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R/dataProcessPlotsTMT.R
In MSstatsTMT: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling
Defines functions
dataProcessPlotsTMT
Documented in
dataProcessPlotsTMT
#' Visualization for explanatory data analysis - TMT experiment
#'
#' To illustrate the quantitative data and quality control of MS runs,
#' dataProcessPlotsTMT takes the quantitative data from converter functions (\code{\link{PDtoMSstatsTMTFormat}}, \code{\link{MaxQtoMSstatsTMTFormat}}, \code{\link{SpectroMinetoMSstatsTMTFormat}}) as input
#' and generate two types of figures in pdf files as output :
#' (1) profile plot (specify "ProfilePlot" in option type), to identify the potential sources of variation for each protein;
#' (2) quality control plot (specify "QCPlot" in option type), to evaluate the systematic bias between MS runs.
#'
#' @export
#' @import ggplot2
#' @importFrom graphics axis image legend mtext par plot.new title plot
#' @importFrom grDevices dev.off hcl pdf
#' @importFrom dplyr mutate
#' @importFrom reshape2 dcast
#' @param data.peptide name of the data with peptide level, which can be the output of converter functions(\code{\link{PDtoMSstatsTMTFormat}}, \code{\link{MaxQtoMSstatsTMTFormat}}, \code{\link{SpectroMinetoMSstatsTMTFormat}}).
#' @param data.summarization name of the data with protein-level, which can be the output of \code{\link{proteinSummarization}} function.
#' @param type choice of visualization. "ProfilePlot" represents profile plot of log intensities across MS runs.
#' "QCPlot" represents box plots of log intensities across channels and MS runs.
#' @param ylimUp upper limit for y-axis in the log scale.
#' FALSE(Default) for Profile Plot and QC Plot uses the upper limit as rounded off maximum of log2(intensities) after normalization + 3..
#' @param ylimDown lower limit for y-axis in the log scale. FALSE(Default) for Profile Plot and QC Plot uses 0..
#' @param x.axis.size size of x-axis labeling for "Run" and "channel in Profile Plot and QC Plot.
#' @param y.axis.size size of y-axis labels. Default is 10.
#' @param text.size size of labels represented each condition at the top of Profile plot and QC plot. Default is 4.
#' @param text.angle angle of labels represented each condition at the top of Profile plot and QC plot. Default is 0.
#' @param legend.size size of legend above Profile plot. Default is 7.
#' @param dot.size.profile size of dots in Profile plot. Default is 2.
#' @param ncol.guide number of columns for legends at the top of plot. Default is 5.
#' @param width width of the saved pdf file. Default is 10.
#' @param height height of the saved pdf file. Default is 10.
#' @param which.Protein Protein list to draw plots. List can be names of Proteins or order numbers of Proteins.
#' Default is "all", which generates all plots for each protein. For QC plot, "allonly" will generate one QC plot with all proteins.
#' @param originalPlot TRUE(default) draws original profile plots, without normalization.
#' @param summaryPlot TRUE(default) draws profile plots with protein summarization for each channel and MS run.
#' @param address the name of folder that will store the results. Default folder is the current working directory.
#' The other assigned folder has to be existed under the current working directory.
#' An output pdf file is automatically created with the default name of "ProfilePlot.pdf" or "QCplot.pdf".
#' The command address can help to specify where to store the file as well as how to modify the beginning of the file name.
#' If address=FALSE, plot will be not saved as pdf file but showed in window.
#' @return plot or pdf
#' @examples
#' data(input.pd)
#' quant.msstats <- proteinSummarization(input.pd,
#' method="msstats",
#' global_norm=TRUE,
#' reference_norm=TRUE)
#'
#' ## Profile plot
#' dataProcessPlotsTMT(data.peptide=input.pd,
#' data.summarization=quant.msstats,
#' type='ProfilePlot',
#' width = 21,
#' height = 7)
#'
#' ## NottoRun: QC plot
#' # dataProcessPlotsTMT(data.peptide=input.pd,
#' # data.summarization=quant.msstats,
#' # type='QCPlot',
#' # width = 21,
#' # height = 7)
dataProcessPlotsTMT <- function(data.peptide,
data.summarization,
type,
ylimUp = FALSE,
ylimDown = FALSE,
x.axis.size = 10,
y.axis.size = 10,
text.size = 4,
text.angle = 90,
legend.size = 7,
dot.size.profile = 2,
ncol.guide = 5,
width = 10,
height = 10,
which.Protein = "all",
originalPlot = TRUE,
summaryPlot = TRUE,
address = "") {
Condition = Run = xorder = Channel = NULL
PeptideSequence = PSM = NULL
groupAxis = cumGroupAxis = abundance = analysis = NULL
datafeature <- data.peptide
datarun <- data.summarization
# conditions in feature data
fea.conds <- as.character(unique(datafeature$Condition))
# conditions in protein data
run.conds <- as.character(unique(datarun$Condition))
# only keep the overlapped conditions between feature data and protein data
shared.conds <- intersect(fea.conds, run.conds)
datafeature <- datafeature[datafeature$Condition %in% shared.conds,]
datarun <- datarun[datarun$Condition %in% shared.conds,]
# make sure condition is factor
datafeature$Condition <- factor(datafeature$Condition)
datarun$Condition <- factor(datarun$Condition)
colnames(datafeature)[colnames(datafeature) == 'ProteinName'] <- 'Protein'
datafeature$Protein <- factor(datafeature$Protein)
datarun$Protein <- factor(datarun$Protein)
## feature level data : log2 transform
datafeature$abundance <- log2(datafeature$Intensity)
datafeature[!is.na(datafeature$Intensity) &
datafeature$Intensity < 1, 'abundance'] <- 0
if (length(setdiff(toupper(type), c(toupper("ProfilePlot"), toupper("QCPlot")))) != 0) {
stop(paste0("Input for type=", type,
". However,'type' should be one of \"ProfilePlot\", \"QCPlot\"."))
}
if (address == FALSE){
## here I used == FALSE, instead of !address. Because address can be logical or characters.
if (which.Protein == 'all') {
stop('** Cannnot generate all plots in a screen. Please set one protein at a time.')
} else if (length(which.Protein) > 1) {
stop('** Cannnot generate multiple plots in a screen. Please set one protein at a time.')
}
}
## Profile plot ##
## ---------------
if (toupper(type) == "PROFILEPLOT") {
## choose Proteins or not
if (which.Protein != "all") {
## check which.Protein is name of Protein
if (is.character(which.Protein)) {
temp.name <- which.Protein
## message if name of Protein is wrong.
if (length(setdiff(temp.name,unique(datafeature$Protein))) > 0) {
stop(paste0("Please check protein name. Data set does not have this protein. - ",
toString(temp.name)))
}
}
## check which.Protein is order number of Protein
if (is.numeric(which.Protein)) {
temp.name <- levels(datafeature$Protein)[which.Protein]
## message if name of Protein is wrong.
if (length(levels(datafeature$Protein)) < max(which.Protein)) {
stop(paste0("Please check your ion of proteins. There are ",
length(levels(datafeature$Protein))," proteins in this dataset."))
}
}
## use only assigned proteins
datafeature <- datafeature[which(datafeature$Protein %in% temp.name), ]
datafeature$Protein <- factor(datafeature$Protein)
datarun <- datarun[which(datarun$Protein %in% temp.name), ]
datarun$Protein <- factor(datarun$Protein)
}
## assign upper or lower limit
y.limup <- ceiling(max(datafeature$abundance, na.rm = TRUE) + 3)
if (is.numeric(ylimUp)) {
y.limup <- ylimUp
}
y.limdown <- 0
if (is.numeric(ylimDown)) {
y.limdown <- ylimDown
}
datafeature <- datafeature[with(datafeature, order(Run, Condition, Channel)), ]
datafeature$Run <- factor(datafeature$Run)
datarun$Run <- factor(datarun$Run)
## !! important: order of x-axis
## can be reorder by group and then channel, WITHIN Run
## first make new column for x-axis
datafeature$group.channel <- paste(datafeature$Condition, datafeature$Channel, sep = "_")
## not sure better way for coding
## potentially change it.
datafeature$xorder <- NA
for (k in seq_along(unique(datafeature$Run))) {
runid <- unique(datafeature$Run)[k]
datafeature[datafeature$Run == runid, ]$xorder <- factor(datafeature[datafeature$Run == runid, ]$group.channel,
levels <- unique(datafeature[datafeature$Run == runid, ]$group.channel),
labels <- seq(1, length(unique(datafeature[datafeature$Run == runid, ]$group.channel))))
}
## check
## unique(datafeature[datafeature$Run == '5', c('Channel', 'Condition', 'Run', 'xorder','group.channel')])
## need to make data.frame with same variables for condition name
datafeature$xorder <- as.numeric(datafeature$xorder)
## keep unique information for x-axis labeling. will be used in plotting
tempGroupName <- unique(datafeature[, c("Condition", "xorder", "Run", "Channel")])
## count # per condition per Run
#groupline <- unique(datafeature[, c('Condition', 'Run')])
#groupline$groupAxis <- as.numeric(xtabs(~Condition+Run, tempGroupName))
groupline <- tempGroupName %>% dplyr::group_by(Condition, Run) %>% dplyr::mutate(groupAxis = n())
groupline <- groupline %>% dplyr::select(-xorder, -Channel)
groupline <- groupline[!duplicated(groupline), ]
## make accumurated # as condition increase
groupline <- groupline %>% dplyr::group_by(Run) %>% dplyr::mutate(cumGroupAxis = cumsum(groupAxis))
groupline$cumGroupAxis <- groupline$cumGroupAxis + 0.5
## add coordinate for group id
groupline$xorder <- groupline$cumGroupAxis - groupline$groupAxis / 2
groupline$abundance <- y.limup - 0.5
## save all information, for labeling group in plot
groupline.all <- groupline
## remove last condition for vertical line between groups
groupline <- groupline[-which(groupline$Condition %in% levels(groupline$Condition)[nlevels(groupline$Condition)]), ]
## need to fill in incomplete rows for Runlevel data
haverun <- FALSE
if (sum(is.element(colnames(datarun), "Run")) != 0) {
datamat <- dcast(Protein + Channel ~ Run, data = datarun, value.var = 'Abundance', keep = TRUE)
datarun <- melt(datamat, id.vars=c('Protein', 'Channel'))
colnames(datarun)[colnames(datarun) %in% c("variable", "value")] <- c('Run', 'Abundance')
## match x axis order
datarun <- merge(datarun, tempGroupName, by = c('Run', 'Channel'))
haverun <- TRUE
}
## save the plots as pdf or not
## If there are the file with the same name, add next numbering at the end of file name
## y-axis labeling
yaxis.name <- 'Log2-intensities'
if (originalPlot) {
if (address != FALSE) {
allfiles <- list.files()
num <- 0
filenaming <- paste0(address, "ProfilePlot")
finalfile <- paste0(address, "ProfilePlot.pdf")
while (is.element(finalfile, allfiles)) {
num <- num + 1
finalfile <- paste0(paste(filenaming, num, sep = "-"), ".pdf")
}
pdf(finalfile, width = width, height = height)
}
## factoring for run, channel, condition should be done before loop
for (i in seq_len(nlevels(datafeature$Protein))) {
sub <- datafeature[datafeature$Protein == levels(datafeature$Protein)[i], ]
sub$PeptideSequence <- factor(as.character(sub$PeptideSequence))
sub$Charge <- factor(as.character(sub$Charge))
sub$PSM <- factor(as.character(sub$PSM))
## if all measurements are NA,
if (nrow(sub) == sum(is.na(sub$abundance))) {
message(paste0("Can't the Profile plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)),
") because all measurements are NAs."))
next()
}
if (nrow(sub) == sum(!is.na(sub$abundance) & sub$abundance == 0)) {
message(paste0("Can't the Profile plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)),
") because all measurements are zeros."))
next()
}
## seq for peptide and charge
## for seting up color and linetype
b <- unique(sub[, c("PeptideSequence", "PSM")])
b <- b[with(b, order(PeptideSequence, PSM)), ] ## add because if there are missing value, orders are different.
temp1 <- xtabs(~PeptideSequence, b)
## unique charge id within peptide sequence, for line type
ss <- NULL
## unique peptide sequence id, for color
s <- NULL
for (j in seq_along(temp1)) {
temp3 <- rep(j, temp1[j])
s <- c(s, temp3)
temp2 <- seq(1, temp1[j])
ss <- c(ss, temp2)
}
## for annotation of condition
groupline.tmp <- data.frame(groupline,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1])
groupline.all.tmp <- data.frame(groupline.all,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1])
## 2019. 12. 17, MC : for profile plot, define color for dot
cbp <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
check.length <- length(unique(s)) %/% length(cbp)
if ( check.length > 0 ){
cbp <- rep(cbp, times=check.length + 1)
}
##
## 1st plot for original plot
ptemp <- ggplot(aes_string(x = 'xorder', y = 'abundance',
color = 'PSM', linetype = 'PSM'), data = sub) +
facet_grid(~Run) +
geom_point(size=dot.size.profile) +
geom_line(size = 0.5) +
scale_colour_manual(values=cbp[s]) +
scale_linetype_manual(values = ss) +
scale_shape_manual(values = c(16)) +
labs(title = unique(sub$Protein),
x = 'MS runs') +
scale_y_continuous(yaxis.name, limits = c(y.limdown, y.limup)) +
scale_x_continuous('MS runs') +
geom_vline(data = groupline.tmp,
aes(xintercept = cumGroupAxis),
colour = "grey", linetype = "longdash") +
geom_text(data = groupline.all.tmp,
aes(x = xorder, y = abundance, label = Condition),
size = text.size,
angle = text.angle,
color = "black") +
theme(
panel.background = element_rect(fill = 'white', colour = "black"),
legend.key = element_rect(fill = 'white', colour = 'white'),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'gray95'),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size = y.axis.size, colour = "black"),
axis.ticks = element_line(colour = "black"),
axis.title.x = element_text(size = x.axis.size + 5, vjust = -0.4),
axis.title.y = element_text(size = y.axis.size + 5, vjust = 0.3),
title = element_text(size = x.axis.size + 8, vjust = 1.5),
legend.position = "top",
legend.text = element_text(size = legend.size)) +
guides(color = guide_legend(title = paste("# peptide:", nlevels(sub$PeptideSequence)),
title.theme = element_text(size = 13, angle = 0),
keywidth = 0.4,
keyheight = 0.1,
default.unit = 'inch',
ncol = ncol.guide),
linetype = guide_legend(title = paste("# peptide:", nlevels(sub$PeptideSequence)),
title.theme = element_text(size = 13, angle = 0),
keywidth = 0.4,
keyheight = 0.1,
default.unit = 'inch',
ncol = ncol.guide))
print(ptemp)
message(paste("Drew the Profile plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)), ")"))
}
# end-loop for each protein
if (address != FALSE) {
dev.off()
}
} # end original plot
############################################
## 2st plot for original plot : summary
############################################
if (summaryPlot) {
if (address != FALSE) {
allfiles <- list.files()
num <- 0
filenaming <- paste0(address, "ProfilePlot_wSummarization")
finalfile <- paste0(address, "ProfilePlot_wSummarization.pdf")
while (is.element(finalfile, allfiles)) {
num <- num + 1
finalfile <- paste0(paste(filenaming, num, sep = "-"), ".pdf")
}
pdf(finalfile, width = width, height = height)
}
for (i in seq_len(nlevels(datafeature$Protein))) {
sub <- datafeature[datafeature$Protein == levels(datafeature$Protein)[i], ]
sub$PeptideSequence <- factor(as.character(sub$PeptideSequence))
sub$Charge <- factor(as.character(sub$Charge))
sub$PSM <- factor(as.character(sub$PSM))
## if all measurements are NA,
if (nrow(sub) == sum(is.na(sub$abundance))) {
message(paste0("Can't the Profile plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)),
") because all measurements are NAs."))
next()
}
if (nrow(sub) == sum(!is.na(sub$abundance) & sub$abundance == 0)) {
message(paste0("Can't the Profile plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)),
") because all measurements are zeros."))
next()
}
## for annotation of condition
groupline.tmp <- data.frame(groupline,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1],
"analysis" = 'Run summary')
groupline.all.tmp <- data.frame(groupline.all,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1],
"analysis" = 'Run summary')
if (haverun) {
subrun <- datarun[datarun$Protein == levels(datafeature$Protein)[i], ]
if (nrow(subrun) != 0) {
quantrun <- sub[1, ]
quantrun[, 2:ncol(quantrun)] <- NA
quantrun <- quantrun[rep(seq_len(nrow(subrun))), ]
quantrun$Protein <- subrun$Protein
quantrun$PeptideSequence <- "Run summary"
quantrun$Charge <- "Run summary"
quantrun$PSM <- "Run summary"
quantrun$Channel <- subrun$Channel
quantrun$Run <- subrun$Run
quantrun$abundance <- subrun$Abundance
quantrun$xorder <- subrun$xorder
} else {
# if there is only one Run measured across all runs, no Run information for linear with censored
quantrun <- datafeature[1, ]
quantrun[, 2:ncol(quantrun)] <- NA
quantrun$Protein <- levels(datafeature$Protein)[i]
quantrun$PeptideSequence <- "Run summary"
quantrun$Charge <- "Run summary"
quantrun$PSM <- "Run summary"
quantrun$abundance <- NA
quantrun$Intensity <- NA
}
quantrun$analysis <- "Run summary"
sub$analysis <- "Processed feature-level data"
final <- rbind(sub, quantrun)
final$analysis <- factor(final$analysis)
final$PSM <- factor(final$PSM)
ptempall <- ggplot(aes_string(x = 'xorder', y = 'abundance',
color = 'analysis', linetype = 'PSM', size = 'analysis'), data = final) +
facet_grid(~Run) +
geom_point(size = dot.size.profile) +
geom_line(size = 0.5) +
scale_colour_manual(values = c("lightgray", "darkred")) +
scale_shape_manual(values = c(16)) +
scale_size_manual(values = c(1.7, 2), guide = "none") +
scale_linetype_manual(values = c(rep(1, times = length(unique(final$PSM))-1), 2), guide = "none") +
labs(title = unique(sub$Protein),
x = 'MS runs') +
scale_y_continuous(yaxis.name, limits = c(y.limdown, y.limup)) +
geom_vline(data = groupline.tmp,
aes(xintercept = cumGroupAxis),
colour = "grey", linetype = "longdash") +
geom_text(data = groupline.all.tmp,
aes(x = xorder, y = abundance, label = Condition),
size = text.size,
angle = text.angle,
color = "black") +
theme(
panel.background = element_rect(fill = 'white', colour = "black"),
legend.key = element_rect(fill = 'white', colour = 'white'),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'gray95'),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size = y.axis.size, colour = "black"),
axis.ticks = element_line(colour = "black"),
axis.title.x = element_text(size = x.axis.size + 5, vjust = -0.4),
axis.title.y = element_text(size = y.axis.size + 5, vjust = 0.3),
title = element_text(size = x.axis.size + 8, vjust = 1.5),
legend.position = "top",
legend.text = element_text(size = legend.size),
legend.title = element_blank()) +
guides(color = guide_legend(order = 1,
title = NULL,
label.theme = element_text(size = 10, angle = 0)))
## draw point again because some red summary dots could be hiden
ptempall <- ptempall + geom_point(data = final, aes(x = xorder, y = abundance, size = analysis, color = analysis))
print(ptempall)
message(paste("Drew the Profile plot with summarization for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)), ")"))
}
} # end-loop for each protein
if (address!=FALSE) {
dev.off()
}
}
} # end Profile plot
## QC plot (Quality control plot) ##
## ---------------------------------
if (toupper(type) == "QCPLOT") {
## y-axis labeling
yaxis.name <- 'Log2-intensities'
## save the plots as pdf or not
## If there are the file with the same name, add next numbering at the end of file name
if (address != FALSE) {
allfiles <- list.files()
num <- 0
filenaming <- paste0(address,"QCPlot")
finalfile <- paste0(address,"QCPlot.pdf")
while (is.element(finalfile, allfiles)) {
num <- num + 1
finalfile <- paste0(paste(filenaming, num, sep = "-"), ".pdf")
}
pdf(finalfile, width = width, height = height)
}
## assign upper or lower limit
y.limup <- ceiling(max(datafeature$abundance, na.rm = TRUE) + 3)
if (is.numeric(ylimUp)) {
y.limup <- ylimUp
}
y.limdown <- 0
if (is.numeric(ylimDown)) {
y.limdown <- ylimDown
}
datafeature <- datafeature[with(datafeature, order(Run, Condition, Channel)), ]
## !! important: order of x-axis
## can be reorder by group and then channel, WITHIN Run
## first make new column for x-axis
datafeature$group.channel <- paste(datafeature$Condition, datafeature$Channel, sep = "_")
## not sure better way for coding
## potentially change it.
datafeature$xorder <- NA
for (k in seq_along(unique(datafeature$Run))) {
runid <- unique(datafeature$Run)[k]
datafeature[datafeature$Run == runid, ]$xorder <- factor(datafeature[datafeature$Run == runid, ]$group.channel,
levels <- unique(datafeature[datafeature$Run == runid, ]$group.channel),
labels <- seq(1, length(unique(datafeature[datafeature$Run == runid, ]$group.channel))))
}
## check
## unique(datafeature[datafeature$Run == 'PAMI-176_Mouse_K-T', c('Channel', 'Condition', 'Run', 'xorder','group.channel')])
## need to make data.frame with same variables for condition name
datafeature$xorder <- as.numeric(datafeature$xorder)
## keep unique information for x-axis labeling. will be used in plotting
tempGroupName <- unique(datafeature[, c("Condition", "xorder", "Run", "Channel")])
## count # per condition per Run
#groupline <- unique(datafeature[, c('Condition', 'Run')])
#groupline$groupAxis <- as.numeric(xtabs(~Condition+Run, tempGroupName))
groupline <- tempGroupName %>% dplyr::group_by(Condition, Run) %>% dplyr::mutate(groupAxis = n())
groupline <- groupline %>% dplyr::select(-xorder, -Channel)
groupline <- groupline[!duplicated(groupline), ]
## make accumurated # as condition increase
groupline <- groupline %>% dplyr::group_by(Run) %>% dplyr::mutate(cumGroupAxis = cumsum(groupAxis))
groupline$cumGroupAxis <- groupline$cumGroupAxis + 0.5
## add coordinate for group id
groupline$xorder <- groupline$cumGroupAxis - groupline$groupAxis / 2
groupline$abundance <- y.limup - 0.5
## save all information, for labeling group in plot
groupline.all <- groupline
## remove last condition for vertical line between groups
groupline <- groupline[-which(groupline$Condition %in% levels(groupline$Condition)[nlevels(groupline$Condition)]), ]
## all protein
if (which.Protein == 'all' | which.Protein == 'allonly') {
## for annotation of condition
groupline.tmp <- data.frame(groupline,
"PSM" = unique(datafeature$PSM)[1],
"PeptideSequence" = unique(datafeature$PeptideSequence)[1])
groupline.all.tmp <- data.frame(groupline.all,
"PSM" = unique(datafeature$PSM)[1],
"PeptideSequence" = unique(datafeature$PeptideSequence)[1])
## 1st plot for original plot
## for boxplot, x-axis, xorder should be factor
datafeature$xorder <- factor(datafeature$xorder)
ptemp <- ggplot(aes_string(x = 'xorder', y = 'abundance'), data = datafeature) +
facet_grid(~Run) +
geom_boxplot(aes_string(fill = 'Condition'), outlier.shape = 1, outlier.size = 1.5) +
labs(title = 'All proteins',
x = 'MS runs') +
scale_y_continuous(yaxis.name, limits = c(y.limdown, y.limup)) +
geom_vline(data = groupline.tmp,
aes(xintercept = cumGroupAxis),
colour = "grey", linetype = "longdash") +
geom_text(data = groupline.all.tmp,
aes(x = xorder, y = abundance, label = Condition),
size = text.size,
angle = text.angle,
color = "black") +
theme(
panel.background = element_rect(fill = 'white', colour = "black"),
legend.key = element_rect(fill = 'white', colour = 'white'),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'gray95'),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size = y.axis.size, colour = "black"),
axis.ticks = element_line(colour = "black"),
axis.title.x = element_text(size = x.axis.size + 5, vjust = -0.4),
axis.title.y = element_text(size = y.axis.size + 5, vjust = 0.3),
title = element_text(size = x.axis.size + 8, vjust = 1.5),
legend.position = "none")
print(ptemp)
message("Drew the Quality Contol plot(boxplot) for all proteins.")
}
## each protein
## choose Proteins or not
if (which.Protein != 'allonly') {
if (which.Protein != "all") {
## check which.Protein is name of Protein
if (is.character(which.Protein)) {
temp.name <- which.Protein
## message if name of Protein is wrong.
if (length(setdiff(temp.name, unique(datafeature$Protein))) > 0) {
dev.off()
stop(paste0("Please check protein name. Data set does not have this protein. - ",
toString(temp.name)))
}
}
## check which.Protein is order number of Protein
if (is.numeric(which.Protein)) {
temp.name <- levels(datafeature$Protein)[which.Protein]
## message if name of Protein is wrong.
if (length(levels(datafeature$Protein)) < max(which.Protein)) {
dev.off()
stop(paste0("Please check your ion of proteins. There are ",
length(levels(datafeature$Protein)), " proteins in this dataset."))
}
}
## use only assigned proteins
datafeature <- datafeature[which(datafeature$Protein %in% temp.name), ]
datafeature$Protein <- factor(datafeature$Protein)
}
for (i in seq_len(nlevels(datafeature$Protein))) {
sub <- datafeature[datafeature$Protein == levels(datafeature$Protein)[i], ]
sub <- sub[!is.na(sub$abundance), ]
## if all measurements are NA,
if (nrow(sub) == sum(is.na(sub$abundance))) {
message(paste("Can't the Quality Control plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)),
") because all measurements are NAs."))
next()
}
## for annotation of condition
groupline.tmp <- data.frame(groupline,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1])
groupline.all.tmp <- data.frame(groupline.all,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1])
## 1st plot for original plot
## for boxplot, x-axis, xorder should be factor
sub$xorder <- factor(sub$xorder)
ptemp <- ggplot(aes_string(x = 'xorder', y = 'abundance'), data = sub) +
facet_grid(~Run) +
geom_boxplot(aes_string(fill = 'Condition'), outlier.shape = 1, outlier.size = 1.5) +
labs(title = unique(sub$Protein),
x = 'MS runs') +
scale_y_continuous(yaxis.name, limits = c(y.limdown, y.limup)) +
geom_vline(data = groupline.tmp,
aes(xintercept = cumGroupAxis),
colour = "grey", linetype = "longdash") +
geom_text(data = groupline.all.tmp,
aes(x = xorder, y = abundance, label = Condition),
size = text.size,
angle = text.angle,
color = "black") +
theme(
panel.background = element_rect(fill = 'white', colour = "black"),
legend.key = element_rect(fill = 'white', colour = 'white'),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'gray95'),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size = y.axis.size, colour = "black"),
axis.ticks = element_line(colour = "black"),
axis.title.x = element_text(size = x.axis.size + 5, vjust = -0.4),
axis.title.y = element_text(size = y.axis.size + 5, vjust = 0.3),
title = element_text(size = x.axis.size + 8, vjust = 1.5),
legend.position = "none")
print(ptemp)
message(paste("Drew the Quality Contol plot(boxplot) for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)), ")"))
} # end-loop
}
if (address != FALSE) {
dev.off()
}
} # end QC plot
}
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Defines functions dataProcessPlotsTMT
Documented in dataProcessPlotsTMT
#' Visualization for explanatory data analysis - TMT experiment
#'
#' To illustrate the quantitative data and quality control of MS runs,
#' dataProcessPlotsTMT takes the quantitative data from converter functions (\code{\link{PDtoMSstatsTMTFormat}}, \code{\link{MaxQtoMSstatsTMTFormat}}, \code{\link{SpectroMinetoMSstatsTMTFormat}}) as input
#' and generate two types of figures in pdf files as output :
#' (1) profile plot (specify "ProfilePlot" in option type), to identify the potential sources of variation for each protein;
#' (2) quality control plot (specify "QCPlot" in option type), to evaluate the systematic bias between MS runs.
#'
#' @export
#' @import ggplot2
#' @importFrom graphics axis image legend mtext par plot.new title plot
#' @importFrom grDevices dev.off hcl pdf
#' @importFrom dplyr mutate
#' @importFrom reshape2 dcast
#' @param data.peptide name of the data with peptide level, which can be the output of converter functions(\code{\link{PDtoMSstatsTMTFormat}}, \code{\link{MaxQtoMSstatsTMTFormat}}, \code{\link{SpectroMinetoMSstatsTMTFormat}}).
#' @param data.summarization name of the data with protein-level, which can be the output of \code{\link{proteinSummarization}} function.
#' @param type choice of visualization. "ProfilePlot" represents profile plot of log intensities across MS runs.
#' "QCPlot" represents box plots of log intensities across channels and MS runs.
#' @param ylimUp upper limit for y-axis in the log scale.
#' FALSE(Default) for Profile Plot and QC Plot uses the upper limit as rounded off maximum of log2(intensities) after normalization + 3..
#' @param ylimDown lower limit for y-axis in the log scale. FALSE(Default) for Profile Plot and QC Plot uses 0..
#' @param x.axis.size size of x-axis labeling for "Run" and "channel in Profile Plot and QC Plot.
#' @param y.axis.size size of y-axis labels. Default is 10.
#' @param text.size size of labels represented each condition at the top of Profile plot and QC plot. Default is 4.
#' @param text.angle angle of labels represented each condition at the top of Profile plot and QC plot. Default is 0.
#' @param legend.size size of legend above Profile plot. Default is 7.
#' @param dot.size.profile size of dots in Profile plot. Default is 2.
#' @param ncol.guide number of columns for legends at the top of plot. Default is 5.
#' @param width width of the saved pdf file. Default is 10.
#' @param height height of the saved pdf file. Default is 10.
#' @param which.Protein Protein list to draw plots. List can be names of Proteins or order numbers of Proteins.
#' Default is "all", which generates all plots for each protein. For QC plot, "allonly" will generate one QC plot with all proteins.
#' @param originalPlot TRUE(default) draws original profile plots, without normalization.
#' @param summaryPlot TRUE(default) draws profile plots with protein summarization for each channel and MS run.
#' @param address the name of folder that will store the results. Default folder is the current working directory.
#' The other assigned folder has to be existed under the current working directory.
#' An output pdf file is automatically created with the default name of "ProfilePlot.pdf" or "QCplot.pdf".
#' The command address can help to specify where to store the file as well as how to modify the beginning of the file name.
#' If address=FALSE, plot will be not saved as pdf file but showed in window.
#' @return plot or pdf
#' @examples
#' data(input.pd)
#' quant.msstats <- proteinSummarization(input.pd,
#' method="msstats",
#' global_norm=TRUE,
#' reference_norm=TRUE)
#'
#' ## Profile plot
#' dataProcessPlotsTMT(data.peptide=input.pd,
#' data.summarization=quant.msstats,
#' type='ProfilePlot',
#' width = 21,
#' height = 7)
#'
#' ## NottoRun: QC plot
#' # dataProcessPlotsTMT(data.peptide=input.pd,
#' # data.summarization=quant.msstats,
#' # type='QCPlot',
#' # width = 21,
#' # height = 7)
dataProcessPlotsTMT <- function(data.peptide,
data.summarization,
type,
ylimUp = FALSE,
ylimDown = FALSE,
x.axis.size = 10,
y.axis.size = 10,
text.size = 4,
text.angle = 90,
legend.size = 7,
dot.size.profile = 2,
ncol.guide = 5,
width = 10,
height = 10,
which.Protein = "all",
originalPlot = TRUE,
summaryPlot = TRUE,
address = "") {
Condition = Run = xorder = Channel = NULL
PeptideSequence = PSM = NULL
groupAxis = cumGroupAxis = abundance = analysis = NULL
datafeature <- data.peptide
datarun <- data.summarization
# conditions in feature data
fea.conds <- as.character(unique(datafeature$Condition))
# conditions in protein data
run.conds <- as.character(unique(datarun$Condition))
# only keep the overlapped conditions between feature data and protein data
shared.conds <- intersect(fea.conds, run.conds)
datafeature <- datafeature[datafeature$Condition %in% shared.conds,]
datarun <- datarun[datarun$Condition %in% shared.conds,]
# make sure condition is factor
datafeature$Condition <- factor(datafeature$Condition)
datarun$Condition <- factor(datarun$Condition)
colnames(datafeature)[colnames(datafeature) == 'ProteinName'] <- 'Protein'
datafeature$Protein <- factor(datafeature$Protein)
datarun$Protein <- factor(datarun$Protein)
## feature level data : log2 transform
datafeature$abundance <- log2(datafeature$Intensity)
datafeature[!is.na(datafeature$Intensity) &
datafeature$Intensity < 1, 'abundance'] <- 0
if (length(setdiff(toupper(type), c(toupper("ProfilePlot"), toupper("QCPlot")))) != 0) {
stop(paste0("Input for type=", type,
". However,'type' should be one of \"ProfilePlot\", \"QCPlot\"."))
}
if (address == FALSE){
## here I used == FALSE, instead of !address. Because address can be logical or characters.
if (which.Protein == 'all') {
stop('** Cannnot generate all plots in a screen. Please set one protein at a time.')
} else if (length(which.Protein) > 1) {
stop('** Cannnot generate multiple plots in a screen. Please set one protein at a time.')
}
}
## Profile plot ##
## ---------------
if (toupper(type) == "PROFILEPLOT") {
## choose Proteins or not
if (which.Protein != "all") {
## check which.Protein is name of Protein
if (is.character(which.Protein)) {
temp.name <- which.Protein
## message if name of Protein is wrong.
if (length(setdiff(temp.name,unique(datafeature$Protein))) > 0) {
stop(paste0("Please check protein name. Data set does not have this protein. - ",
toString(temp.name)))
}
}
## check which.Protein is order number of Protein
if (is.numeric(which.Protein)) {
temp.name <- levels(datafeature$Protein)[which.Protein]
## message if name of Protein is wrong.
if (length(levels(datafeature$Protein)) < max(which.Protein)) {
stop(paste0("Please check your ion of proteins. There are ",
length(levels(datafeature$Protein))," proteins in this dataset."))
}
}
## use only assigned proteins
datafeature <- datafeature[which(datafeature$Protein %in% temp.name), ]
datafeature$Protein <- factor(datafeature$Protein)
datarun <- datarun[which(datarun$Protein %in% temp.name), ]
datarun$Protein <- factor(datarun$Protein)
}
## assign upper or lower limit
y.limup <- ceiling(max(datafeature$abundance, na.rm = TRUE) + 3)
if (is.numeric(ylimUp)) {
y.limup <- ylimUp
}
y.limdown <- 0
if (is.numeric(ylimDown)) {
y.limdown <- ylimDown
}
datafeature <- datafeature[with(datafeature, order(Run, Condition, Channel)), ]
datafeature$Run <- factor(datafeature$Run)
datarun$Run <- factor(datarun$Run)
## !! important: order of x-axis
## can be reorder by group and then channel, WITHIN Run
## first make new column for x-axis
datafeature$group.channel <- paste(datafeature$Condition, datafeature$Channel, sep = "_")
## not sure better way for coding
## potentially change it.
datafeature$xorder <- NA
for (k in seq_along(unique(datafeature$Run))) {
runid <- unique(datafeature$Run)[k]
datafeature[datafeature$Run == runid, ]$xorder <- factor(datafeature[datafeature$Run == runid, ]$group.channel,
levels <- unique(datafeature[datafeature$Run == runid, ]$group.channel),
labels <- seq(1, length(unique(datafeature[datafeature$Run == runid, ]$group.channel))))
}
## check
## unique(datafeature[datafeature$Run == '5', c('Channel', 'Condition', 'Run', 'xorder','group.channel')])
## need to make data.frame with same variables for condition name
datafeature$xorder <- as.numeric(datafeature$xorder)
## keep unique information for x-axis labeling. will be used in plotting
tempGroupName <- unique(datafeature[, c("Condition", "xorder", "Run", "Channel")])
## count # per condition per Run
#groupline <- unique(datafeature[, c('Condition', 'Run')])
#groupline$groupAxis <- as.numeric(xtabs(~Condition+Run, tempGroupName))
groupline <- tempGroupName %>% dplyr::group_by(Condition, Run) %>% dplyr::mutate(groupAxis = n())
groupline <- groupline %>% dplyr::select(-xorder, -Channel)
groupline <- groupline[!duplicated(groupline), ]
## make accumurated # as condition increase
groupline <- groupline %>% dplyr::group_by(Run) %>% dplyr::mutate(cumGroupAxis = cumsum(groupAxis))
groupline$cumGroupAxis <- groupline$cumGroupAxis + 0.5
## add coordinate for group id
groupline$xorder <- groupline$cumGroupAxis - groupline$groupAxis / 2
groupline$abundance <- y.limup - 0.5
## save all information, for labeling group in plot
groupline.all <- groupline
## remove last condition for vertical line between groups
groupline <- groupline[-which(groupline$Condition %in% levels(groupline$Condition)[nlevels(groupline$Condition)]), ]
## need to fill in incomplete rows for Runlevel data
haverun <- FALSE
if (sum(is.element(colnames(datarun), "Run")) != 0) {
datamat <- dcast(Protein + Channel ~ Run, data = datarun, value.var = 'Abundance', keep = TRUE)
datarun <- melt(datamat, id.vars=c('Protein', 'Channel'))
colnames(datarun)[colnames(datarun) %in% c("variable", "value")] <- c('Run', 'Abundance')
## match x axis order
datarun <- merge(datarun, tempGroupName, by = c('Run', 'Channel'))
haverun <- TRUE
}
## save the plots as pdf or not
## If there are the file with the same name, add next numbering at the end of file name
## y-axis labeling
yaxis.name <- 'Log2-intensities'
if (originalPlot) {
if (address != FALSE) {
allfiles <- list.files()
num <- 0
filenaming <- paste0(address, "ProfilePlot")
finalfile <- paste0(address, "ProfilePlot.pdf")
while (is.element(finalfile, allfiles)) {
num <- num + 1
finalfile <- paste0(paste(filenaming, num, sep = "-"), ".pdf")
}
pdf(finalfile, width = width, height = height)
}
## factoring for run, channel, condition should be done before loop
for (i in seq_len(nlevels(datafeature$Protein))) {
sub <- datafeature[datafeature$Protein == levels(datafeature$Protein)[i], ]
sub$PeptideSequence <- factor(as.character(sub$PeptideSequence))
sub$Charge <- factor(as.character(sub$Charge))
sub$PSM <- factor(as.character(sub$PSM))
## if all measurements are NA,
if (nrow(sub) == sum(is.na(sub$abundance))) {
message(paste0("Can't the Profile plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)),
") because all measurements are NAs."))
next()
}
if (nrow(sub) == sum(!is.na(sub$abundance) & sub$abundance == 0)) {
message(paste0("Can't the Profile plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)),
") because all measurements are zeros."))
next()
}
## seq for peptide and charge
## for seting up color and linetype
b <- unique(sub[, c("PeptideSequence", "PSM")])
b <- b[with(b, order(PeptideSequence, PSM)), ] ## add because if there are missing value, orders are different.
temp1 <- xtabs(~PeptideSequence, b)
## unique charge id within peptide sequence, for line type
ss <- NULL
## unique peptide sequence id, for color
s <- NULL
for (j in seq_along(temp1)) {
temp3 <- rep(j, temp1[j])
s <- c(s, temp3)
temp2 <- seq(1, temp1[j])
ss <- c(ss, temp2)
}
## for annotation of condition
groupline.tmp <- data.frame(groupline,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1])
groupline.all.tmp <- data.frame(groupline.all,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1])
## 2019. 12. 17, MC : for profile plot, define color for dot
cbp <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
check.length <- length(unique(s)) %/% length(cbp)
if ( check.length > 0 ){
cbp <- rep(cbp, times=check.length + 1)
}
##
## 1st plot for original plot
ptemp <- ggplot(aes_string(x = 'xorder', y = 'abundance',
color = 'PSM', linetype = 'PSM'), data = sub) +
facet_grid(~Run) +
geom_point(size=dot.size.profile) +
geom_line(size = 0.5) +
scale_colour_manual(values=cbp[s]) +
scale_linetype_manual(values = ss) +
scale_shape_manual(values = c(16)) +
labs(title = unique(sub$Protein),
x = 'MS runs') +
scale_y_continuous(yaxis.name, limits = c(y.limdown, y.limup)) +
scale_x_continuous('MS runs') +
geom_vline(data = groupline.tmp,
aes(xintercept = cumGroupAxis),
colour = "grey", linetype = "longdash") +
geom_text(data = groupline.all.tmp,
aes(x = xorder, y = abundance, label = Condition),
size = text.size,
angle = text.angle,
color = "black") +
theme(
panel.background = element_rect(fill = 'white', colour = "black"),
legend.key = element_rect(fill = 'white', colour = 'white'),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'gray95'),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size = y.axis.size, colour = "black"),
axis.ticks = element_line(colour = "black"),
axis.title.x = element_text(size = x.axis.size + 5, vjust = -0.4),
axis.title.y = element_text(size = y.axis.size + 5, vjust = 0.3),
title = element_text(size = x.axis.size + 8, vjust = 1.5),
legend.position = "top",
legend.text = element_text(size = legend.size)) +
guides(color = guide_legend(title = paste("# peptide:", nlevels(sub$PeptideSequence)),
title.theme = element_text(size = 13, angle = 0),
keywidth = 0.4,
keyheight = 0.1,
default.unit = 'inch',
ncol = ncol.guide),
linetype = guide_legend(title = paste("# peptide:", nlevels(sub$PeptideSequence)),
title.theme = element_text(size = 13, angle = 0),
keywidth = 0.4,
keyheight = 0.1,
default.unit = 'inch',
ncol = ncol.guide))
print(ptemp)
message(paste("Drew the Profile plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)), ")"))
}
# end-loop for each protein
if (address != FALSE) {
dev.off()
}
} # end original plot
############################################
## 2st plot for original plot : summary
############################################
if (summaryPlot) {
if (address != FALSE) {
allfiles <- list.files()
num <- 0
filenaming <- paste0(address, "ProfilePlot_wSummarization")
finalfile <- paste0(address, "ProfilePlot_wSummarization.pdf")
while (is.element(finalfile, allfiles)) {
num <- num + 1
finalfile <- paste0(paste(filenaming, num, sep = "-"), ".pdf")
}
pdf(finalfile, width = width, height = height)
}
for (i in seq_len(nlevels(datafeature$Protein))) {
sub <- datafeature[datafeature$Protein == levels(datafeature$Protein)[i], ]
sub$PeptideSequence <- factor(as.character(sub$PeptideSequence))
sub$Charge <- factor(as.character(sub$Charge))
sub$PSM <- factor(as.character(sub$PSM))
## if all measurements are NA,
if (nrow(sub) == sum(is.na(sub$abundance))) {
message(paste0("Can't the Profile plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)),
") because all measurements are NAs."))
next()
}
if (nrow(sub) == sum(!is.na(sub$abundance) & sub$abundance == 0)) {
message(paste0("Can't the Profile plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)),
") because all measurements are zeros."))
next()
}
## for annotation of condition
groupline.tmp <- data.frame(groupline,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1],
"analysis" = 'Run summary')
groupline.all.tmp <- data.frame(groupline.all,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1],
"analysis" = 'Run summary')
if (haverun) {
subrun <- datarun[datarun$Protein == levels(datafeature$Protein)[i], ]
if (nrow(subrun) != 0) {
quantrun <- sub[1, ]
quantrun[, 2:ncol(quantrun)] <- NA
quantrun <- quantrun[rep(seq_len(nrow(subrun))), ]
quantrun$Protein <- subrun$Protein
quantrun$PeptideSequence <- "Run summary"
quantrun$Charge <- "Run summary"
quantrun$PSM <- "Run summary"
quantrun$Channel <- subrun$Channel
quantrun$Run <- subrun$Run
quantrun$abundance <- subrun$Abundance
quantrun$xorder <- subrun$xorder
} else {
# if there is only one Run measured across all runs, no Run information for linear with censored
quantrun <- datafeature[1, ]
quantrun[, 2:ncol(quantrun)] <- NA
quantrun$Protein <- levels(datafeature$Protein)[i]
quantrun$PeptideSequence <- "Run summary"
quantrun$Charge <- "Run summary"
quantrun$PSM <- "Run summary"
quantrun$abundance <- NA
quantrun$Intensity <- NA
}
quantrun$analysis <- "Run summary"
sub$analysis <- "Processed feature-level data"
final <- rbind(sub, quantrun)
final$analysis <- factor(final$analysis)
final$PSM <- factor(final$PSM)
ptempall <- ggplot(aes_string(x = 'xorder', y = 'abundance',
color = 'analysis', linetype = 'PSM', size = 'analysis'), data = final) +
facet_grid(~Run) +
geom_point(size = dot.size.profile) +
geom_line(size = 0.5) +
scale_colour_manual(values = c("lightgray", "darkred")) +
scale_shape_manual(values = c(16)) +
scale_size_manual(values = c(1.7, 2), guide = "none") +
scale_linetype_manual(values = c(rep(1, times = length(unique(final$PSM))-1), 2), guide = "none") +
labs(title = unique(sub$Protein),
x = 'MS runs') +
scale_y_continuous(yaxis.name, limits = c(y.limdown, y.limup)) +
geom_vline(data = groupline.tmp,
aes(xintercept = cumGroupAxis),
colour = "grey", linetype = "longdash") +
geom_text(data = groupline.all.tmp,
aes(x = xorder, y = abundance, label = Condition),
size = text.size,
angle = text.angle,
color = "black") +
theme(
panel.background = element_rect(fill = 'white', colour = "black"),
legend.key = element_rect(fill = 'white', colour = 'white'),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'gray95'),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size = y.axis.size, colour = "black"),
axis.ticks = element_line(colour = "black"),
axis.title.x = element_text(size = x.axis.size + 5, vjust = -0.4),
axis.title.y = element_text(size = y.axis.size + 5, vjust = 0.3),
title = element_text(size = x.axis.size + 8, vjust = 1.5),
legend.position = "top",
legend.text = element_text(size = legend.size),
legend.title = element_blank()) +
guides(color = guide_legend(order = 1,
title = NULL,
label.theme = element_text(size = 10, angle = 0)))
## draw point again because some red summary dots could be hiden
ptempall <- ptempall + geom_point(data = final, aes(x = xorder, y = abundance, size = analysis, color = analysis))
print(ptempall)
message(paste("Drew the Profile plot with summarization for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)), ")"))
}
} # end-loop for each protein
if (address!=FALSE) {
dev.off()
}
}
} # end Profile plot
## QC plot (Quality control plot) ##
## ---------------------------------
if (toupper(type) == "QCPLOT") {
## y-axis labeling
yaxis.name <- 'Log2-intensities'
## save the plots as pdf or not
## If there are the file with the same name, add next numbering at the end of file name
if (address != FALSE) {
allfiles <- list.files()
num <- 0
filenaming <- paste0(address,"QCPlot")
finalfile <- paste0(address,"QCPlot.pdf")
while (is.element(finalfile, allfiles)) {
num <- num + 1
finalfile <- paste0(paste(filenaming, num, sep = "-"), ".pdf")
}
pdf(finalfile, width = width, height = height)
}
## assign upper or lower limit
y.limup <- ceiling(max(datafeature$abundance, na.rm = TRUE) + 3)
if (is.numeric(ylimUp)) {
y.limup <- ylimUp
}
y.limdown <- 0
if (is.numeric(ylimDown)) {
y.limdown <- ylimDown
}
datafeature <- datafeature[with(datafeature, order(Run, Condition, Channel)), ]
## !! important: order of x-axis
## can be reorder by group and then channel, WITHIN Run
## first make new column for x-axis
datafeature$group.channel <- paste(datafeature$Condition, datafeature$Channel, sep = "_")
## not sure better way for coding
## potentially change it.
datafeature$xorder <- NA
for (k in seq_along(unique(datafeature$Run))) {
runid <- unique(datafeature$Run)[k]
datafeature[datafeature$Run == runid, ]$xorder <- factor(datafeature[datafeature$Run == runid, ]$group.channel,
levels <- unique(datafeature[datafeature$Run == runid, ]$group.channel),
labels <- seq(1, length(unique(datafeature[datafeature$Run == runid, ]$group.channel))))
}
## check
## unique(datafeature[datafeature$Run == 'PAMI-176_Mouse_K-T', c('Channel', 'Condition', 'Run', 'xorder','group.channel')])
## need to make data.frame with same variables for condition name
datafeature$xorder <- as.numeric(datafeature$xorder)
## keep unique information for x-axis labeling. will be used in plotting
tempGroupName <- unique(datafeature[, c("Condition", "xorder", "Run", "Channel")])
## count # per condition per Run
#groupline <- unique(datafeature[, c('Condition', 'Run')])
#groupline$groupAxis <- as.numeric(xtabs(~Condition+Run, tempGroupName))
groupline <- tempGroupName %>% dplyr::group_by(Condition, Run) %>% dplyr::mutate(groupAxis = n())
groupline <- groupline %>% dplyr::select(-xorder, -Channel)
groupline <- groupline[!duplicated(groupline), ]
## make accumurated # as condition increase
groupline <- groupline %>% dplyr::group_by(Run) %>% dplyr::mutate(cumGroupAxis = cumsum(groupAxis))
groupline$cumGroupAxis <- groupline$cumGroupAxis + 0.5
## add coordinate for group id
groupline$xorder <- groupline$cumGroupAxis - groupline$groupAxis / 2
groupline$abundance <- y.limup - 0.5
## save all information, for labeling group in plot
groupline.all <- groupline
## remove last condition for vertical line between groups
groupline <- groupline[-which(groupline$Condition %in% levels(groupline$Condition)[nlevels(groupline$Condition)]), ]
## all protein
if (which.Protein == 'all' | which.Protein == 'allonly') {
## for annotation of condition
groupline.tmp <- data.frame(groupline,
"PSM" = unique(datafeature$PSM)[1],
"PeptideSequence" = unique(datafeature$PeptideSequence)[1])
groupline.all.tmp <- data.frame(groupline.all,
"PSM" = unique(datafeature$PSM)[1],
"PeptideSequence" = unique(datafeature$PeptideSequence)[1])
## 1st plot for original plot
## for boxplot, x-axis, xorder should be factor
datafeature$xorder <- factor(datafeature$xorder)
ptemp <- ggplot(aes_string(x = 'xorder', y = 'abundance'), data = datafeature) +
facet_grid(~Run) +
geom_boxplot(aes_string(fill = 'Condition'), outlier.shape = 1, outlier.size = 1.5) +
labs(title = 'All proteins',
x = 'MS runs') +
scale_y_continuous(yaxis.name, limits = c(y.limdown, y.limup)) +
geom_vline(data = groupline.tmp,
aes(xintercept = cumGroupAxis),
colour = "grey", linetype = "longdash") +
geom_text(data = groupline.all.tmp,
aes(x = xorder, y = abundance, label = Condition),
size = text.size,
angle = text.angle,
color = "black") +
theme(
panel.background = element_rect(fill = 'white', colour = "black"),
legend.key = element_rect(fill = 'white', colour = 'white'),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'gray95'),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size = y.axis.size, colour = "black"),
axis.ticks = element_line(colour = "black"),
axis.title.x = element_text(size = x.axis.size + 5, vjust = -0.4),
axis.title.y = element_text(size = y.axis.size + 5, vjust = 0.3),
title = element_text(size = x.axis.size + 8, vjust = 1.5),
legend.position = "none")
print(ptemp)
message("Drew the Quality Contol plot(boxplot) for all proteins.")
}
## each protein
## choose Proteins or not
if (which.Protein != 'allonly') {
if (which.Protein != "all") {
## check which.Protein is name of Protein
if (is.character(which.Protein)) {
temp.name <- which.Protein
## message if name of Protein is wrong.
if (length(setdiff(temp.name, unique(datafeature$Protein))) > 0) {
dev.off()
stop(paste0("Please check protein name. Data set does not have this protein. - ",
toString(temp.name)))
}
}
## check which.Protein is order number of Protein
if (is.numeric(which.Protein)) {
temp.name <- levels(datafeature$Protein)[which.Protein]
## message if name of Protein is wrong.
if (length(levels(datafeature$Protein)) < max(which.Protein)) {
dev.off()
stop(paste0("Please check your ion of proteins. There are ",
length(levels(datafeature$Protein)), " proteins in this dataset."))
}
}
## use only assigned proteins
datafeature <- datafeature[which(datafeature$Protein %in% temp.name), ]
datafeature$Protein <- factor(datafeature$Protein)
}
for (i in seq_len(nlevels(datafeature$Protein))) {
sub <- datafeature[datafeature$Protein == levels(datafeature$Protein)[i], ]
sub <- sub[!is.na(sub$abundance), ]
## if all measurements are NA,
if (nrow(sub) == sum(is.na(sub$abundance))) {
message(paste("Can't the Quality Control plot for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)),
") because all measurements are NAs."))
next()
}
## for annotation of condition
groupline.tmp <- data.frame(groupline,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1])
groupline.all.tmp <- data.frame(groupline.all,
"PSM" = unique(sub$PSM)[1],
"PeptideSequence" = unique(sub$PeptideSequence)[1])
## 1st plot for original plot
## for boxplot, x-axis, xorder should be factor
sub$xorder <- factor(sub$xorder)
ptemp <- ggplot(aes_string(x = 'xorder', y = 'abundance'), data = sub) +
facet_grid(~Run) +
geom_boxplot(aes_string(fill = 'Condition'), outlier.shape = 1, outlier.size = 1.5) +
labs(title = unique(sub$Protein),
x = 'MS runs') +
scale_y_continuous(yaxis.name, limits = c(y.limdown, y.limup)) +
geom_vline(data = groupline.tmp,
aes(xintercept = cumGroupAxis),
colour = "grey", linetype = "longdash") +
geom_text(data = groupline.all.tmp,
aes(x = xorder, y = abundance, label = Condition),
size = text.size,
angle = text.angle,
color = "black") +
theme(
panel.background = element_rect(fill = 'white', colour = "black"),
legend.key = element_rect(fill = 'white', colour = 'white'),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'gray95'),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size = y.axis.size, colour = "black"),
axis.ticks = element_line(colour = "black"),
axis.title.x = element_text(size = x.axis.size + 5, vjust = -0.4),
axis.title.y = element_text(size = y.axis.size + 5, vjust = 0.3),
title = element_text(size = x.axis.size + 8, vjust = 1.5),
legend.position = "none")
print(ptemp)
message(paste("Drew the Quality Contol plot(boxplot) for ", unique(sub$Protein),
"(", i, " of ", length(unique(datafeature$Protein)), ")"))
} # end-loop
}
if (address != FALSE) {
dev.off()
}
} # end QC plot
}
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