "loess", span = 0.2,
se = FALSE) +
ggplot2::labs(title = "Time resolved number of MS2 scans") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the number of MS2 per MS1 scan versus retention time") +
ggplot2::theme(legend.position = "top") -> gp
}else{NULL}
gp +
ggplot2::labs(title = "Time resolved number of MS2 scans") +
ggplot2::labs(x = "Retention Time [min]", y = "Number of MS2 per MS1 [counts]") +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::coord_cartesian(ylim = c(0, max(xx$n) + 1)) +
ggplot2::theme_light()
}
#' Checks Bioconductor installation instructions
#' @importFrom rawrr installRawFileReaderDLLs installRawrrExe
#' @importFrom BiocManager install
#' @return TRUE if everything is installed correctly
#' @export
checkRawrr <- function(){
if (isFALSE(requireNamespace("BiocManager", quietly = TRUE)))
stop("exec", "install.packages('BiocManager')")
if (isFALSE(requireNamespace("rawrr", quietly = TRUE)))
stop("exec", "BiocManager::install('rawrr')")
if (isFALSE(rawrr:::.checkRawFileReaderDLLs()))
rawrr::installRawFileReaderDLLs()
if (isFALSE(file.exists(rawrr:::.rawrrAssembly())))
rawrr::installRawrrExe()
if (isFALSE(rawrr:::.isAssemblyWorking()))
stop("rawrr assembly is not working. check if mono if available.")
TRUE
}
#' @keywords internal
"_PACKAGE"
## usethis namespace: start
## usethis namespace: end
NULL
protViz/rawDiag documentation built on April 24, 2024, 4:28 a.m.
R Package Documentation
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Defines functions plotInjectionTime plotCycleTime .cycleTime plotTicBasepeak plotPrecursorHeatmap plotLockMassCorrection .plotMissingData validate_readRaw is.rawDiag .rawDiagColumns readRaw
Documented in .cycleTime is.rawDiag plotCycleTime plotInjectionTime plotLockMassCorrection plotPrecursorHeatmap plotTicBasepeak readRaw
#R
#' Reads selected raw file trailer information for rawDiag plot functions
#'
#' implements a wrapper function using the rawrr methods
#' \code{\link[rawrr]{readIndex}}, \code{\link[rawrr]{readTrailer}},
#' and \code{\link[rawrr]{readChromatogram}} to read
#' proprietary mass spectrometer generated data using third-party libraries.
#'
#' @note
#' The set up procedure for the rawrr package needs to be run in order to use
#' this package.
#'
#' @inheritParams rawrr::readIndex
#' @param msgFUN this function is used for logging information while composing
#' the resulting data.frame. It can also be used for shiny progress bar. The
#' default is using the \code{message}.
#' @return a \code{data.frame} containing the selected trailer information.
#' @author Christian Panse (2016-2023)
#' @aliases rawDiag readRaw
#' @examples
#' rawrr::sampleFilePath() |>
#' rawDiag::readRaw()
#' @importFrom rawrr readIndex readTrailer readChromatogram
#' @export
#' @references \doi{10.1021/acs.jproteome.8b00173}
readRaw <- function(rawfile, msgFUN = function(x){message(x)}){
message("reading index for ", basename(rawfile), "...")
t0 <- Sys.time()
rawfile |>
rawrr::readIndex() -> rawrrIndex
rawrrIndex$rawfile <- basename(rawfile)
rawfile |>
rawrr::readTrailer() -> trailerNames
msgFUN("determining ElapsedScanTimesec ...")
rawrrIndex$ElapsedScanTimesec <- c(diff(rawrrIndex$StartTime), NA)
if ("LM m/z-Correction (ppm):" %in% trailerNames){
msgFUN("reading trailer LM m/z-Correction (ppm) ...")
rawfile |>
rawrr::readTrailer("LM m/z-Correction (ppm):") |>
as.numeric() -> LMCorrection
rawrrIndex$LMCorrection <- LMCorrection
}else{
rawrrIndex$LMCorrection <- NA
}
if ("AGC:" %in% trailerNames){
msgFUN("reading trailer AGC ...")
rawfile |>
rawrr::readTrailer("AGC:") -> AGC
rawrrIndex$AGC <- AGC
}else{
rawrrIndex$AGC <- NA
}
if ("AGC PS Mode:" %in% trailerNames){
msgFUN("reading trailer AGC PS Mode ...")
rawfile |>
rawrr::readTrailer("AGC PS Mode:") -> PrescanMode
rawrrIndex$PrescanMode <- PrescanMode
}else{
rawrrIndex$PrescanMode <- NA
}
if ("FT Resolution:" %in% trailerNames){
msgFUN("reading trailer FT Resolution ...")
rawfile |>
rawrr::readTrailer("FT Resolution:") |>
as.numeric() -> FTResolution
rawrrIndex$FTResolution <- FTResolution
}else if ("Orbitrap Resolution:" %in% trailerNames){
msgFUN("reading trailer Orbitrap Resolution ...")
rawfile |>
rawrr::readTrailer("Orbitrap Resolution:") |>
as.numeric() -> FTResolution
rawrrIndex$FTResolution <- FTResolution
}else{
rawrrIndex$FTResolution <- NA
}
if ("Ion Injection Time (ms):" %in% trailerNames){
msgFUN("reading trailer Ion Injection Time (ms) ...")
rawfile |>
rawrr::readTrailer("Ion Injection Time (ms):") |>
as.numeric() -> IonInjectionTime
rawrrIndex$IonInjectionTime <- IonInjectionTime
}else{
rawrrIndex$IonInjectionTime <- NA
}
msgFUN("reading TIC ...")
rawrrIndex$TIC <- NA
rawfile |> rawrr::readChromatogram(type = 'tic') -> tic
rawrrIndex$TIC[rawrrIndex$MSOrder == "Ms"] <- tic$intensities
msgFUN("reading BasePeakIntensity ...")
rawrrIndex$BasePeakIntensity <- NA
rawfile |> rawrr::readChromatogram(type = 'bpc') -> bpc
rawrrIndex$BasePeakIntensity[rawrrIndex$MSOrder == "Ms"] <- bpc$intensities
t1 <- Sys.time()
td <- difftime(t1, t0, units = "secs") |>
round(3)
message("reading took", td, "seconds")
rawrrIndex |>
validate_readRaw()
}
.rawDiagColumns <- function(){
c("scan", "scanType", "StartTime", "precursorMass",
"MSOrder", "charge", "masterScan", "dependencyType",
"TIC", "BasePeakIntensity", "ElapsedScanTimesec", "rawfile", "AGC",
"LMCorrection", "PrescanMode", "FTResolution") |>
sort()
}
#' Is an Object an rawDiag Object?
#'
#' @inheritParams methods::is
#' @return a boolean
#' @author Christian Panse 2018
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() |> rawDiag::is.rawDiag()
#'
#' @export
is.rawDiag <- function(object){
cn <- .rawDiagColumns()
msg <- cn[! cn %in% colnames(object)]
if (length(msg) > 0){
message("missing column name(s):", paste(msg, collapse = ", "))
return(FALSE)
}
return(TRUE)
}
validate_readRaw <- function(x){
validateIndex <- TRUE
if (!is.data.frame(x)){
message("Object is not a 'data.frame'.")
valideIndex <- FALSE
}
IndexColNames <- .rawDiagColumns()
for (i in IndexColNames){
if (!(i %in% colnames(x))){
msg <- sprintf("Missing column %s.", i)
warning(msg)
valideIndex <- FALSE
}
}
# stopifnot(valideIndex)
return(x)
}
#' @importFrom ggplot2 geom_blank theme_void
.plotMissingData <- function(){
ggplot2::ggplot() +
ggplot2::geom_blank() +
ggplot2::theme_void() +
ggplot2::labs(
title = "Oops! Missing Data Detected",
subtitle = "It seems there are missing values in your data.",
caption = "Please handle missing data before creating the plot."
)
}
#' Lock Mass Correction Plot
#'
#' @param x a \code{data.frame} object adhering to the specified criteria for
#' the \code{is.rawDiag} function.
#' @param method specifying the plot method 'trellis' | 'violin' | 'overlay'.
#' The default is 'trellis'.
#'
#' @return a \code{\link[ggplot2]{ggplot}} object.
#'
#' @author Christian Trachsel (2017), Christian Panse (2023)
#'
#' @references
#' * rawDiag: \doi{10.1021/acs.jproteome.8b00173},
#' * rawrr: \doi{10.1021/acs.jproteome.0c00866}
#' @md
#' @examples
#' rawrr::sampleFilePath() |>
#' readRaw() |>
#' plotLockMassCorrection()
#'
#' @importFrom ggplot2 ggplot aes_string geom_hline geom_line labs scale_x_continuous facet_wrap theme_light
#' @export
#' @aliases PlotLockMassCorrection
plotLockMassCorrection <- function(x, method = 'trellis'){
if(isFALSE("LMCorrection" %in% colnames(x))) {return(.plotMissingData())}
x |>
base::subset(x['MSOrder'] == "Ms") -> x
if (method %in% c('trellis')){
x |>
ggplot2::ggplot(ggplot2::aes(x = .data$StartTime , y = .data$LMCorrection)) +
ggplot2::geom_hline(yintercept = c(-5, 5), colour = "red3", linetype = "longdash") +
ggplot2::geom_line(linewidth = 0.3) +
ggplot2::geom_line(stat = "smooth",
method= "gam",
formula = y ~ s(x, bs ="cs"),
colour = "deepskyblue3", se = FALSE) -> gp
}else if(method %in% c('overlay')){
x |>
ggplot2::ggplot(ggplot2::aes(x = .data$StartTime , y = .data$LMCorrection, colour = .data$rawfile)) +
ggplot2::geom_hline(yintercept = c(-5, 5), colour = "red3", linetype = "longdash") +
ggplot2::geom_line(linewidth = 0.3) +
ggplot2::geom_line(stat = "smooth",
method= "gam",
formula = y ~ s(x, bs ="cs"),
colour = "deepskyblue3", se = FALSE) -> gp
}else{
x |>
ggplot2::ggplot(ggplot2::aes(x = .data$StartTime , y = .data$LMCorrection)) +
ggplot2::geom_hline(yintercept = c(-5, 5), colour = "red3", linetype = "longdash") +
ggplot2::geom_violin() -> gp
}
gp +
ggplot2::labs(title = "Lock mass correction plot") +
ggplot2::labs(subtitle = "Plotting lock mass correction value versus retention time") +
ggplot2::labs(x = "Retention Time [min]", y = "Lock Mass Correction [ppm]") +
ggplot2::scale_x_continuous(breaks = base::pretty(8)) +
# scale_y_continuous(breaks = scales::pretty_breaks(8), limits = c(-10, 10)) +
ggplot2::facet_wrap(~ rawfile) +
ggplot2::theme_light() -> gp
gp
}
#' Precursor Mass versus StartTime MS2 based hexagons
#'
#' @inherit plotLockMassCorrection params return references author
#' @param bins number of bins in both vertical and horizontal directions. default is 80.
#' @author Christian Trachsel (2017)
#' @note TODO: define bin with dynamically as h= 2x IQR x n e-1/3 or number of bins (max-min)/h
#' @import hexbin
#' @importFrom ggplot2 ggplot aes_string geom_hex labs scale_fill_gradientn theme_light
#' @importFrom grDevices colorRampPalette
#' @examples
#' rawrr::sampleFilePath() |> readRaw() |> plotPrecursorHeatmap()
#' @export
#' @aliases PlotPrecursorHeatmap
plotPrecursorHeatmap <- function(x, method = 'overlay', bins = 80){
spectral <- c("#5E4FA2",
"#3288BD",
"#66C2A5",
"#ABDDA4",
"#E6F598",
"#FFFFBF",
"#FEE08B",
"#FDAE61",
"#F46D43",
"#D53E4F",
"#9E0142")
spectralramp <- colorRampPalette(spectral)
x |>
base::subset(x['MSOrder'] == "Ms2") |>
ggplot2::ggplot(ggplot2::aes_string(x = 'StartTime', y = 'precursorMass')) +
ggplot2::labs(x = "Retention Time [min]", y = "Precursor Mass [Da]") +
ggplot2::geom_hex(bins = bins) +
ggplot2::scale_fill_gradientn(colours = spectralramp(32)) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(15)) +
ggplot2::theme_light() -> gp
if (method == 'trellis'){
gp +
ggplot2::facet_wrap(~ rawfile) -> gp
}
gp +
ggplot2::labs(title = "precursorMass versus StartTime hexagons MS2")
}
#' Total Ion Count and Base Peak Plot
#'
#' displays the Total Ion Count (TIC) and the Base Peak Chromatogram
#' of a mass spectrometry measurement.
#' Multiple files are handled by faceting based on rawfile name.
#'
#' @inheritParams plotLockMassCorrection
#' @return a ggplot2 object for graphing the TIC and the Base Peak chromatogram.
#' @author Christian Trachsel (2017), Christian Panse (20231130) refactored
#' @importFrom ggplot2 ggplot aes_string geom_line labs scale_x_continuous facet_wrap theme_light
#' @importFrom reshape2 melt
#' @examples
#' rawrr::sampleFilePath() |> readRaw() |> plotTicBasepeak()
#' @export
#' @aliases PlotTicBasepeak
plotTicBasepeak <- function(x, method = 'trellis'){
stopifnot(method %in% c('trellis', 'violin', 'overlay'))
x[, c('StartTime', 'TIC', 'BasePeakIntensity', 'rawfile')] |>
base::subset(x$MSOrder == "Ms") |>
reshape2::melt(id.vars = c("StartTime", "rawfile")) -> xx
if (method == 'trellis'){
xx |>
ggplot2::ggplot(ggplot2::aes_string(x = "StartTime", y = "value")) +
ggplot2::geom_line(linewidth = 0.3) +
ggplot2::facet_wrap(rawfile ~ variable, scales = "free", ncol = 2) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) -> gp
}else if(method =='violin'){
xx |>
ggplot2::ggplot(ggplot2::aes(x = "rawfile", y = "value")) +
ggplot2::geom_violin() +
ggplot2::facet_grid(variable ~ ., scales = "free") +
#ggplot2::scale_y_continuous(trans = scales::log10_trans()) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else if (method == 'overlay'){
xx |>
ggplot2::ggplot(ggplot2::aes_string(x = "StartTime", y = "value", colour = "rawfile")) +
ggplot2::geom_line(size = 0.3) +
ggplot2::facet_grid(variable ~ ., scales = "free") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::theme(legend.position="top") -> gp
}else{NULL}
gp +
ggplot2::labs(title = "Total Ion Count and Base-Peak plot") +
ggplot2::labs(subtitle = "Plotting the TIC and base peak density for all mass spectrometry runs") +
ggplot2::labs(x = "Retention Time [min]", y = "Intensity Counts [arb. unit]") +
ggplot2::theme_light()
}
#' Calculate MS Cycle Time
#'
#' calculates the lock mass deviations along RT.
#'
#' @inheritParams plotLockMassCorrection
#'
#' @note TODO: quantile part needed? If no MS1 scan is present?
#' E.g., DIA take lowest window as cycle indicator?
#'
#' @importFrom stats na.omit
#' @author Christian Trachsel (2017), Christian Panse (20231201) refactored
#'
#' @return calculates the time of all ms cycles and the 95% quantile value there of.
#' the cycle time is defined as the time between two consecutive MS1 scans
.cycleTime <- function(x){
x |>
subset(x$MSOrder == "Ms") -> xx
split(xx, xx$rawfile) |>
lapply(function(o){
o$CycleTime <- c(NA, (o$StartTime * 60) |> diff())
o$quan <- quantile(o$CycleTime, probs = 0.95, na.rm = TRUE)
o[, c('StartTime', 'CycleTime', 'quan', 'rawfile')]
}) |>
Reduce(f = rbind) |>
na.omit()
}
#' Plot Cycle Time
#'
#' graphs the time difference between two consecutive MS1 scans
#' (cycle time) with respect to RT (scatter plots) or its density (violin).
#' A smooth curve graphs the trend. The 95th percentile is indicated by a red
#' dashed line.
#'
#' @inherit plotLockMassCorrection params return
#'
#' @importFrom ggplot2 ggplot aes geom_point geom_line scale_x_continuous scale_y_continuous geom_hline theme_light
#' @importFrom scales pretty_breaks
#' @importFrom dplyr left_join
#' @importFrom stats quantile na.omit
#' @importFrom rlang .data
#' @examples
#' rawrr::sampleFilePath() |> readRaw() |> plotCycleTime()
#' @export
#' @aliases PlotCycleTime
plotCycleTime <- function(x, method = 'trellis'){
xx <- .cycleTime(x)
if (method == 'trellis'){
xx |>
ggplot2::ggplot(ggplot2::aes(x = .data$StartTime, y = .data$CycleTime)) +
ggplot2::geom_point(shape = ".") +
ggplot2::geom_line(stat = "smooth", method = "gam", formula = y ~ s(x, bs= "cs"),
colour = "deepskyblue3", se = FALSE) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::geom_hline(ggplot2::aes(yintercept = .data$quan, group = .data$rawfile),
colour = "red3", linetype = "longdash") +
ggplot2::facet_grid(rawfile ~ ., scales = "free") +
ggplot2::labs(subtitle = "Plotting the caclulated cycle time of each cycle vs retention time") +
ggplot2::labs(x = "Retention Time [min]", y = "Cycle Time [sec]") -> gp
}else if (method == 'violin'){
xx |>
ggplot2::ggplot(ggplot2::aes(x = .data$rawfile, y = .data$CycleTime)) +
ggplot2::geom_violin() +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the cycle time density of all mass spectrometry runs") +
ggplot2::labs(x = "rawfile", y = "Cycle Time [sec]") +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
} else if(method == 'overlay'){
xx|>
ggplot2::ggplot(ggplot2::aes(x = .data$StartTime, y = .data$CycleTime, colour = .data$rawfile)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::geom_line(ggplot2::aes(group = .data$rawfile,
colour = .data$rawfile),
stat = "smooth", method = "gam",
formula = y ~ s(x, bs= "cs"), se = FALSE) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the caclulated cycle time of each cycle vs retention time") +
ggplot2::labs(x = "Retention Time [min]", y = "Cycle Time [sec]") +
ggplot2::theme(legend.position="top") -> gp
}else{NULL}
gp +
ggplot2::labs(title = "Cycle time plot") +
ggplot2::labs(x = "Retention Time [min]", y = "Cycle Time [sec]") +
ggplot2::theme_light()
}
#' Plot Injection Time
#'
#' shows the injection time density of each mass spectrometry file as a violin
#' plot. The higher the maximum number of MS2 scans is in the method,
#' the more the density is shifted towards the maximum injection time value.
#'
#' @inherit plotLockMassCorrection params return references author
#' @importFrom ggplot2 ggplot aes geom_point geom_line scale_x_continuous scale_y_continuous geom_hline theme_light
#' @importFrom rlang .data
#' @examples
#' rawrr::sampleFilePath() |> readRaw() |> plotInjectionTime()
#' @export
#' @aliases PlotInjectionTime
plotInjectionTime <- function(x, method = 'trellis'){
if (method == 'trellis'){
maxtimes <- x |>
dplyr::group_by(.data$rawfile, .data$MSOrder) |>
dplyr::summarise(maxima = max(.data$IonInjectionTime))
ggplot2::ggplot(x, ggplot2::aes(x = .data$StartTime,
y = .data$IonInjectionTime)) +
ggplot2::geom_hline(data = maxtimes,
ggplot2::aes(yintercept = .data$maxima),
colour = "red3", linetype = "longdash") +
ggplot2::geom_point(shape = ".") +
ggplot2::geom_line(stat = "smooth", method = "gam",
formula = y ~ s(x, bs= "cs"),
colour = "deepskyblue3", se = FALSE) +
ggplot2::facet_grid(rawfile ~ MSOrder, scales = "free") +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::labs(subtitle = "Plotting injection time against retention time for MS and MSn level") +
ggplot2::labs(x = "Retentione Time [min]", y = "Injection Time [ms]") -> gp
}else if (method == 'violin'){
ggplot2::ggplot(x, ggplot2::aes(x = .data$rawfile,
y = .data$IonInjectionTime)) +
ggplot2::geom_violin() +
ggplot2::facet_grid(MSOrder ~ .) +
ggplot2::labs(subtitle = "Plotting retention time resolved injection time density for each mass spectrometry run") +
ggplot2::labs(x = "rawfile", y = "Injection Time [ms]") +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else if(method == 'overlay'){
ggplot2::ggplot(x, ggplot2::aes(x = .data$StartTime,
y = .data$IonInjectionTime, colour = .data$rawfile)) +
ggplot2::geom_point(size = 0.5, alpha = 0.1) +
ggplot2::geom_line(ggplot2::aes(group = .data$rawfile, colour = .data$rawfile),
stat = "smooth",
method = "gam",
formula = y ~ s(x, bs= "cs"),
se = FALSE) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::facet_grid(~ .data$MSOrder, scales = "free") +
ggplot2::labs(subtitle = "Plotting injection time against retention time for MS and MSn level") +
ggplot2::labs(x = "Retentione Time [min]", y = "Injection Time [ms]") +
ggplot2::theme(legend.position = "top") -> gp
}else{NULL}
gp + ggplot2::labs(title = "Injection time plot") +
ggplot2::theme_light()
}
#' mZ Distribution Plot of Ms2 Scans
#'
#' draws precursor mass vs retention time for each MS2 scan in the raw file.
#'
#' @inherit plotLockMassCorrection params return references author
#' @aliases PlotMzDistribution
#' @importFrom rlang .data
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() -> S
#' plotMzDistribution(S)
#' @export
#' @aliases PlotMzDistribution
plotMzDistribution <- function(x, method='trellis'){
x |>
dplyr::filter(.data$MSOrder == "Ms2") -> xx
if (method == 'trellis'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime,
y = .data$precursorMass)) +
ggplot2::geom_point(shape = ".") +
ggplot2::facet_grid(rawfile ~ ., scales = "free") +
ggplot2::geom_line(stat = "smooth", method = "gam",
formula = y ~ s(x, bs= "cs"),
linewidth = 1.1, alpha = 0.6,
colour = "cornflowerblue", se = FALSE) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting retention time against m/z value of all selected precursors") +
ggplot2::labs(x = "Retention Time", y = "Presursor m/z value") -> gp
}else if (method == 'violin'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$rawfile,
y = .data$precursorMass)) +
ggplot2::geom_violin() +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the precursor m/z value density of all mass spectrometry runs") +
ggplot2::labs(x = "Filename", y = "Presursor m/z value [Da]") +
ggplot2::theme_light() +
ggplot2::theme(
axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else if (method == 'overlay'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime,
y = .data$precursorMass,
colour = .data$rawfile)) +
ggplot2::geom_point(size = 0.5, alpha = 0.3) +
ggplot2::geom_line(stat = "smooth",
method = "gam",
formula = y ~ s(x, bs= "cs"),
size = 1.1,
se = FALSE) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting retention time against m/z value of all selected precursors") +
ggplot2::labs(x = "Retention Time [min]", y = "Presursor m/z value [Da]") +
ggplot2::theme(legend.position="top") -> gp
}else{NULL}
gp +
ggplot2::theme_light() +
ggplot2::labs(title = "Retention Time to m/z correlation plot")
}
#' Mass Distribution Plot
#'
#' displays charge state resolved frequency of precursor masses.
#'
#' @description plots the mass frequency in dependency to the charge state
#' @inherit plotLockMassCorrection params return references author
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() |> rawDiag::plotMassDistribution('overlay')
#' @export
#' @aliases PlotMassDistribution
plotMassDistribution <- function(x, method = 'trellis'){
x[x['MSOrder'] == 'Ms2', c('MSOrder', 'charge','rawfile', 'precursorMass')] -> xx
xx$deconv <- round((xx$precursorMass - 1.00782) * xx$charge, 0)
xx$charge <- factor(xx$charge)
if (method == 'trellis'){
xx |>
ggplot2::ggplot(ggplot2::aes(x = .data$deconv, fill = .data$charge, colour = .data$charge)) +
ggplot2::geom_histogram(binwidth = 50, alpha = 0.3, position = "identity") +
ggplot2::labs(title = "Precursor mass to charge frequency plot ") +
ggplot2::labs(subtitle = "Plotting charge state resolved frequency of precursor masses") +
ggplot2::labs(x = "Precursor neutral mass [Da]", y = "Frequency [counts]") +
ggplot2::labs(fill = "Charge State", colour = "Charge State") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::coord_cartesian(xlim = range(xx$deconv)) +
ggplot2::facet_wrap(~ .data$rawfile) -> gp
}else if (method == 'violin'){ #mz.frequency.violin
xx |>
ggplot2::ggplot(ggplot2::aes(x = .data$charge, y = .data$deconv, fill = .data$rawfile)) +
ggplot2::geom_violin() +
ggplot2::labs(title = "Precursor mass to charge density plot ") +
ggplot2::labs(subtitle = "Plotting the charge state resolved precursor masse density for each mass spectrometry run") +
ggplot2::labs(x = "Charge State ", y = "Neutral Mass [Da]") +
ggplot2::theme(legend.position = "top") -> gp
}else if (method == 'overlay'){ #mz.frequency.overlay
xx |>
ggplot2::ggplot(ggplot2::aes(x = .data$deconv, colour = .data$rawfile)) +
ggplot2::geom_line(stat = "density") +
ggplot2::labs(title = "Precursor mass density plot ") +
ggplot2::labs(subtitle = "Plotting the precursor masse density for each mass spectrometry run") +
ggplot2::labs(x = "Precursor mass [neutral mass]", y = "Density") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(12)) +
ggplot2::coord_cartesian(xlim = range(xx$deconv)) +
ggplot2::theme(legend.position = "top") -> gp
}else{NULL}
gp +
ggplot2::theme_light()
}
#' Charge State Overview Plot
#'
#' graphs the number of occurrences of all selected precursor charge states.
#'
#' @inherit plotLockMassCorrection params return references author
#' @importFrom rlang .data
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() -> S
#'
#' S|>plotLockMassCorrection()
#' @export
#' @aliases PlotChargeState
plotChargeState <- function(x, method='trellis'){
x |>
dplyr::filter(.data$MSOrder == "Ms2") |>
dplyr::group_by_at(dplyr::vars(.data$rawfile)) |>
dplyr::count(.data$charge) |>
dplyr::ungroup() |>
dplyr::rename_at(dplyr::vars("n"), list(~ as.character("Counts"))) -> xx
xx$percentage <- (100 / sum(xx$Counts)) * xx$Counts
xbreaks <- unique(xx$charge)
if (method == 'trellis'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$charge, y = .data$percentage)) +
ggplot2::geom_bar(stat = "identity", fill = "cornflowerblue") +
ggplot2::geom_text(ggplot2::aes(label = .data$Counts),
vjust=-0.3, size=3.5) +
ggplot2::scale_x_continuous(breaks = xbreaks) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(15),
expand = c(0, 0),
limits = c(0, (max(xx$percentage)) + 3)) +
ggplot2::labs(subtitle = "Plotting the number of occurrences of all selected precursor charge states") +
ggplot2::labs(x = "Charge States", y = "Percent [%]") +
ggplot2::facet_wrap(~ rawfile) -> gp
}else if(method =='overlay'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$charge, y = .data$percentage,
fill = .data$rawfile)) +
ggplot2::geom_bar(stat = "identity", position = ggplot2::position_dodge(),
colour = "black") +
ggplot2::scale_x_continuous(breaks = xbreaks) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(15),
expand = c(0, 0),
limits = c(0, (max(xx$percentage)) + 3)) +
ggplot2::labs(subtitle = "Plotting the number of occurrences of all selected precursor charge states") +
ggplot2::labs(x = "Charge States", y = "Percent [%]") +
ggplot2::theme(legend.position = "top") -> gp
}else if (method =='violin'){
x |>
dplyr::filter(.data$MSOrder == "Ms2") -> xx
ggplot2::ggplot(xx, ggplot2::aes(x = .data$rawfile, y = .data$charge)) +
ggplot2::geom_violin() +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the precursor charge state density for each mass spectrometry run") +
ggplot2::labs(x = "Filename", y = "Charge State") +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else{NULL}
gp +
ggplot2::theme_light() +
ggplot2::labs(title = "Charge state plot")
}
.mapType <- function(x){
x |>
dplyr::mutate(Type = dplyr::case_when(grepl("FTMS [[:punct:]] c NSI Full ms", .data$scanType) == "TRUE" ~ "FT_Full_ms_c",
grepl("FTMS [[:punct:]] p NSI Full ms", .data$scanType) == "TRUE" ~ "FT_Full_ms_p",
grepl("FTMS [[:punct:]] c NSI Full lock ms", .data$scanType) == "TRUE" ~ "FT_Full_ms_c",
grepl("FTMS [[:punct:]] p NSI Full lock ms", .data$scanType) == "TRUE" ~ "FT_Full_ms_p",
grepl("FTMS [[:punct:]] c NSI d Full ms2", .data$scanType) == "TRUE" ~ "FT_d_Full_ms2_c",
grepl("FTMS [[:punct:]] p NSI d Full ms2", .data$scanType) == "TRUE" ~ "FT_d_Full_ms2_p",
grepl("FTMS [[:punct:]] c NSI SIM ms", .data$scanType) == "TRUE" ~ "FT_SIM_ms_c",
grepl("FTMS [[:punct:]] p NSI SIM ms", .data$scanType) == "TRUE" ~ "FT_SIM_ms_p",
grepl("FTMS [[:punct:]] p NSI SIM msx ms", .data$scanType) == "TRUE" ~ "FT_msxSIM_ms_p",
grepl("FTMS [[:punct:]] c NSI SIM msx ms", .data$scanType) == "TRUE" ~ "FT_msxSIM_ms_c",
grepl("FTMS [[:punct:]] c NSI Full ms2", .data$scanType) == "TRUE" ~ "FT_Full_ms2_c",
grepl("FTMS [[:punct:]] p NSI Full ms2", .data$scanType) == "TRUE" ~ "FT_Full_ms2_p",
grepl("ITMS [[:punct:]] c NSI r d Full ms2", .data$scanType) == "TRUE" ~ "IT_Full_ms2_c",
grepl("ITMS [[:punct:]] p NSI r d Full ms2", .data$scanType) == "TRUE" ~ "IT_Full_ms2_p"
)
)
}
#TODO: supply cases as list
#patterns <- list(
#x %% 35 == 0 ~ "fizz buzz",
#x %% 5 == 0 ~ "fizz",
#x %% 7 == 0 ~ "buzz",
#TRUE ~ as.character(x)
#)
#case_when(!!! patterns)
.calcTransient <- function(x){
x$MassAnalyzer <- NA
x$MassAnalyzer[grepl(x$scanType, pattern="^ITMS")] <- "ITMS"
x$MassAnalyzer[grepl(x$scanType, pattern="^FTMS")] <- "FTMS"
x |>
dplyr::mutate(transient = dplyr::case_when(FTResolution == 7500 ~ 16,
FTResolution == 15000 ~ 32,
FTResolution == 17500 ~ 64,
FTResolution == 30000 ~ 64,
FTResolution == 45000 ~ 96,
FTResolution == 50000 ~ 96,
FTResolution == 35000 ~ 128,
FTResolution == 60000 ~ 128,
FTResolution == 70000 ~ 256,
FTResolution == 120000 ~ 256,
FTResolution == 140000 ~ 512,
FTResolution == 240000 ~ 512
)
)
}
#' Scan Event Plot
#'
#' Plotting the elapsed scan time for each individual scan event.
#'
#' @inherit plotLockMassCorrection params return references author
#' @importFrom rlang .data
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() -> S
#'
#' S|> plotScanTime()
#' @export
#' @aliases PlotScanTime
plotScanTime <- function(x, method='trellis'){
x |>
.calcTransient() |>
dplyr::mutate(ElapsedScanTimesec = .data$ElapsedScanTimesec * 1000) |>
dplyr::select_at(dplyr::vars("StartTime", "scanType", "ElapsedScanTimesec", "rawfile", "MassAnalyzer", "MSOrder", "transient")) |>
na.omit() |>
.mapType()-> xx
if(method == 'trellis'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime, y = .data$ElapsedScanTimesec)) +
ggplot2::geom_point(shape = ".") +
ggplot2::facet_grid(rawfile ~ Type) +
ggplot2::geom_line(stat = "smooth", method = "gam",
formula = y ~ s(x), colour = "deepskyblue3", se = FALSE) +
ggplot2::labs(subtitle = "Plotting the elapsed scan time for each individual scan") +
ggplot2::labs(x = "Retentione Time [min]", y = "Elapsed Scan Time [ms]") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::geom_hline(data = xx, ggplot2::aes(yintercept = .data$transient), colour = "red3") -> gp
}else if (method == 'violin'){
xx |>
dplyr::mutate_at(dplyr::vars("ElapsedScanTimesec"), list(~ .*1000)) |>
dplyr::select_at(dplyr::vars("ElapsedScanTimesec", "rawfile", "MassAnalyzer", "MSOrder")) |>
na.omit() -> xxx
ggplot2::ggplot(xxx, ggplot2::aes(x = .data$rawfile, y = .data$ElapsedScanTimesec)) +
ggplot2::geom_violin() +
ggplot2::facet_grid(MSOrder + MassAnalyzer ~ ., scales = "free") +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the retention time resolved elapsed scan time density for each mass spectrometry run") +
ggplot2::labs(x = "rawfile", y = "Elapsed Scan Time [ms]") +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else if (method == 'overlay'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime, y = .data$ElapsedScanTimesec, colour = .data$rawfile)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::geom_line(ggplot2::aes(group = "rawfile", colour = "rawfile"), stat = "smooth", method = "gam", formula = y ~ s(x, bs= "cs"), se = FALSE) +
ggplot2::facet_grid(~ MSOrder + MassAnalyzer, scales = "free") +
ggplot2::labs(subtitle = "Plotting the elapsed scan time for each individual scan") +
ggplot2::labs(x = "Retentione Time [min]", y = "Elapsed Scan Time [ms]") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks((n = 8))) +
ggplot2::theme(legend.position="top") -> gp
}else{NULL}
gp +
ggplot2::theme_light() +
ggplot2::labs(title = "Scan time plot")
}
#' Fill NA values with last previous value
#'
#' @param x a vector of values
#' @author Christian Trachsel
#' @return a vector with any NA values replaced with the last previous actuall value
#'
#' @examples
#' c(NA, 1, 2, 3, NA, 4, 5, NA, NA, NA, 6) |>
#' rawDiag:::.fillNAgaps()
.fillNAgaps <- function(x) {
goodVals <- c(NA, x[!is.na(x)])
fillIdx <- cumsum(!is.na(x))+1
res <- goodVals[fillIdx]
return(res)
}
#' Calculate Master Scan Number
#'
#' calculates the MS1 master scan number of an MS2 scan
#' and populates the MasterScanNumber with it
#'
#' @return a \code{data.frame} containing a \code{MasterScanNumber} column.
#' @inheritParams plotLockMassCorrection
#' @importFrom rlang .data
#'
#' @author Christian Trachsel
.calculatioMasterScan <- function(x){
x |>
dplyr::mutate(MasterScanNumber = dplyr::case_when(.data$MSOrder == "Ms" ~ .data$scan)) |>
dplyr::mutate(MasterScanNumber = .fillNAgaps(.data$MasterScanNumber)) |>
dplyr::mutate(MasterScanNumber = replace(.data$MasterScanNumber, .data$scan == .data$MasterScanNumber, NA)) |>
dplyr::pull(.data$MasterScanNumber) -> x$MasterScanNumber
x
}
#' Cycle Load Plot
#'
#' plotting the number of MS2 per MS1 (the duty cycle) scan versus retention
#' time. The deepskyblue colored loess curve shows the trend.
#'
#' @inherit plotLockMassCorrection params return references author
#' @examples
#' rawrr::sampleFilePath() |> rawDiag::readRaw() -> S
#'
#' S|> plotCycleLoad()
#' @export
#' @aliases PlotCycleLoad
plotCycleLoad <- function(x, method = 'trellis'){
x |>
.calculatioMasterScan() |>
dplyr::filter(.data$MSOrder == "Ms2") |>
dplyr::group_by_at(dplyr::vars("rawfile")) |>
dplyr::count(.data$MasterScanNumber) |>
dplyr::rename(scan = .data$MasterScanNumber) -> MS2
x [, c('StartTime', 'scan')] -> MS
xx <- dplyr::inner_join(MS, MS2, by = "scan")
if (method == 'trellis'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime, y = .data$n)) +
ggplot2::geom_point(shape = ".") +
ggplot2::geom_line(stat = "smooth",
method = "loess",
span = 0.2,
colour = "deepskyblue3",
se = FALSE) +
ggplot2::facet_wrap(~ rawfile) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the number of MS2 per MS1 scan versus retention time. The deepskyblue colored loess curve shows the trend.") -> gp
}else if(method == 'violin'){
xx <- dplyr::inner_join(MS, MS2, by = "scan") |>
dplyr::mutate_at(dplyr::vars("rawfile"), list(~ as.factor(.))) |>
dplyr::mutate_at(dplyr::vars("n"), list(~ as.numeric(.)))
ggplot2::ggplot(xx, ggplot2::aes(x = .data$rawfile, y = .data$n)) +
ggplot2::geom_violin() +
ggplot2::labs(subtitle = "Plotting the duty cycle resolved MS2 density for each mass spectrometry run") +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90)) -> gp
}else if (method == 'overlay'){
ggplot2::ggplot(xx, ggplot2::aes(x = .data$StartTime,
y = .data$n,
colour = .data$rawfile)) +
ggplot2::geom_point(shape = ".") +
ggplot2::geom_line(ggplot2::aes(group = .data$rawfile,
colour = .data$rawfile),
stat = "smooth", method = "loess", span = 0.2,
se = FALSE) +
ggplot2::labs(title = "Time resolved number of MS2 scans") +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::labs(subtitle = "Plotting the number of MS2 per MS1 scan versus retention time") +
ggplot2::theme(legend.position = "top") -> gp
}else{NULL}
gp +
ggplot2::labs(title = "Time resolved number of MS2 scans") +
ggplot2::labs(x = "Retention Time [min]", y = "Number of MS2 per MS1 [counts]") +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(8)) +
ggplot2::coord_cartesian(ylim = c(0, max(xx$n) + 1)) +
ggplot2::theme_light()
}
#' Checks Bioconductor installation instructions
#' @importFrom rawrr installRawFileReaderDLLs installRawrrExe
#' @importFrom BiocManager install
#' @return TRUE if everything is installed correctly
#' @export
checkRawrr <- function(){
if (isFALSE(requireNamespace("BiocManager", quietly = TRUE)))
stop("exec", "install.packages('BiocManager')")
if (isFALSE(requireNamespace("rawrr", quietly = TRUE)))
stop("exec", "BiocManager::install('rawrr')")
if (isFALSE(rawrr:::.checkRawFileReaderDLLs()))
rawrr::installRawFileReaderDLLs()
if (isFALSE(file.exists(rawrr:::.rawrrAssembly())))
rawrr::installRawrrExe()
if (isFALSE(rawrr:::.isAssemblyWorking()))
stop("rawrr assembly is not working. check if mono if available.")
TRUE
}
#' @keywords internal
"_PACKAGE"
## usethis namespace: start
## usethis namespace: end
NULL
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