cardinal_quality_report: quality_report.xml comparison

comparison quality_report.xml @ 15:23d0394b5908 draft

"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit c8d3adac445b4e08e2724e22d7201bfc38bbf40f"

author	galaxyp
date	Sun, 29 Aug 2021 07:30:21 +0000
parents	ecaebe7c7b54
children	d426a9107a6c

comparison

equal deleted inserted replaced

-:5a35e3a8d013
+:23d0394b5908
-<tool id="cardinal_quality_report" name="MSI Qualitycontrol" version="@VERSION@.1">
+<tool id="cardinal_quality_report" name="MSI Qualitycontrol" version="@VERSION@.0">
 <description>
 mass spectrometry imaging QC
 </description>
 <macros>
 <import>macros.xml</import>
 </macros>
 <expand macro="requirements">
 <requirement type="package" version="2.3">r-gridextra</requirement>
-<requirement type="package" version="3.3.2">r-ggplot2</requirement>
+<requirement type="package" version="3.3.5">r-ggplot2</requirement>
 <requirement type="package" version="1.1_2">r-rcolorbrewer</requirement>
-<requirement type="package" version="2.23_17">r-kernsmooth</requirement>
+<requirement type="package" version="2.23_20">r-kernsmooth</requirement>
 <requirement type="package" version="1.1.1">r-scales</requirement>
 <requirement type="package" version="1.0.12">r-pheatmap</requirement>
 </expand>
 <command detect_errors="exit_code">
 <![CDATA[
 #end for
 #end if
 #################### 4) m/z heatmaps #######################################
-par(mfrow=c(1,1), mar=c(5.1, 4.1, 4.1, 2.1), mgp=c(3, 1, 0), las=0)
-if (length(inputcalibrants[,1]) != 0){
+#if $report_depth:
-for (mass in 1:length(inputcalibrants[,1])){
-par(oma=c(0,0,0,1))## margin for image legend
+		par(mfrow=c(1,1), mar=c(5.1, 4.1, 4.1, 2.1), mgp=c(3, 1, 0), las=0)
+		if (length(inputcalibrants[,1]) != 0){
-tryCatch(
+		for (mass in 1:length(inputcalibrants[,1])){
-{
+			par(oma=c(0,0,0,1))## margin for image legend
-print(image(msidata, mz=inputcalibrants[,1][mass], plusminus=plusminusvalues[mass],
-main= paste0(inputcalibrants[,2][mass], ": ", round(inputcalibrants[,1][mass], digits = 2)," (±",$plusminus_ppm, " ppm)"),
+		   tryCatch(
-contrast.enhance = "histogram", strip=FALSE, ylim= c(maximumy,minimumy)))
+				{
-},
+				print(image(msidata, mz=inputcalibrants[,1][mass], plusminus=plusminusvalues[mass],
-error=function(cond) {
+			main= paste0(inputcalibrants[,2][mass], ": ", round(inputcalibrants[,1][mass], digits = 2)," (±",$plusminus_ppm, " ppm)"),
-## if there are not enough intensities in the mz range skip creating an image
+			contrast.enhance = "histogram", strip=FALSE, ylim= c(maximumy,minimumy)))
-print(paste0("Not enough intensities > 0 for m/z ", inputcalibrants[,1][mass]))
+				},
-}
+				error=function(cond) {
-)
+				## if there are not enough intensities in the mz range skip creating an image
-}
+				print(paste0("Not enough intensities > 0 for m/z ", inputcalibrants[,1][mass]))
-} else {print("4) The input peptide and calibrant m/z were not provided or outside the m/z range")}
+				}
+				)
+		}
+		} else {print("4) The input peptide and calibrant m/z were not provided or outside the m/z range")}
+#end if
 #################### 5) Number of peaks per pixel - image ##################
 ## here every intensity value > 0 counts as peak
 peaksperpixel = colSums(spectra(msidata)> 0, na.rm=TRUE)
 rm(TICcoordarray)
 gc()
 ############################### 6b) median int image ###############################
-median_int = pixelApply(msidata, median, na.rm=TRUE)
+#if $report_depth:
-median_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, median_int)
+		median_int = pixelApply(msidata, median, na.rm=TRUE)
-colnames(median_coordarray) = c("x", "y", "median_int")
-print(ggplot(median_coordarray, aes(x=x, y=y, fill=median_int))+
+		median_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, median_int)
-geom_tile() + coord_fixed() +
+		colnames(median_coordarray) = c("x", "y", "median_int")
-ggtitle("Median intensity per spectrum")+
+		print(ggplot(median_coordarray, aes(x=x, y=y, fill=median_int))+
-theme_bw() +
+		 geom_tile() + coord_fixed() +
-theme(plot.title = element_text(hjust = 0.5))+
+		 ggtitle("Median intensity per spectrum")+
-theme(text=element_text(family="ArialMT", face="bold", size=12))+
+		 theme_bw() +
-scale_fill_gradientn(colours = c("blue", "purple" , "red","orange")
+		 theme(plot.title = element_text(hjust = 0.5))+
-,space = "Lab", na.value = "black", name = "median\nintensity"))
+		 theme(text=element_text(family="ArialMT", face="bold", size=12))+
+		 scale_fill_gradientn(colours = c("blue", "purple" , "red","orange")
-## remove median_coordarray to clean up RAM space
+		                        ,space = "Lab", na.value = "black", name = "median\nintensity"))
-rm(median_coordarray)
-gc()
+		## remove median_coordarray to clean up RAM space
+		    rm(median_coordarray)
-############################### 6c) max int image ###############################
+		    gc()
-max_int = pixelApply(msidata, max, na.rm=TRUE)
+		############################### 6c) max int image ###############################
-max_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, max_int)
+		max_int = pixelApply(msidata, max, na.rm=TRUE)
-colnames(max_coordarray) = c("x", "y", "max_int")
-print(ggplot(max_coordarray, aes(x=x, y=y, fill=max_int))+
+		max_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, max_int)
-geom_tile() + coord_fixed() +
+		colnames(max_coordarray) = c("x", "y", "max_int")
-ggtitle("Maximum intensity per spectrum")+
+		print(ggplot(max_coordarray, aes(x=x, y=y, fill=max_int))+
-theme_bw() +
+		 geom_tile() + coord_fixed() +
-theme(plot.title = element_text(hjust = 0.5))+
+		 ggtitle("Maximum intensity per spectrum")+
-theme(text=element_text(family="ArialMT", face="bold", size=12))+
+		 theme_bw() +
-scale_fill_gradientn(colours = c("blue", "purple" , "red","orange")
+		 theme(plot.title = element_text(hjust = 0.5))+
-,space = "Lab", na.value = "black", name = "max\nintensity"))
+		 theme(text=element_text(family="ArialMT", face="bold", size=12))+
+		 scale_fill_gradientn(colours = c("blue", "purple" , "red","orange")
-## remove median_coordarray to clean up RAM space
+		                        ,space = "Lab", na.value = "black", name = "max\nintensity"))
-rm(max_coordarray)
-gc()
+		## remove median_coordarray to clean up RAM space
+		    rm(max_coordarray)
-############################### 7) Most abundant m/z image #################
+		    gc()
-## for each spectrum find the row (m/z) with the highest intensity
+		############################### 7) Most abundant m/z image #################
-highestmz = pixelApply(msidata, which.max)
+		## for each spectrum find the row (m/z) with the highest intensity
-## in case for some spectra max returns integer(0), highestmz is a list and integer(0) have to be replaced with NA and unlisted
+		highestmz = pixelApply(msidata, which.max)
-if (class(highestmz) == "list"){
-##find zero-length values
+		## in case for some spectra max returns integer(0), highestmz is a list and integer(0) have to be replaced with NA and unlisted
-zero_entry <- !(sapply(highestmz, length))
+		if (class(highestmz) == "list"){
-### replace these values with NA
+		    ##find zero-length values
-highestmz[zero_entry] <- NA
+		    zero_entry <- !(sapply(highestmz, length))
-### unlist list to get a vector
+		    ### replace these values with NA
-highestmz = unlist(highestmz)}
+		    highestmz[zero_entry] <- NA
+		    ### unlist list to get a vector
-highestmz_matrix = data.frame(coord(msidata)\$x, coord(msidata)\$y,mz(msidata)[highestmz])
+		    highestmz = unlist(highestmz)}
-colnames(highestmz_matrix) = c("x", "y", "highestmzinDa")
+		highestmz_matrix = data.frame(coord(msidata)\$x, coord(msidata)\$y,mz(msidata)[highestmz])
-print(ggplot(highestmz_matrix, aes(x=x, y=y, fill=highestmzinDa))+
+		colnames(highestmz_matrix) = c("x", "y", "highestmzinDa")
-geom_tile() + coord_fixed() +
-ggtitle("Most abundant m/z in each spectrum")+
+		print(ggplot(highestmz_matrix, aes(x=x, y=y, fill=highestmzinDa))+
-theme_bw() +
+		geom_tile() + coord_fixed() +
-theme(plot.title = element_text(hjust = 0.5))+
+		ggtitle("Most abundant m/z in each spectrum")+
-scale_fill_gradientn(colours = c("blue", "purple" , "red","orange"), space = "Lab", na.value = "black", name = "m/z",
+		theme_bw() +
-limits=c(min(highestmz_matrix\$highestmzinDa), max(highestmz_matrix\$highestmzinDa)))+
+		theme(plot.title = element_text(hjust = 0.5))+
-theme(text=element_text(family="ArialMT", face="bold", size=12)))
+		scale_fill_gradientn(colours = c("blue", "purple" , "red","orange"), space = "Lab", na.value = "black", name = "m/z",
+		  limits=c(min(highestmz_matrix\$highestmzinDa), max(highestmz_matrix\$highestmzinDa)))+
-## remove highestmz_matrix to clean up RAM space
+		theme(text=element_text(family="ArialMT", face="bold", size=12)))
-rm(highestmz_matrix)
-gc()
+		## remove highestmz_matrix to clean up RAM space
+		    rm(highestmz_matrix)
+		    gc()
+#end if
 ########################## 8) optional pca image for two components #################
 #if $do_pca:
 print("properties over pixels")
 par(mfrow = c(2,1), mar=c(5,6,4,2))
 ########################## 9) number of peaks per spectrum #################
 ## 9a) scatterplot
-plot_colorByDensity(pixels(msidata), peaksperpixel, ylab = "", xlab = "", main="Number of peaks per spectrum")
+#if $report_depth:
-title(xlab="Spectra index", line=3)
-title(ylab="Number of peaks", line=4)
+		plot_colorByDensity(pixels(msidata), peaksperpixel, ylab = "", xlab = "", main="Number of peaks per spectrum")
+		title(xlab="Spectra index", line=3)
-if (!is.null(unique(msidata\$annotation))){
+		title(ylab="Number of peaks", line=4)
-abline(v=abline_vector, lty = 3)}
+		if (!is.null(unique(msidata\$annotation))){
-## 9b) histogram
+		    abline(v=abline_vector, lty = 3)}
-hist(peaksperpixel, main="", las=1, xlab = "Number of peaks per spectrum", ylab="")
+		## 9b) histogram
-title(main="Number of peaks per spectrum", line=2)
-title(ylab="Frequency = # spectra", line=4)
-abline(v=median(peaksperpixel), col="blue")
+		hist(peaksperpixel, main="", las=1, xlab = "Number of peaks per spectrum", ylab="")
-## 9c) additional histogram to show contribution of annotation groups
+		title(main="Number of peaks per spectrum", line=2)
+		title(ylab="Frequency = # spectra", line=4)
-if (!is.null(unique(msidata\$annotation))){
+		abline(v=median(peaksperpixel), col="blue")
-df_9 = data.frame(peaksperpixel, msidata\$annotation)
+		## 9c) additional histogram to show contribution of annotation groups
-colnames(df_9) = c("Npeaks", "annotation")
+		if (!is.null(unique(msidata\$annotation))){
-hist_9 = ggplot(df_9, aes(x=Npeaks, fill=annotation)) +
-geom_histogram()+ theme_bw()+
+		    df_9 = data.frame(peaksperpixel, msidata\$annotation)
-theme(text=element_text(family="ArialMT", face="bold", size=12))+
+		    colnames(df_9) = c("Npeaks", "annotation")
-theme(plot.title = element_text(hjust = 0.5))+
-theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = 8))+
+		    hist_9 = ggplot(df_9, aes(x=Npeaks, fill=annotation)) +
-theme(legend.position="bottom",legend.direction="vertical")+
+		    geom_histogram()+ theme_bw()+
-labs(title="Number of peaks per spectrum and annotation group", x="Number of peaks per spectrum", y = "Frequency = # spectra") +
+		    theme(text=element_text(family="ArialMT", face="bold", size=12))+
-guides(fill=guide_legend(ncol=5,byrow=TRUE))+
+		    theme(plot.title = element_text(hjust = 0.5))+
-geom_vline(xintercept = median(peaksperpixel), size = 1, colour = "black",linetype = "dashed")
+		    theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = 8))+
-print(hist_9)}
+		    theme(legend.position="bottom",legend.direction="vertical")+
+		    labs(title="Number of peaks per spectrum and annotation group", x="Number of peaks per spectrum", y = "Frequency = # spectra") +
+		    guides(fill=guide_legend(ncol=5,byrow=TRUE))+
+		    geom_vline(xintercept = median(peaksperpixel), size = 1, colour = "black",linetype = "dashed")
+		    print(hist_9)}
+#end if
 ########################## 10) TIC per spectrum ###########################
 ## 10a)density scatterplot
 par(mfrow = c(2,1), mar=c(5,6,4,2))
 par(mfrow = c(1, 1), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
 hist(mz(msidata), xlab = "m/z", main="Histogram of m/z values")
 ########################## 12) Number of peaks per m/z #####################
-peakspermz = rowSums(spectra(msidata) > 0, na.rm=TRUE)
+#if $report_depth:
-par(mfrow = c(2,1), mar=c(5,6,4,4.5))
+		peakspermz = rowSums(spectra(msidata) > 0, na.rm=TRUE)
-## 12a) scatterplot
-plot_colorByDensity(mz(msidata),peakspermz, main= "Number of peaks per m/z", ylab ="")
+		par(mfrow = c(2,1), mar=c(5,6,4,4.5))
-title(xlab="m/z", line=2.5)
+		## 12a) scatterplot
-title(ylab = "Number of peaks", line=4)
+		plot_colorByDensity(mz(msidata),peakspermz, main= "Number of peaks per m/z", ylab ="")
-axis(4, at=pretty(peakspermz),labels=as.character(round((pretty(peakspermz)/pixelcount*100), digits=1)), las=1)
+		title(xlab="m/z", line=2.5)
-mtext("Coverage of spectra [%]", 4, line=3, adj=1)
+		title(ylab = "Number of peaks", line=4)
+		axis(4, at=pretty(peakspermz),labels=as.character(round((pretty(peakspermz)/pixelcount*100), digits=1)), las=1)
-## 12b) histogram
+		mtext("Coverage of spectra [%]", 4, line=3, adj=1)
-hist(peakspermz, main="", las=1, ylab="", xlab="")
-title(ylab = "Frequency", line=4)
+		## 12b) histogram
-title(main="Number of peaks per m/z", xlab = "Number of peaks per m/z", line=2)
+		hist(peakspermz, main="", las=1, ylab="", xlab="")
-abline(v=median(peakspermz), col="blue")
+		title(ylab = "Frequency", line=4)
+		title(main="Number of peaks per m/z", xlab = "Number of peaks per m/z", line=2)
-########################## 13) Sum of intensities per m/z ##################
+		abline(v=median(peakspermz), col="blue")
-## Sum of all intensities for each m/z (like TIC, but for m/z instead of pixel)
+		########################## 13) Sum of intensities per m/z ##################
-mzTIC = featureApply(msidata, sum, na.rm=TRUE) ## calculate intensity sum for each m/z
+		## Sum of all intensities for each m/z (like TIC, but for m/z instead of pixel)
-par(mfrow = c(2,1), mar=c(5,6,4,2))
+		mzTIC = featureApply(msidata, sum, na.rm=TRUE) ## calculate intensity sum for each m/z
-## 13a) scatterplot
-plot_colorByDensity(mz(msidata),mzTIC,  main= "Sum of intensities per m/z", ylab ="")
+		par(mfrow = c(2,1), mar=c(5,6,4,2))
-title(xlab="m/z", line=2.5)
+		## 13a) scatterplot
-title(ylab="Intensity sum", line=4)
+		plot_colorByDensity(mz(msidata),mzTIC,  main= "Sum of intensities per m/z", ylab ="")
+		title(xlab="m/z", line=2.5)
-## 13b) histogram
+		title(ylab="Intensity sum", line=4)
-hist(mzTIC, main="", xlab = "", las=1, ylab="")
-title(main="Sum of intensities per m/z", line=2, ylab="")
+		## 13b) histogram
-title(xlab = "sum of intensities per m/z")
+		hist(mzTIC, main="", xlab = "", las=1, ylab="")
-title(ylab = "Frequency", line=4)
+		title(main="Sum of intensities per m/z", line=2, ylab="")
-abline(v=median(mzTIC[mzTIC>0]), col="blue")
+		title(xlab = "sum of intensities per m/z")
+		title(ylab = "Frequency", line=4)
-################################## V) intensity plots ########################
+		abline(v=median(mzTIC[mzTIC>0]), col="blue")
-############################################################################
-print("intensity plots")
+		################################## V) intensity plots ########################
-########################## 14) Intensity distribution ######################
+		############################################################################
+		print("intensity plots")
-par(mfrow = c(2,1), mar=c(5,6,4,2))
+		########################## 14) Intensity distribution ######################
-## 14a) Median intensity over spectra
+		par(mfrow = c(2,1), mar=c(5,6,4,2))
-medianint_spectra = pixelApply(msidata, median, na.rm=TRUE)
-plot(medianint_spectra, main="Median intensity per spectrum",las=1, xlab="Spectra index", ylab="")
+		## 14a) Median intensity over spectra
-title(ylab="Median spectrum intensity", line=4)
+		medianint_spectra = pixelApply(msidata, median, na.rm=TRUE)
-if (!is.null(unique(msidata\$annotation))){
+		plot(medianint_spectra, main="Median intensity per spectrum",las=1, xlab="Spectra index", ylab="")
-abline(v=abline_vector, lty = 3)}
+		title(ylab="Median spectrum intensity", line=4)
+		if (!is.null(unique(msidata\$annotation))){
-## 14b) histogram:
+		    abline(v=abline_vector, lty = 3)}
-hist(int_matrix, main="", xlab = "", ylab="", las=1)
-title(main="Intensity histogram", line=2)
+		## 14b) histogram:
-title(xlab="intensities")
+		hist(int_matrix, main="", xlab = "", ylab="", las=1)
-title(ylab="Frequency", line=4)
+		title(main="Intensity histogram", line=2)
-abline(v=median(int_matrix)[(as.matrix(spectra(msidata))>0)], col="blue")
+		title(xlab="intensities")
+		title(ylab="Frequency", line=4)
+		abline(v=median(int_matrix)[(as.matrix(spectra(msidata))>0)], col="blue")
+#end if
 ## 14c) histogram to show contribution of annotation groups
 if (!is.null(unique(msidata\$annotation))){
 ############################################################################
 print("Mass spectra and m/z accuracy")
 ############################ 15) Mass spectra ##############################
 ## replace any NA with 0, otherwise plot function will not work at all
 msidata_no_NA = msidata
-## find three equal m/z ranges for the average mass spectra plots:
+#if $report_depth:
-third_mz_range = round(nrow(msidata_no_NA)/3,0)
+		## find three equal m/z ranges for the average mass spectra plots:
-par(cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
+		third_mz_range = round(nrow(msidata_no_NA)/3,0)
-print(plot(msidata_no_NA, run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum", col="black"))
-print(plot(msidata_no_NA[1:third_mz_range,], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))
+		par(cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
-print(plot(msidata_no_NA[third_mz_range:(2*third_mz_range),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))
+		print(plot(msidata_no_NA, run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum", col="black"))
-print(plot(msidata_no_NA[(2*third_mz_range):nrow(msidata_no_NA),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))
+		print(plot(msidata_no_NA[1:third_mz_range,], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))
+		print(plot(msidata_no_NA[third_mz_range:(2*third_mz_range),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))
-## plot one average mass spectrum for each pixel annotation group
+		print(plot(msidata_no_NA[(2*third_mz_range):nrow(msidata_no_NA),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))
-if (!is.null(unique(msidata\$annotation))){
+		## plot one average mass spectrum for each pixel annotation group
-## print legend only for less than 10 samples
-if (length(unique(msidata\$annotation)) < 10){
+		if (!is.null(unique(msidata\$annotation))){
-key_legend = TRUE
+		    ## print legend only for less than 10 samples
-}else{key_legend = FALSE}
+		    if (length(unique(msidata\$annotation)) < 10){
-par(mfrow = c(1,1), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
+		        key_legend = TRUE
-print(plot(msidata, run="infile", pixel.groups=msidata\$annotation, key=key_legend, col=hue_pal()(length(unique(msidata\$annotation))),superpose=TRUE, main="Average mass spectra for annotation groups"))
+		    }else{key_legend = FALSE}
-}
+		    par(mfrow = c(1,1), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
+		    print(plot(msidata, run="infile", pixel.groups=msidata\$annotation, key=key_legend, col=hue_pal()(length(unique(msidata\$annotation))),superpose=TRUE, main="Average mass spectra for annotation groups"))
-## plot 4 random mass spectra
+		}
-## find four random, not empty pixel to plot their spectra in the following plots:
-pixel_vector = sample(which(TICs != 0),4)
+		## plot 4 random mass spectra
+		## find four random, not empty pixel to plot their spectra in the following plots:
-par(mfrow = c(2, 2), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
+		pixel_vector = sample(which(TICs != 0),4)
-print(plot(msidata_no_NA, pixel = pixel_vector, col="black"))
+		par(mfrow = c(2, 2), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
+		print(plot(msidata_no_NA, pixel = pixel_vector, col="black"))
+#end if
 ################### 16) Zoomed in mass spectra for calibrants ##############
 count = 1
 differencevector = numeric()
 rm(msidata_no_NA)
 gc()
 ######### 17) ppm difference input calibrant m/z and m/z with max intensity in given m/z range#########
+#if $report_depth:
 par(mfrow = c(1,1))
 ### plot the ppm difference calculated above: theor. m/z value to highest m/z value:
 calibrant_names = as.character(inputcalibrants[,2])
 theme(plot.title = element_text(hjust = 0.5, size=14))+theme(text=element_text(family="ArialMT", face="bold", size=14))+
 geom_text(aes(label=differencevector2), vjust=-0.3, size=5.5, col="blue")+
 theme(axis.text.x = element_text(angle = 90, hjust = 1, size=14))
 print(diff_plot2)
+#end if
 #################### 19) ppm difference over pixels #####################
 par(mfrow = c(1,1))
 count = 1
 if (!is.null(unique(msidata\$annotation))){
 abline(v=abline_vector, lty = 3)}}
 ### make x-y-images for mz accuracy
+#if $report_depth:
 ppm_dataframe = data.frame(coord(msidata)\$x, coord(msidata)\$y, ppm_df)
 colnames(ppm_dataframe) = c("x", "y", "ppm_df")
 for (each_cal in 1:ncol(ppm_df)){
 tmp_ppm = ppm_dataframe[,c(1,2,each_cal+2)]
 theme_bw() +
 theme(plot.title = element_text(hjust = 0.5))+
 theme(text=element_text(family="ArialMT", face="bold", size=12))+
 scale_fill_gradient2(low = "navy", mid = "grey", high = "red", midpoint = 0 ,space = "Lab", na.value = "black", name = "ppm\nerror"))}
+#end if
 }else{print("plot 16+17+18+19) The inputcalibrant m/z were not provided or outside the m/z range")}
 }else{
 print("inputfile has no intensities > 0")
 }
 </conditional>
 <expand macro="pdf_filename"/>
 <expand macro="reading_2_column_mz_tabular" optional="true"/>
 <param name="plusminus_ppm" value="200" type="float" label="ppm range" help="Will be added in both directions to input calibrant m/z"/>
 <param name="do_pca" type="boolean" label="PCA with 2 components"/>
+<param name="report_depth" type="boolean" label="Generate full QC report" truevalue="TRUE" falsevalue="FALSE" checked="True" help="No: does not generate all plots but only the most informatives"/>
 <repeat name="calibrantratio" title="Plot fold change of two m/z" min="0" max="10">
 <param name="mass1" value="1111" type="float" label="M/z 1" help="First m/z"/>
 <param name="mass2" value="2222" type="float" label="M/z 2" help="Second m/z"/>
 <param name="distance" value="200" type="float" label="ppm range" help="Will be added in both directions to input calibrant m/z and intensities will be averaged in this range."/>
 <param name="filenameratioplot" type="text" optional="true" label="Title" help="Optional title for fold change plot.">
 <param name="distance" value="500"/>
 <param name="filenameratioplot" value = "Ratio of mz 328.9 and mz 398.8"/>
 </repeat>
 <output name="QC_report" file="QC_imzml.pdf" compare="sim_size"/>
 </test>
 <test>
 <expand macro="infile_analyze75"/>
 <conditional name="tabular_annotation">
 <param name="load_annotation" value="no_annotation"/>
 </conditional>
 <param name="filename" value="Testfile_analyze75"/>
 <param name="do_pca" value="True"/>
 <output name="QC_report" file="QC_analyze75.pdf" compare="sim_size"/>
 </test>
 <test>
 <param name="infile" value="3_files_combined.RData" ftype="rdata"/>
 <conditional name="tabular_annotation">
 <param name="load_annotation" value="yes_annotation"/>
 <param name="annotation_file" value="annotations_rdata.tabular"/>
 <param name="name_column" value="2"/>
 <param name="filename" value="Testfile_rdata"/>
 <param name="do_pca" value="False"/>
 <output name="QC_report" file="QC_empty_spectra.pdf" compare="sim_size"/>
 </test>
+<test>
+<param name="infile" value="" ftype="imzml">
+<composite_data value="Example_Processed.imzML"/>
+<composite_data value="Example_Processed.ibd"/>
+</param>
+<conditional name="processed_cond">
+<param name="processed_file" value="processed"/>
+<param name="accuracy" value="200"/>
+<param name="units" value="ppm"/>
+</conditional>
+<conditional name="tabular_annotation">
+<param name="load_annotation" value="no_annotation"/>
+</conditional>
+<param name="calibrant_file" value="inputcalibrantfile1.tabular" ftype="tabular"/>
+<param name="mz_column" value="1"/>
+<param name="name_column" value="1"/>
+<param name="report_depth" value="False"/>
+<output name="QC_report" file="QC_imzml_shortreport.pdf" compare="sim_size"/>
+</test>
 </tests>
 <help>
 <![CDATA[
 @CARDINAL_DESCRIPTION@

Mercurial > repos > galaxyp > cardinal_quality_report

comparison quality_report.xml @ 15:23d0394b5908 draft