R/plotfigures.R
In elqumsan/RNAseqMVA: Application of Multivariate Analysis methods to RNA-seq Data
Defines functions
plotFigures.DataTableWithClasses
plotFigures
Documented in
plotFigures
plotFigures.DataTableWithClasses
#' @title plot diagnostic figures for specific classes of objects
#' @author Mustafa AbuElQUmsan and Jacques van Helden
#' @description to explore the nature of raw data and showing the the distribution and main statistics for the raw data.
#' @param self is an object that is belonge to DataTableWithClasses.
#' @return utmost figures and visualisation about the main statistics and the distribution for the raw data.
#' @export
plotFigures <- function(self,...){
message("Exporting object of class", class(self), "to figures")
UseMethod("plotFigures", self)
}
#' @title plot diagnostic figures for an object of the class DataTableWithClasses.
#' @author Mustafa AbuElQUmsan and Jacques van Helden
#' @description to explore the nature of raw data and showing the the distribution and main statistics for the raw data.
#' @param self is an object that is belonge to DataTableWithClasses.
#' @return utmost figures and visualisation about the main statistics and the distribution for the raw data.
#' @export
plotFigures.DataTableWithClasses <- function( self,
plot.dir,
file.prefix,
dataType = self[["dataType"]],
extension = ".pdf") {
message( "Plotting the nature of the original, filtered and normalised count Data ", self[["ID"]], "for experiment" )
message("\tfigure directory\t", plot.dir)
message("\tFile prefix\t", file.prefix)
rawTable <- self$dataTable
# dim(rawTable)
stat.raw <- list()
# M = data.frame(matrix(rnorm(100000),nrow=500))
##### some basic statistics to exhibit the nature of the raw data #####
stat.raw$min = apply(rawTable,1,min)
stat.raw$fQuan = apply(rawTable,1,quantile,prob=0.25)
stat.raw$median = apply(rawTable,1,median)
stat.raw$tQaun = apply(rawTable,1,quantile,prob=0.75)
stat.raw$max = apply(rawTable,1,max)
stat.raw$mean = apply(rawTable,1,mean)
stat.raw$sd = apply(rawTable,1,sd)
# par(mfrow=c(1,2))
message.with.time("\t\tDrawing plots describing original count table statistics")
#file.prefix <- paste("main_stat.raw_",parameters$recountID,sep = "")
boxplot.file <- file.path(plot.dir , paste(dataType ,"_",self[["ID"]], "_basic_stat", extension, sep = ""))
pdf(file = boxplot.file)
boxplot(cbind(stat.raw$min, stat.raw$fQuan, stat.raw$median, stat.raw$tQaun, stat.raw$max, stat.raw$mean, stat.raw$sd) ,
ylim= c(0,100), names=c("min","F.Qaunt","median","T.Qaunt","max","mean","sd"), las=1, cex.axis = 0.7,
xlab= c(paste("Sammary statistics for the raw Table",self[["ID"]])), ylab=" No. of genes")
silence <- dev.off()
file.prefix <- paste(dataType,"_", self[["ID"]], sep = "")
boxplot.file <- file.path(plot.dir, paste(file.prefix, "differ_stat",extension, sep = "_"))
pdf(file = boxplot.file)
boxplot(cbind(stat.raw$fQuan - stat.raw$min, stat.raw$median - stat.raw$min, stat.raw$tQaun - stat.raw$min, stat.raw$max - stat.raw$tQaun) , ylim= c(0,50),
names= c("fQaun-min", "median-min", "tQuan-min", "max-tQuan"), cex.axis = 0.7,
xlab= c(paste("difference between some statistics\n","in Raw table" ,parameters$recountID)),
ylab = "No. genes")
silence <- dev.off()
##### cheking the No. libsum for each classes in the Raw Table ##################
# data.frame(libsum=apply(loaded$dataTable, 1, sum), class=loaded$classes)
# x <- data.frame(q3 = apply(rawCounts$Counts, 1, quantile, q=0.75), sum = apply(rawCounts$Counts, 1, sum), class=loaded$classes)
#
# x <- data.frame(q3 = apply(rawCounts$Counts, 1, quantile, q=0.75), sum = apply(rawCounts$Counts, 1, sum), class=loaded$classes)
libsum=apply(self[["dataTable"]], 1, sum)
x <- data.frame(libsum=apply(self[["dataTable"]], 2, sum), class=self[["classLabels"]])
head(x)
file.prefix <- paste("libsum_",dataType,"_",self[["ID"]],sep = "")
boxplot.file <- file.path(plot.dir, paste(file.prefix,extension, sep = ""))
pdf(file = boxplot.file)
save.margins <- par("mar")
par(mar = c(14,5,5,1))
boxplot(libsum ~ self[["classLabels"]] , data=x, horizontal=TRUE, las=1, cex.axis=0.7,
xlab= c(paste("Libsum statistics for the raw Table\n","distribution for each class in",self[["ID"]])),
ylab="Class Labels")
par(mar=save.margins)
silence <- dev.off()
##### some basic statistics to exhibit the nature of the log2norm data #####
# log2normTable <- loaded$log2norm$counts
# stat.log2norm <- list()
#
# stat.log2norm$min = apply(log2normTable,2,min)
# stat.log2norm$fQuan = apply(log2normTable,2,quantile,prob=0.25)
# stat.log2norm$median = apply(log2normTable,2,median)
# stat.log2norm$tQaun = apply(log2normTable,2,quantile,prob=0.75)
# stat.log2norm$max = apply(log2normTable,2,max)
#
# stat.log2norm$mean = apply(log2normTable,2,mean)
# stat.log2norm$sd = apply(log2normTable,2,sd)
#
# # par(mfrow=c(1,2))
# message.with.time("Drawing plots describing log2norm table statistics")
#
# file.prefix <- paste("main_stat.log2norm_",parameters$recountID,sep = "")
# boxplot.file <- file.path(parameters$dir$NormalizationImpact, paste(file.prefix,"boxplot.pdf", sep = "_"))
# pdf(file = boxplot.file)
# boxplot(cbind(stat.log2norm$min, stat.log2norm$fQuan, stat.log2norm$median, stat.log2norm$tQaun, stat.log2norm$max, stat.log2norm$mean, stat.log2norm$sd) ,
# ylim= c(0,100), names=c("min","F.Qaunt","median","T.Qaunt","max","mean","sd"), las=1, cex.axis = 0.7,
# xlab= c(paste("Sammary statistics for the log2norm Table",parameters$recountID)))
# silence <- dev.off()
#
# file.prefix <- paste("diff_bet_stat.log2norm_", parameters$recountID, sep = "")
# boxplot.file <- file.path(parameters$dir$NormalizationImpact, paste(file.prefix, "boxplot.pdf", sep = "_"))
# pdf(file = boxplot.file)
# boxplot(cbind(stat.log2norm$fQuan - stat.log2norm$min, stat.log2norm$median - stat.log2norm$min, stat.log2norm$tQaun - stat.log2norm$min, stat.log2norm$max - stat.log2norm$tQaun) , ylim= c(0,100),
# names= c("fQaun-min", "median-min", "tQuan-min", "max-tQuan"), cex.axis = 0.7,
# xlab= c(paste("difference between some statistics\n","in log2norm table" ,parameters$recountID)))
# silence <- dev.off()
##### cheking the No. libsum for each classes in the log2norm Table ##################
# data.frame(libsum=apply(loaded$dataTable, 1, sum), class=loaded$classes)
# x <- data.frame(q3 = apply(log2norm$Counts, 1, quantile, q=0.75), sum = apply(log2norm$Counts, 1, sum), class=loaded$classes)
#
# x <- data.frame(q3 = apply(rawCounts1, 1, quantile, q=0.75), sum = apply(rawCounts1, 1, sum), class=loaded$classes)
# x <- data.frame(libsum=apply(loaded$log2norm$counts, 2, sum), class=loaded$log2norm$classLabels)
#
# head(x)
#
#
# file.prefix <- paste("libsum.log2norm_",parameters$recountID,sep = "")
# boxplot.file <- file.path(parameters$dir$NormalizationImpact, paste(file.prefix,"boxplot.pdf", sep = "_"))
# pdf(file = boxplot.file)
# save.margins <- par("mar")
# par(mar = c(14,5,5,1))
# boxplot(libsum ~ class , data=x, horizontal=TRUE, las=1, cex.axis=0.7,
# xlab= c(paste("Libsum statistics for the log2norm Table\n","distribution for each class in",parameters$recountID)))
# par(mar=save.margins)
# silence <- dev.off()
#############################################################################################
###### Draw some plots to showing effects of some treatments(Norm and log2) on the Count Table ####
#############################################################################################
message.with.time("\tDrawing plots describing count table statistics and distribution")
#### Compute a trimmed mean: suppress the 5% top and bottom values ####
if (parameters$compute) {
message.with.time("Computing trimmed mean of normalized counts")
x <- unlist(self[["dataTable"]])
q0.05 <- quantile(x = x, probs = 0.05, na.rm=TRUE)
q0.95 <- quantile(x = x, probs = 0.95, na.rm=TRUE)
trimmed <- (x[x > q0.05 & x < q0.95])
suppressed.proportion <- 1 - length(trimmed)/length(x)
} else {
message.with.time("Skipping a trimmed mean")
}
## histogram of normalized counts. Zoom on the representative part of the histogram
file.prefix <- file.path(plot.dir, paste( dataType ,"_",self[["ID"]], "_","histogram" ,extension, sep = ""))
pdf(file=file.prefix,
width = 8, height = 8)
hist(unlist(self[["dataTable"]]), breaks=10, las=1,
xlab="normalized counts", ylab="Occurrences", col="grey",
main=paste(parameters$recountID, "Normalised count distrib"))
abline(v=mean(unlist(self[["dataTable"]])), lwd=1, col="darkgreen") # mark the mean
abline(v=median(unlist(self[["dataTable"]])), lwd=1, col="blue") # mark the median
abline(v=mean(unlist(trimmed)), lwd=1, col="purple") # mark the trimmed mean
legend("topright",lwd=2,
legend=c("mean", "median", "trimmed mean"),
col=c("darkgreen", "blue", "purple"))
silence <- dev.off()
#
# #### Compute a trimmed mean: suppress the 5% top and bottom values ####
# if (parameters$compute) {
# message.with.time("Computing trimmed mean of log2normalized counts")
# x <- unlist(self[["dataTable"]])
# q0.05 <- quantile(x = x, probs = 0.05, na.rm=TRUE)
# q0.95 <- quantile(x = x, probs = 0.95, na.rm=TRUE)
# log2.trimmed <- (x[x > q0.05 & x < q0.95])
# suppressed.proportion <- 1 - length(log2.trimmed)/length(x)
# } else {
# message.with.time("Skipping a trimmed mean")
# }
#
# file.prefix <- file.path(plot.dir, dataType,paste(self[["ID"]], extension , sep = ""))
#
# pdf(file=file.prefix,
# width = 8, height = 8)
# hist(unlist(loaded$log2norm$counts), breaks=100, las=1,
# xlab="log2 Normalized counts", ylab="Occurrences", col="grey",
# main=paste(parameters$recountID, " log 2 Normalised count distribution"))
# abline(v=mean(unlist(loaded$log2norm$counts)), lwd=1, col="darkgreen") # mark the mean
# abline(v=median(unlist(loaded$log2norm$counts)), lwd=1, col="blue") # mark the median
# abline(v=mean(unlist(log2.trimmed)), lwd=1, col="purple") # mark the trimmed mean
# legend("topright",lwd=2,
# legend=c("mean", "median", "trimmed mean"),
# col=c("darkgreen", "blue", "purple"))
# silence <- dev.off()
#
###### interpretation the difference between the mean and the third quartile raw count #####
file.prefix <- file.path(plot.dir, paste(dataType, "_",self[["ID"]],"", extension, sep = ""))
pdf(file= file.prefix,
width = 8, height = 8)
plot(x=apply(self[["dataTable"]], 1, mean),
y=signif(digits=3, apply(self[["dataTable"]], 1, quantile, 0.75)),
main="raw counts: Percentile 75 versus mean",
xlab="Mean counts per sample",
ylab="Percentile 75",
col=self$sampleColors,
las=1,
panel.first=grid())
legend("topleft", legend=self$classNames, col=self$sampleColors, pch=1, cex=0.8)
silence <- dev.off()
###### interpretation the difference between the mean and the third quartile log2counts #####
file.prefix <- file.path(plot.dir, paste(dataType,"_",self[["ID"]], "_","mean_3Q",extension, sep = ""))
pdf(file= file.prefix,
width = 8, height = 8)
plot(x=apply(self[["dataTable"]], 1, mean),
y=signif(digits=3, apply(self[["dataTable"]], 1, quantile, 0.75)),
main="log2norm counts: Percentile 75 versus mean",
xlab="Mean counts per sample",
ylab="Percentile 75",
col=self$sampleColors,
las=1,
panel.first=grid())
legend("topleft", legend = unique(names(self$sampleColors)), col=self$sampleColors, pch=1, cex=0.8)
silence <- dev.off()
message.with.time(" finishing from drawing plots describing count table statistics and distribution")
}
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Defines functions plotFigures.DataTableWithClasses plotFigures
Documented in plotFigures plotFigures.DataTableWithClasses
#' @title plot diagnostic figures for specific classes of objects
#' @author Mustafa AbuElQUmsan and Jacques van Helden
#' @description to explore the nature of raw data and showing the the distribution and main statistics for the raw data.
#' @param self is an object that is belonge to DataTableWithClasses.
#' @return utmost figures and visualisation about the main statistics and the distribution for the raw data.
#' @export
plotFigures <- function(self,...){
message("Exporting object of class", class(self), "to figures")
UseMethod("plotFigures", self)
}
#' @title plot diagnostic figures for an object of the class DataTableWithClasses.
#' @author Mustafa AbuElQUmsan and Jacques van Helden
#' @description to explore the nature of raw data and showing the the distribution and main statistics for the raw data.
#' @param self is an object that is belonge to DataTableWithClasses.
#' @return utmost figures and visualisation about the main statistics and the distribution for the raw data.
#' @export
plotFigures.DataTableWithClasses <- function( self,
plot.dir,
file.prefix,
dataType = self[["dataType"]],
extension = ".pdf") {
message( "Plotting the nature of the original, filtered and normalised count Data ", self[["ID"]], "for experiment" )
message("\tfigure directory\t", plot.dir)
message("\tFile prefix\t", file.prefix)
rawTable <- self$dataTable
# dim(rawTable)
stat.raw <- list()
# M = data.frame(matrix(rnorm(100000),nrow=500))
##### some basic statistics to exhibit the nature of the raw data #####
stat.raw$min = apply(rawTable,1,min)
stat.raw$fQuan = apply(rawTable,1,quantile,prob=0.25)
stat.raw$median = apply(rawTable,1,median)
stat.raw$tQaun = apply(rawTable,1,quantile,prob=0.75)
stat.raw$max = apply(rawTable,1,max)
stat.raw$mean = apply(rawTable,1,mean)
stat.raw$sd = apply(rawTable,1,sd)
# par(mfrow=c(1,2))
message.with.time("\t\tDrawing plots describing original count table statistics")
#file.prefix <- paste("main_stat.raw_",parameters$recountID,sep = "")
boxplot.file <- file.path(plot.dir , paste(dataType ,"_",self[["ID"]], "_basic_stat", extension, sep = ""))
pdf(file = boxplot.file)
boxplot(cbind(stat.raw$min, stat.raw$fQuan, stat.raw$median, stat.raw$tQaun, stat.raw$max, stat.raw$mean, stat.raw$sd) ,
ylim= c(0,100), names=c("min","F.Qaunt","median","T.Qaunt","max","mean","sd"), las=1, cex.axis = 0.7,
xlab= c(paste("Sammary statistics for the raw Table",self[["ID"]])), ylab=" No. of genes")
silence <- dev.off()
file.prefix <- paste(dataType,"_", self[["ID"]], sep = "")
boxplot.file <- file.path(plot.dir, paste(file.prefix, "differ_stat",extension, sep = "_"))
pdf(file = boxplot.file)
boxplot(cbind(stat.raw$fQuan - stat.raw$min, stat.raw$median - stat.raw$min, stat.raw$tQaun - stat.raw$min, stat.raw$max - stat.raw$tQaun) , ylim= c(0,50),
names= c("fQaun-min", "median-min", "tQuan-min", "max-tQuan"), cex.axis = 0.7,
xlab= c(paste("difference between some statistics\n","in Raw table" ,parameters$recountID)),
ylab = "No. genes")
silence <- dev.off()
##### cheking the No. libsum for each classes in the Raw Table ##################
# data.frame(libsum=apply(loaded$dataTable, 1, sum), class=loaded$classes)
# x <- data.frame(q3 = apply(rawCounts$Counts, 1, quantile, q=0.75), sum = apply(rawCounts$Counts, 1, sum), class=loaded$classes)
#
# x <- data.frame(q3 = apply(rawCounts$Counts, 1, quantile, q=0.75), sum = apply(rawCounts$Counts, 1, sum), class=loaded$classes)
libsum=apply(self[["dataTable"]], 1, sum)
x <- data.frame(libsum=apply(self[["dataTable"]], 2, sum), class=self[["classLabels"]])
head(x)
file.prefix <- paste("libsum_",dataType,"_",self[["ID"]],sep = "")
boxplot.file <- file.path(plot.dir, paste(file.prefix,extension, sep = ""))
pdf(file = boxplot.file)
save.margins <- par("mar")
par(mar = c(14,5,5,1))
boxplot(libsum ~ self[["classLabels"]] , data=x, horizontal=TRUE, las=1, cex.axis=0.7,
xlab= c(paste("Libsum statistics for the raw Table\n","distribution for each class in",self[["ID"]])),
ylab="Class Labels")
par(mar=save.margins)
silence <- dev.off()
##### some basic statistics to exhibit the nature of the log2norm data #####
# log2normTable <- loaded$log2norm$counts
# stat.log2norm <- list()
#
# stat.log2norm$min = apply(log2normTable,2,min)
# stat.log2norm$fQuan = apply(log2normTable,2,quantile,prob=0.25)
# stat.log2norm$median = apply(log2normTable,2,median)
# stat.log2norm$tQaun = apply(log2normTable,2,quantile,prob=0.75)
# stat.log2norm$max = apply(log2normTable,2,max)
#
# stat.log2norm$mean = apply(log2normTable,2,mean)
# stat.log2norm$sd = apply(log2normTable,2,sd)
#
# # par(mfrow=c(1,2))
# message.with.time("Drawing plots describing log2norm table statistics")
#
# file.prefix <- paste("main_stat.log2norm_",parameters$recountID,sep = "")
# boxplot.file <- file.path(parameters$dir$NormalizationImpact, paste(file.prefix,"boxplot.pdf", sep = "_"))
# pdf(file = boxplot.file)
# boxplot(cbind(stat.log2norm$min, stat.log2norm$fQuan, stat.log2norm$median, stat.log2norm$tQaun, stat.log2norm$max, stat.log2norm$mean, stat.log2norm$sd) ,
# ylim= c(0,100), names=c("min","F.Qaunt","median","T.Qaunt","max","mean","sd"), las=1, cex.axis = 0.7,
# xlab= c(paste("Sammary statistics for the log2norm Table",parameters$recountID)))
# silence <- dev.off()
#
# file.prefix <- paste("diff_bet_stat.log2norm_", parameters$recountID, sep = "")
# boxplot.file <- file.path(parameters$dir$NormalizationImpact, paste(file.prefix, "boxplot.pdf", sep = "_"))
# pdf(file = boxplot.file)
# boxplot(cbind(stat.log2norm$fQuan - stat.log2norm$min, stat.log2norm$median - stat.log2norm$min, stat.log2norm$tQaun - stat.log2norm$min, stat.log2norm$max - stat.log2norm$tQaun) , ylim= c(0,100),
# names= c("fQaun-min", "median-min", "tQuan-min", "max-tQuan"), cex.axis = 0.7,
# xlab= c(paste("difference between some statistics\n","in log2norm table" ,parameters$recountID)))
# silence <- dev.off()
##### cheking the No. libsum for each classes in the log2norm Table ##################
# data.frame(libsum=apply(loaded$dataTable, 1, sum), class=loaded$classes)
# x <- data.frame(q3 = apply(log2norm$Counts, 1, quantile, q=0.75), sum = apply(log2norm$Counts, 1, sum), class=loaded$classes)
#
# x <- data.frame(q3 = apply(rawCounts1, 1, quantile, q=0.75), sum = apply(rawCounts1, 1, sum), class=loaded$classes)
# x <- data.frame(libsum=apply(loaded$log2norm$counts, 2, sum), class=loaded$log2norm$classLabels)
#
# head(x)
#
#
# file.prefix <- paste("libsum.log2norm_",parameters$recountID,sep = "")
# boxplot.file <- file.path(parameters$dir$NormalizationImpact, paste(file.prefix,"boxplot.pdf", sep = "_"))
# pdf(file = boxplot.file)
# save.margins <- par("mar")
# par(mar = c(14,5,5,1))
# boxplot(libsum ~ class , data=x, horizontal=TRUE, las=1, cex.axis=0.7,
# xlab= c(paste("Libsum statistics for the log2norm Table\n","distribution for each class in",parameters$recountID)))
# par(mar=save.margins)
# silence <- dev.off()
#############################################################################################
###### Draw some plots to showing effects of some treatments(Norm and log2) on the Count Table ####
#############################################################################################
message.with.time("\tDrawing plots describing count table statistics and distribution")
#### Compute a trimmed mean: suppress the 5% top and bottom values ####
if (parameters$compute) {
message.with.time("Computing trimmed mean of normalized counts")
x <- unlist(self[["dataTable"]])
q0.05 <- quantile(x = x, probs = 0.05, na.rm=TRUE)
q0.95 <- quantile(x = x, probs = 0.95, na.rm=TRUE)
trimmed <- (x[x > q0.05 & x < q0.95])
suppressed.proportion <- 1 - length(trimmed)/length(x)
} else {
message.with.time("Skipping a trimmed mean")
}
## histogram of normalized counts. Zoom on the representative part of the histogram
file.prefix <- file.path(plot.dir, paste( dataType ,"_",self[["ID"]], "_","histogram" ,extension, sep = ""))
pdf(file=file.prefix,
width = 8, height = 8)
hist(unlist(self[["dataTable"]]), breaks=10, las=1,
xlab="normalized counts", ylab="Occurrences", col="grey",
main=paste(parameters$recountID, "Normalised count distrib"))
abline(v=mean(unlist(self[["dataTable"]])), lwd=1, col="darkgreen") # mark the mean
abline(v=median(unlist(self[["dataTable"]])), lwd=1, col="blue") # mark the median
abline(v=mean(unlist(trimmed)), lwd=1, col="purple") # mark the trimmed mean
legend("topright",lwd=2,
legend=c("mean", "median", "trimmed mean"),
col=c("darkgreen", "blue", "purple"))
silence <- dev.off()
#
# #### Compute a trimmed mean: suppress the 5% top and bottom values ####
# if (parameters$compute) {
# message.with.time("Computing trimmed mean of log2normalized counts")
# x <- unlist(self[["dataTable"]])
# q0.05 <- quantile(x = x, probs = 0.05, na.rm=TRUE)
# q0.95 <- quantile(x = x, probs = 0.95, na.rm=TRUE)
# log2.trimmed <- (x[x > q0.05 & x < q0.95])
# suppressed.proportion <- 1 - length(log2.trimmed)/length(x)
# } else {
# message.with.time("Skipping a trimmed mean")
# }
#
# file.prefix <- file.path(plot.dir, dataType,paste(self[["ID"]], extension , sep = ""))
#
# pdf(file=file.prefix,
# width = 8, height = 8)
# hist(unlist(loaded$log2norm$counts), breaks=100, las=1,
# xlab="log2 Normalized counts", ylab="Occurrences", col="grey",
# main=paste(parameters$recountID, " log 2 Normalised count distribution"))
# abline(v=mean(unlist(loaded$log2norm$counts)), lwd=1, col="darkgreen") # mark the mean
# abline(v=median(unlist(loaded$log2norm$counts)), lwd=1, col="blue") # mark the median
# abline(v=mean(unlist(log2.trimmed)), lwd=1, col="purple") # mark the trimmed mean
# legend("topright",lwd=2,
# legend=c("mean", "median", "trimmed mean"),
# col=c("darkgreen", "blue", "purple"))
# silence <- dev.off()
#
###### interpretation the difference between the mean and the third quartile raw count #####
file.prefix <- file.path(plot.dir, paste(dataType, "_",self[["ID"]],"", extension, sep = ""))
pdf(file= file.prefix,
width = 8, height = 8)
plot(x=apply(self[["dataTable"]], 1, mean),
y=signif(digits=3, apply(self[["dataTable"]], 1, quantile, 0.75)),
main="raw counts: Percentile 75 versus mean",
xlab="Mean counts per sample",
ylab="Percentile 75",
col=self$sampleColors,
las=1,
panel.first=grid())
legend("topleft", legend=self$classNames, col=self$sampleColors, pch=1, cex=0.8)
silence <- dev.off()
###### interpretation the difference between the mean and the third quartile log2counts #####
file.prefix <- file.path(plot.dir, paste(dataType,"_",self[["ID"]], "_","mean_3Q",extension, sep = ""))
pdf(file= file.prefix,
width = 8, height = 8)
plot(x=apply(self[["dataTable"]], 1, mean),
y=signif(digits=3, apply(self[["dataTable"]], 1, quantile, 0.75)),
main="log2norm counts: Percentile 75 versus mean",
xlab="Mean counts per sample",
ylab="Percentile 75",
col=self$sampleColors,
las=1,
panel.first=grid())
legend("topleft", legend = unique(names(self$sampleColors)), col=self$sampleColors, pch=1, cex=0.8)
silence <- dev.off()
message.with.time(" finishing from drawing plots describing count table statistics and distribution")
}
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