# HG changeset patch
# User galaxyp
# Date 1630221409 0
# Node ID e4dda61bb5c862799a4d4ee7b97517c628bf5104
# Parent 060440cf66f35836df14f633c75e2d7ad27b2aac
"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit c8d3adac445b4e08e2724e22d7201bfc38bbf40f"
diff -r 060440cf66f3 -r e4dda61bb5c8 macros.xml
--- a/macros.xml Wed May 13 17:55:17 2020 +0000
+++ b/macros.xml Sun Aug 29 07:16:49 2021 +0000
@@ -1,10 +1,10 @@
- 2.4.0
+ 2.10.0
bioconductor-cardinal
- r-base
+
@@ -117,6 +117,13 @@
0, for if conditions
- npeaks= sum(as.matrix(spectra(msidata))>0, na.rm=TRUE)
+ npeaks= sum(int_matrix>0)
## Number of NA in spectra matrix
- NAcount = sum(is.na(spectra(msidata)))
- ## Number of NA in spectra matrix
- infcount = sum(is.infinite(as.matrix(spectra(msidata))))
+ infcount = sum(is.infinite(int_matrix))
## Number of duplicated coordinates
dupl_coord = sum(duplicated(coord(msidata)))
properties = c("Number of m/z features",
@@ -175,7 +180,7 @@
- imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format `_
- Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
- - Cardinal "MSImageSet" data saved as .RData
+ - Cardinal "MSImageSet" or "MSImagingExperiment" saved as .RData
]]>
-
+
diff -r 060440cf66f3 -r e4dda61bb5c8 quality_report.xml
--- a/quality_report.xml Wed May 13 17:55:17 2020 +0000
+++ b/quality_report.xml Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
-
+
mass spectrometry imaging QC
@@ -7,11 +7,11 @@
r-gridextra
- r-ggplot2
+ r-ggplot2
r-rcolorbrewer
- r-kernsmooth
- r-scales
- r-pheatmap
+ r-kernsmooth
+ r-scales
+ r-pheatmap
0
percpeaks = round(npeaks/numpeaks*100, digits=2)
## Number of empty TICs
-TICs = pixelApply(msidata, sum)
+TICs = pixelApply(msidata, sum, na.rm=TRUE)
NumemptyTIC = sum(TICs == 0)
## Median und sd TIC
medTIC = round(median(TICs), digits=1)
@@ -82,6 +82,8 @@
## Median and sd # peaks per spectrum
medpeaks = round(median(colSums(spectra(msidata)>0, na.rm=TRUE), na.rm=TRUE), digits=0)
sdpeaks = round(sd(colSums(spectra(msidata)>0, na.rm=TRUE), na.rm=TRUE), digits=0)
+##max window size
+max_window = round(mz(msidata)[nrow(msidata)]-mz(msidata)[nrow(msidata)-1], digits=2)
## Processing informations
centroidedinfo = centroided(msidata)
@@ -137,6 +139,7 @@
"Number of empty spectra",
"Median TIC ± sd",
"Median # peaks per spectrum ± sd",
+ "maximum m/z window size",
"Centroided",
paste0("input m/z (#valid/#input) in \n", "$calibrant_file.display_name"))
@@ -145,6 +148,7 @@
paste0(NumemptyTIC),
paste0(medTIC, " ± ", sdTIC),
paste0(medpeaks, " ± ",sdpeaks),
+ paste0(max_window),
paste0(centroidedinfo),
paste0(number_calibrants_valid, " / ", number_calibrants_in))
@@ -183,12 +187,13 @@
### only for previously combined data, same plot as in combine QC pdf
- if (!is.null(levels(msidata\$annotation))){
+ if (!is.null(unique(msidata\$annotation))){
- number_combined = length(levels(msidata\$annotation))
+ number_combined = length(unique(msidata\$annotation))
position_df = data.frame(coord(msidata)\$x, coord(msidata)\$y, msidata\$annotation)
colnames(position_df) = c("x", "y","annotation")
+ print(position_df)
combine_plot = ggplot(position_df, aes(x=x, y=y, fill=annotation))+
geom_tile() +
@@ -220,7 +225,7 @@
pixelxyarray=data.frame(coord(msidata)\$x, coord(msidata)\$y,pixelnumber)
colnames(pixelxyarray) = c("x", "y", "pixelnumber")
gg_title = "Pixel order"
-
+
print(ggplot(pixelxyarray, aes(x=x, y=y, fill=pixelnumber))+
geom_tile() + coord_fixed()+
ggtitle(gg_title) + theme_bw()+
@@ -354,24 +359,29 @@
#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){
- for (mass in 1:length(inputcalibrants[,1])){
- par(oma=c(0,0,0,1))## margin for image legend
+
+ #if $report_depth:
+
+ par(mfrow=c(1,1), mar=c(5.1, 4.1, 4.1, 2.1), mgp=c(3, 1, 0), las=0)
+ if (length(inputcalibrants[,1]) != 0){
+ for (mass in 1:length(inputcalibrants[,1])){
+ par(oma=c(0,0,0,1))## margin for image legend
- tryCatch(
- {
- print(image(msidata, mz=inputcalibrants[,1][mass], plusminus=plusminusvalues[mass],
- main= paste0(inputcalibrants[,2][mass], ": ", round(inputcalibrants[,1][mass], digits = 2)," (±",$plusminus_ppm, " ppm)"),
- contrast.enhance = "histogram", strip=FALSE, ylim= c(maximumy,minimumy)))
- },
- 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")}
+ tryCatch(
+ {
+ print(image(msidata, mz=inputcalibrants[,1][mass], plusminus=plusminusvalues[mass],
+ main= paste0(inputcalibrants[,2][mass], ": ", round(inputcalibrants[,1][mass], digits = 2)," (±",$plusminus_ppm, " ppm)"),
+ contrast.enhance = "histogram", strip=FALSE, ylim= c(maximumy,minimumy)))
+ },
+ 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")}
+
+ #end if
#################### 5) Number of peaks per pixel - image ##################
@@ -414,72 +424,75 @@
############################### 6b) median int image ###############################
- median_int = pixelApply(msidata, median)
+ #if $report_depth:
+
+ median_int = pixelApply(msidata, median, na.rm=TRUE)
- median_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, median_int)
- colnames(median_coordarray) = c("x", "y", "median_int")
- print(ggplot(median_coordarray, aes(x=x, y=y, fill=median_int))+
- geom_tile() + coord_fixed() +
- ggtitle("Median intensity per spectrum")+
- theme_bw() +
- 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 = "median\nintensity"))
+ median_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, median_int)
+ colnames(median_coordarray) = c("x", "y", "median_int")
+ print(ggplot(median_coordarray, aes(x=x, y=y, fill=median_int))+
+ geom_tile() + coord_fixed() +
+ ggtitle("Median intensity per spectrum")+
+ theme_bw() +
+ 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 = "median\nintensity"))
- ## remove median_coordarray to clean up RAM space
- rm(median_coordarray)
- gc()
+ ## remove median_coordarray to clean up RAM space
+ rm(median_coordarray)
+ gc()
- ############################### 6c) max int image ###############################
-
- max_int = pixelApply(msidata, max)
+ ############################### 6c) max int image ###############################
+
+ max_int = pixelApply(msidata, max, na.rm=TRUE)
- max_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, max_int)
- colnames(max_coordarray) = c("x", "y", "max_int")
- print(ggplot(max_coordarray, aes(x=x, y=y, fill=max_int))+
- geom_tile() + coord_fixed() +
- ggtitle("Maximum intensity per spectrum")+
- theme_bw() +
- 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 = "max\nintensity"))
+ max_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, max_int)
+ colnames(max_coordarray) = c("x", "y", "max_int")
+ print(ggplot(max_coordarray, aes(x=x, y=y, fill=max_int))+
+ geom_tile() + coord_fixed() +
+ ggtitle("Maximum intensity per spectrum")+
+ theme_bw() +
+ 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 = "max\nintensity"))
- ## remove median_coordarray to clean up RAM space
- rm(max_coordarray)
- gc()
+ ## remove median_coordarray to clean up RAM space
+ rm(max_coordarray)
+ gc()
+
+ ############################### 7) Most abundant m/z image #################
+
+ ## for each spectrum find the row (m/z) with the highest intensity
+ highestmz = pixelApply(msidata, which.max)
- ############################### 7) Most abundant m/z image #################
-
- ## for each spectrum find the row (m/z) with the highest intensity
- highestmz = pixelApply(msidata, which.max)
+ ## in case for some spectra max returns integer(0), highestmz is a list and integer(0) have to be replaced with NA and unlisted
+ if (class(highestmz) == "list"){
+ ##find zero-length values
+ zero_entry <- !(sapply(highestmz, length))
+ ### replace these values with NA
+ highestmz[zero_entry] <- NA
+ ### unlist list to get a vector
+ highestmz = unlist(highestmz)}
- ## in case for some spectra max returns integer(0), highestmz is a list and integer(0) have to be replaced with NA and unlisted
- if (class(highestmz) == "list"){
- ##find zero-length values
- zero_entry <- !(sapply(highestmz, length))
- ### replace these values with NA
- highestmz[zero_entry] <- NA
- ### unlist list to get a vector
- highestmz = unlist(highestmz)}
-
- highestmz_matrix = data.frame(coord(msidata)\$x, coord(msidata)\$y,mz(msidata)[highestmz])
- colnames(highestmz_matrix) = c("x", "y", "highestmzinDa")
+ highestmz_matrix = data.frame(coord(msidata)\$x, coord(msidata)\$y,mz(msidata)[highestmz])
+ colnames(highestmz_matrix) = c("x", "y", "highestmzinDa")
- print(ggplot(highestmz_matrix, aes(x=x, y=y, fill=highestmzinDa))+
- geom_tile() + coord_fixed() +
- ggtitle("Most abundant m/z in each spectrum")+
- theme_bw() +
- theme(plot.title = element_text(hjust = 0.5))+
- 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)))+
- theme(text=element_text(family="ArialMT", face="bold", size=12)))
+ print(ggplot(highestmz_matrix, aes(x=x, y=y, fill=highestmzinDa))+
+ geom_tile() + coord_fixed() +
+ ggtitle("Most abundant m/z in each spectrum")+
+ theme_bw() +
+ theme(plot.title = element_text(hjust = 0.5))+
+ 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)))+
+ theme(text=element_text(family="ArialMT", face="bold", size=12)))
- ## remove highestmz_matrix to clean up RAM space
- 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 #################
@@ -495,7 +508,7 @@
par(oma=c(0,0,0,1))## margin for image legend
print(image(pca, column = "PC1" , strip=FALSE, superpose = FALSE, main="PC1", col.regions = risk.colors(100), layout=c(2,1), ylim= c(maximumy+0.2*maximumy,minimumy-1)))
print(image(pca, column = "PC2" , strip=FALSE, superpose = FALSE, main="PC2", col.regions = risk.colors(100), layout=FALSE, ylim= c(maximumy+0.2*maximumy,minimumy-1)))
- ## remove pca to clean up RAM space
+ ## remove pca to clean up space
rm(pca)
gc()
@@ -508,38 +521,44 @@
########################## 9) number of peaks per spectrum #################
## 9a) scatterplot
+
+ #if $report_depth:
- plot_colorByDensity(pixels(msidata), peaksperpixel, ylab = "", xlab = "", main="Number of peaks per spectrum")
- 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)
+ title(ylab="Number of peaks", line=4)
- if (!is.null(levels(msidata\$annotation))){
- abline(v=abline_vector, lty = 3)}
-
- ## 9b) histogram
+ if (!is.null(unique(msidata\$annotation))){
+ abline(v=abline_vector, lty = 3)}
+
+ ## 9b) histogram
+
- hist(peaksperpixel, main="", las=1, xlab = "Number of peaks per spectrum", ylab="")
- 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="")
+ title(main="Number of peaks per spectrum", line=2)
+ title(ylab="Frequency = # spectra", line=4)
+ abline(v=median(peaksperpixel), col="blue")
- ## 9c) additional histogram to show contribution of annotation groups
+ ## 9c) additional histogram to show contribution of annotation groups
- if (!is.null(levels(msidata\$annotation))){
-
- df_9 = data.frame(peaksperpixel, msidata\$annotation)
- 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()+
- theme(text=element_text(family="ArialMT", face="bold", size=12))+
- theme(plot.title = element_text(hjust = 0.5))+
- theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = 8))+
- 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)}
+ df_9 = data.frame(peaksperpixel, msidata\$annotation)
+ colnames(df_9) = c("Npeaks", "annotation")
+
+ hist_9 = ggplot(df_9, aes(x=Npeaks, fill=annotation)) +
+ geom_histogram()+ theme_bw()+
+ theme(text=element_text(family="ArialMT", face="bold", size=12))+
+ theme(plot.title = element_text(hjust = 0.5))+
+ theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = 8))+
+ 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 ###########################
@@ -555,17 +574,17 @@
title(xlab="Spectra index", line=3)
title(ylab = "Total ion current intensity", line=4)
- if (!is.null(levels(msidata\$annotation))){
+ if (!is.null(unique(msidata\$annotation))){
abline(v=abline_vector, lty = 3)}
## 10b) histogram
- hist((TICs), main="", las=1, xlab = "TIC per spectrum", ylab="")
+ hist(TICs, main="", las=1, xlab = "TIC per spectrum", ylab="")
title(main= "TIC per spectrum", line=2)
title(ylab="Frequency = # spectra", line=4)
abline(v=median(TICs[TICs>0]), col="blue")
## 10c) additional histogram to show annotation contributions
- if (!is.null(levels(msidata\$annotation))){
+ if (!is.null(unique(msidata\$annotation))){
df_10 = data.frame((TICs), msidata\$annotation)
colnames(df_10) = c("TICs", "annotation")
@@ -591,68 +610,71 @@
########################## 12) Number of peaks per m/z #####################
- peakspermz = rowSums(spectra(msidata) > 0, na.rm=TRUE)
+ #if $report_depth:
+
+ peakspermz = rowSums(spectra(msidata) > 0, na.rm=TRUE)
- par(mfrow = c(2,1), mar=c(5,6,4,4.5))
- ## 12a) scatterplot
- plot_colorByDensity(mz(msidata),peakspermz, main= "Number of peaks per m/z", ylab ="")
- title(xlab="m/z", line=2.5)
- title(ylab = "Number of peaks", line=4)
- axis(4, at=pretty(peakspermz),labels=as.character(round((pretty(peakspermz)/pixelcount*100), digits=1)), las=1)
- mtext("Coverage of spectra [%]", 4, line=3, adj=1)
+ par(mfrow = c(2,1), mar=c(5,6,4,4.5))
+ ## 12a) scatterplot
+ plot_colorByDensity(mz(msidata),peakspermz, main= "Number of peaks per m/z", ylab ="")
+ title(xlab="m/z", line=2.5)
+ title(ylab = "Number of peaks", line=4)
+ axis(4, at=pretty(peakspermz),labels=as.character(round((pretty(peakspermz)/pixelcount*100), digits=1)), las=1)
+ mtext("Coverage of spectra [%]", 4, line=3, adj=1)
- ## 12b) histogram
- hist(peakspermz, main="", las=1, ylab="", xlab="")
- title(ylab = "Frequency", line=4)
- title(main="Number of peaks per m/z", xlab = "Number of peaks per m/z", line=2)
- abline(v=median(peakspermz), col="blue")
+ ## 12b) histogram
+ hist(peakspermz, main="", las=1, ylab="", xlab="")
+ title(ylab = "Frequency", line=4)
+ title(main="Number of peaks per m/z", xlab = "Number of peaks per m/z", line=2)
+ abline(v=median(peakspermz), col="blue")
- ########################## 13) Sum of intensities per m/z ##################
+ ########################## 13) Sum of intensities per m/z ##################
- ## Sum of all intensities for each m/z (like TIC, but for m/z instead of pixel)
- 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)
+ mzTIC = featureApply(msidata, sum, na.rm=TRUE) ## calculate intensity sum for each m/z
- par(mfrow = c(2,1), mar=c(5,6,4,2))
- ## 13a) scatterplot
- plot_colorByDensity(mz(msidata),mzTIC, main= "Sum of intensities per m/z", ylab ="")
- title(xlab="m/z", line=2.5)
- title(ylab="Intensity sum", line=4)
+ par(mfrow = c(2,1), mar=c(5,6,4,2))
+ ## 13a) scatterplot
+ plot_colorByDensity(mz(msidata),mzTIC, main= "Sum of intensities per m/z", ylab ="")
+ title(xlab="m/z", line=2.5)
+ title(ylab="Intensity sum", line=4)
- ## 13b) histogram
- hist(mzTIC, main="", xlab = "", las=1, ylab="")
- title(main="Sum of intensities per m/z", line=2, ylab="")
- title(xlab = "sum of intensities per m/z")
- title(ylab = "Frequency", line=4)
- abline(v=median(mzTIC[mzTIC>0]), col="blue")
+ ## 13b) histogram
+ hist(mzTIC, main="", xlab = "", las=1, ylab="")
+ title(main="Sum of intensities per m/z", line=2, ylab="")
+ title(xlab = "sum of intensities per m/z")
+ title(ylab = "Frequency", line=4)
+ abline(v=median(mzTIC[mzTIC>0]), col="blue")
- ################################## V) intensity plots ########################
- ############################################################################
- print("intensity plots")
- ########################## 14) Intensity distribution ######################
+ ################################## V) intensity plots ########################
+ ############################################################################
+ print("intensity plots")
+ ########################## 14) Intensity distribution ######################
- par(mfrow = c(2,1), mar=c(5,6,4,2))
+ par(mfrow = c(2,1), mar=c(5,6,4,2))
- ## 14a) Median intensity over spectra
- medianint_spectra = pixelApply(msidata, median)
- plot(medianint_spectra, main="Median intensity per spectrum",las=1, xlab="Spectra index", ylab="")
- title(ylab="Median spectrum intensity", line=4)
- if (!is.null(levels(msidata\$annotation))){
- abline(v=abline_vector, lty = 3)}
+ ## 14a) Median intensity over spectra
+ medianint_spectra = pixelApply(msidata, median, na.rm=TRUE)
+ plot(medianint_spectra, main="Median intensity per spectrum",las=1, xlab="Spectra index", ylab="")
+ title(ylab="Median spectrum intensity", line=4)
+ if (!is.null(unique(msidata\$annotation))){
+ abline(v=abline_vector, lty = 3)}
- ## 14b) histogram:
- hist(as.matrix(spectra(msidata)), main="", xlab = "", ylab="", las=1)
- title(main="Intensity histogram", line=2)
- title(xlab="intensities")
- title(ylab="Frequency", line=4)
- abline(v=median(as.matrix(spectra(msidata))[(as.matrix(spectra(msidata))>0)], na.rm=TRUE), col="blue")
+ ## 14b) histogram:
+ hist(int_matrix, main="", xlab = "", ylab="", las=1)
+ title(main="Intensity histogram", line=2)
+ 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(levels(msidata\$annotation))){
+ if (!is.null(unique(msidata\$annotation))){
df_13 = data.frame(matrix(,ncol=2, nrow=0))
- for (subsample in levels(msidata\$annotation)){
+ for (subsample in unique(msidata\$annotation)){
log2_int_subsample = spectra(msidata)[,msidata\$annotation==subsample]
df_subsample = data.frame(as.numeric(log2_int_subsample))
df_subsample\$annotation = subsample
@@ -668,43 +690,43 @@
theme(legend.position="bottom",legend.direction="vertical")+
theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = 8))+
guides(fill=guide_legend(ncol=5,byrow=TRUE))+
- geom_vline(xintercept = median(spectra(msidata)[(spectra(msidata)>0)]), size = 1, colour = "black",linetype = "dashed")
+ geom_vline(xintercept = median(int_matrix)[(int_matrix>0)], size = 1, colour = "black",linetype = "dashed")
print(hist_13)
## 14d) boxplots to visualize in a different way the intensity distributions
- par(mfrow = c(1,1), cex.axis=1.3, cex.lab=1.3, mar=c(13.1,4.1,5.1,2.1))
+ par(mfrow = c(1,1), cex.axis=1.3, cex.lab=1.3, mar=c(10,4.1,5.1,2.1))
mean_matrix = matrix(,ncol=0, nrow = nrow(msidata))
- for (subsample in levels(msidata\$annotation)){
+ for (subsample in unique(msidata\$annotation)){
mean_mz_sample = rowMeans(spectra(msidata)[,msidata\$annotation==subsample],na.rm=TRUE)
mean_matrix = cbind(mean_matrix, mean_mz_sample)}
-
- boxplot(log10(mean_matrix), ylab = "Log10 mean intensity per m/z", main="Log10 mean m/z intensities per annotation group", xaxt = "n")
- (axis(1, at = c(1:number_combined), labels=levels(msidata\$annotation), las=2))
+
+ boxplot(log10(as.data.frame(mean_matrix)), ylab = "Log10 mean intensity per m/z", main="Log10 mean m/z intensities per annotation group", xaxt = "n")
+ (axis(1, at = c(1:number_combined), cex.axis=0.9, labels=unique(msidata\$annotation), las=2))
## 14e) Heatmap of mean intensities of annotation groups
- colnames(mean_matrix) = levels(msidata\$annotation)
+ colnames(mean_matrix) = unique(msidata\$annotation)
mean_matrix[is.na(mean_matrix)] = 0
heatmap.parameters <- list(mean_matrix,
show_rownames = T, show_colnames = T,
main = "Heatmap of mean intensities per annotation group")
- par(oma=c(3,0,0,0))
- print(heatmap(mean_matrix),margins = c(10, 10))
+ par(oma=c(5,0,0,0))
+ heatmap(mean_matrix)
## 14f) PCA of mean intensities of annotation groups
-
+ par(mar=c(4.1, 4.1, 4.1, 8.5))
## define annotation by colour
- annotation_colour = rainbow(length(levels(msidata\$annotation)))[as.factor(levels(msidata\$annotation))]
+ annotation_colour = rainbow(length(unique(msidata\$annotation)))[as.factor(unique(msidata\$annotation))]
## transform and scale dataframe
pca = prcomp(t(mean_matrix),center=FALSE,scale.=FALSE)
## plot single plot
plot(pca\$x[,c(1,2)],col=annotation_colour,pch=19)
+ legend("topright",xpd=TRUE, bty="n", inset=c(-0.3,0), cex=0.8, legend=unique(msidata\$annotation), col=rainbow(length(unique(msidata\$annotation))), pch=19)
## plot pca with colours for max first 5 PCs
pc_comp = ifelse(ncol(pca\$x)<5 , ncol(pca\$x), 5)
pairs(pca\$x[,1:pc_comp],col=annotation_colour,pch=19)
- legend("bottom", horiz = TRUE, legend=levels(msidata\$annotation), col=rainbow(length(levels(msidata\$annotation))), pch=19)
}
@@ -714,36 +736,40 @@
############################ 15) Mass spectra ##############################
+
## replace any NA with 0, otherwise plot function will not work at all
msidata_no_NA = msidata
+
+ #if $report_depth:
- ## find three equal m/z ranges for the average mass spectra plots:
- third_mz_range = round(nrow(msidata_no_NA)/3,0)
+ ## find three equal m/z ranges for the average mass spectra plots:
+ third_mz_range = round(nrow(msidata_no_NA)/3,0)
- par(cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
- print(plot(msidata_no_NA, run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum"))
- print(plot(msidata_no_NA[1:third_mz_range,], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum"))
- print(plot(msidata_no_NA[third_mz_range:(2*third_mz_range),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum"))
- print(plot(msidata_no_NA[(2*third_mz_range):nrow(msidata_no_NA),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum"))
+ par(cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
+ 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"))
+ 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[(2*third_mz_range):nrow(msidata_no_NA),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))
- ## plot one average mass spectrum for each pixel annotation group
+ ## plot one average mass spectrum for each pixel annotation group
- if (!is.null(levels(msidata\$annotation))){
- ## print legend only for less than 10 samples
- if (length(levels(msidata\$annotation)) < 10){
- key_legend = TRUE
- }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(levels(msidata\$annotation))),superpose=TRUE, main="Average mass spectra for annotation groups"))
- }
+ if (!is.null(unique(msidata\$annotation))){
+ ## print legend only for less than 10 samples
+ if (length(unique(msidata\$annotation)) < 10){
+ key_legend = TRUE
+ }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:
+ pixel_vector = sample(which(TICs != 0),4)
- 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))
+ 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 ##############
@@ -753,6 +779,7 @@
if (length(inputcalibrantmasses) != 0){
+
### calculate plusminus values in m/z for each calibrant, this is used for all following plots
plusminusvalues = rep($plusminus_ppm/1000000, length(inputcalibrantmasses)) * inputcalibrantmasses
@@ -765,6 +792,17 @@
maxmasspixel2 = features(msidata_no_NA, mz=inputcalibrantmasses[mass]+0.5)
minmasspixel3 = features(msidata_no_NA, mz=inputcalibrantmasses[mass]-1.5)
maxmasspixel3 = features(msidata_no_NA, mz=inputcalibrantmasses[mass]+3)
+
+ ## test if some values are lower than min(mz)
+ minmasspixel1 = ifelse(length(minmasspixel1)>0, minmasspixel1, 1)
+ minmasspixel2 = ifelse(length(minmasspixel2)>0, minmasspixel2, 1)
+ minmasspixel3 = ifelse(length(minmasspixel3)>0, minmasspixel3, 1)
+
+ ## test if min and max are same (more likely for centroided data):
+ maxmasspixel1 = ifelse(minmasspixel1 != maxmasspixel1, maxmasspixel1, maxmasspixel1 + 1)
+ maxmasspixel2 = ifelse(minmasspixel2 != maxmasspixel2, maxmasspixel2, maxmasspixel1 + 1)
+ maxmasspixel3 = ifelse(minmasspixel3 != maxmasspixel3, maxmasspixel3, maxmasspixel1 + 1)
+
### find m/z with the highest mean intensity in m/z range (red line in plot 16) and calculate ppm difference for plot 17
filtered_data = msidata_no_NA[mz(msidata_no_NA) >= inputcalibrantmasses[mass]-plusminusvalues[mass] & mz(msidata_no_NA) <= inputcalibrantmasses[mass]+plusminusvalues[mass],]
@@ -790,20 +828,20 @@
par(oma=c(0,0,2,0))
## average plot
- print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum"))
+ print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum", col="black"))
abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
abline(v=c(maxvalue), col="red", lty=2)
abline(v=c(mzvalue), col="green2", lty=4)
## average plot including points per data point
- print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=FALSE, strip=FALSE, main="Average spectrum with data points"))
+ print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=FALSE, strip=FALSE, main="Average spectrum with data points", col="black"))
points(mz(msidata_no_NA[minmasspixel1:maxmasspixel1,]), rowMeans(spectra(msidata_no_NA)[minmasspixel1:maxmasspixel1,,drop=FALSE]), col="blue", pch=20)
## plot of third average plot
- print(plot(msidata_no_NA[minmasspixel2:maxmasspixel2,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum"))
+ print(plot(msidata_no_NA[minmasspixel2:maxmasspixel2,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum", col="black"))
abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
abline(v=c(maxvalue), col="red", lty=2)
abline(v=c(mzvalue), col="green2", lty=4)
## plot of fourth average plot
- print(plot(msidata_no_NA[minmasspixel3:maxmasspixel3,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum"))
+ print(plot(msidata_no_NA[minmasspixel3:maxmasspixel3,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum", col="black"))
abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
abline(v=c(maxvalue), col="red", lty=2)
abline(v=c(mzvalue), col="green2", lty=4)
@@ -813,7 +851,7 @@
### 16b) one large extra plot with different colours for different pixel annotation groups
- if (!is.null(levels(msidata\$annotation))){
+ if (!is.null(unique(msidata\$annotation))){
if (number_combined < 10){
key_zoomed = TRUE
}else{key_zoomed = FALSE}
@@ -831,12 +869,16 @@
######### 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])
+
diff_df = data.frame(differencevector, calibrant_names)
+
if (sum(is.na(diff_df[,1])) == nrow(diff_df)){
plot(0,type='n',axes=FALSE,ann=FALSE)
title(main=paste("plot 17: no peaks in the chosen region, repeat with higher ppm range"))
@@ -866,6 +908,8 @@
theme(axis.text.x = element_text(angle = 90, hjust = 1, size=14))
print(diff_plot2)
+
+ #end if
#################### 19) ppm difference over pixels #####################
@@ -910,11 +954,12 @@
for (each_cal in 1:ncol(ppm_df)){
lines(ppm_df[,each_cal], col=mycolours[each_cal], type="p")}
legend("topright", inset=c(-0.2,0), xpd = TRUE, bty="n", cex=0.8,legend=inputcalibrantmasses, col=mycolours[1:ncol(ppm_df)],lty=1)
- if (!is.null(levels(msidata\$annotation))){
+ 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")
@@ -931,6 +976,7 @@
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{
@@ -957,6 +1003,7 @@
+
@@ -982,7 +1029,7 @@
-
+
@@ -1002,7 +1049,6 @@
-
@@ -1012,7 +1058,6 @@
-
@@ -1043,6 +1088,25 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
-
+
+
@@ -42,7 +42,7 @@
-
+
diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/112_auto_combined.imzml.txt
--- a/test-data/112_auto_combined.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/112_auto_combined.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 556
--rw-r--r-- 1 meli meli 537552 Apr 21 12:46 ibd
--rw-r--r-- 1 meli meli 27497 Apr 21 12:46 imzml
+-rw-rw-r-- 1 meli meli 537552 Aug 30 13:50 ibd
+-rw-rw-r-- 1 meli meli 27497 Aug 30 13:50 imzml
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/123_combined.imzml
--- a/test-data/123_combined.imzml Wed May 13 17:55:17 2020 +0000
+++ b/test-data/123_combined.imzml Sun Aug 29 07:16:49 2021 +0000
@@ -9,8 +9,8 @@
-
-
+
+
@@ -42,7 +42,7 @@
-
+
diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/123_combined.imzml.txt
--- a/test-data/123_combined.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/123_combined.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 452
--rw-r--r-- 1 meli meli 436764 Apr 21 12:41 ibd
--rw-r--r-- 1 meli meli 22855 Apr 21 12:41 imzml
+-rw-rw-r-- 1 meli meli 436764 Aug 30 13:46 ibd
+-rw-rw-r-- 1 meli meli 22855 Aug 30 13:46 imzml
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/123_combined_auto.imzml
--- a/test-data/123_combined_auto.imzml Wed May 13 17:55:17 2020 +0000
+++ b/test-data/123_combined_auto.imzml Sun Aug 29 07:16:49 2021 +0000
@@ -9,8 +9,8 @@
-
-
+
+
@@ -42,7 +42,7 @@
-
+
diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/123_combined_auto.imzml.txt
--- a/test-data/123_combined_auto.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/123_combined_auto.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 452
--rw-r--r-- 1 meli meli 436764 Apr 21 12:43 ibd
--rw-r--r-- 1 meli meli 22860 Apr 21 12:43 imzml
+-rw-rw-r-- 1 meli meli 436764 Aug 30 13:48 ibd
+-rw-rw-r-- 1 meli meli 22860 Aug 30 13:48 imzml
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/12_combined.imzml
--- a/test-data/12_combined.imzml Wed May 13 17:55:17 2020 +0000
+++ b/test-data/12_combined.imzml Sun Aug 29 07:16:49 2021 +0000
@@ -9,8 +9,8 @@
-
-
+
+
@@ -42,7 +42,7 @@
-
+
diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/12_combined.imzml.txt
--- a/test-data/12_combined.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/12_combined.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 352
--rw-r--r-- 1 meli meli 335976 Apr 21 12:45 ibd
--rw-r--r-- 1 meli meli 18225 Apr 21 12:45 imzml
+-rw-rw-r-- 1 meli meli 335976 Aug 30 13:49 ibd
+-rw-rw-r-- 1 meli meli 18225 Aug 30 13:49 imzml
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/2123_auto_combined.imzml
--- a/test-data/2123_auto_combined.imzml Wed May 13 17:55:17 2020 +0000
+++ b/test-data/2123_auto_combined.imzml Sun Aug 29 07:16:49 2021 +0000
@@ -9,8 +9,8 @@
-
-
+
+
@@ -42,7 +42,7 @@
-
+
diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/2123_auto_combined.imzml.txt
--- a/test-data/2123_auto_combined.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/2123_auto_combined.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 656
--rw-r--r-- 1 meli meli 638340 Apr 21 12:46 ibd
--rw-r--r-- 1 meli meli 32132 Apr 21 12:46 imzml
+-rw-rw-r-- 1 meli meli 638340 Aug 30 13:50 ibd
+-rw-rw-r-- 1 meli meli 32132 Aug 30 13:50 imzml
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/out3.imzml
--- a/test-data/out3.imzml Wed May 13 17:55:17 2020 +0000
+++ b/test-data/out3.imzml Sun Aug 29 07:16:49 2021 +0000
@@ -9,8 +9,8 @@
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+++ b/test-data/out3.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 24
--rw-r--r-- 1 meli meli 9616 Mär 24 20:25 ibd
--rw-r--r-- 1 meli meli 8958 Mär 24 20:25 imzml
+-rw-rw-r-- 1 meli meli 9616 Sep 27 10:51 ibd
+-rw-rw-r-- 1 meli meli 8958 Sep 27 10:51 imzml
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--- a/test-data/out4.imzml Wed May 13 17:55:17 2020 +0000
+++ b/test-data/out4.imzml Sun Aug 29 07:16:49 2021 +0000
@@ -9,8 +9,8 @@
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/out4.imzml.txt
--- a/test-data/out4.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/out4.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 44
--rw-r--r-- 1 meli meli 28792 Mär 24 19:23 ibd
--rw-r--r-- 1 meli meli 12046 Mär 24 19:23 imzml
+-rw-rw-r-- 1 meli meli 28792 Sep 27 10:52 ibd
+-rw-rw-r-- 1 meli meli 12046 Sep 27 10:52 imzml
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--- a/test-data/out5.imzml Wed May 13 17:55:17 2020 +0000
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+++ b/test-data/out5.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 20
--rw-r--r-- 1 meli meli 380 Mär 24 19:24 ibd
--rw-r--r-- 1 meli meli 13525 Mär 24 19:24 imzml
+-rw-rw-r-- 1 meli meli 380 Sep 27 10:53 ibd
+-rw-rw-r-- 1 meli meli 13525 Sep 27 10:53 imzml
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+++ b/test-data/out6.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 164
--rw-r--r-- 1 meli meli 146896 Mär 24 19:25 ibd
--rw-r--r-- 1 meli meli 18221 Mär 24 19:25 imzml
+-rw-rw-r-- 1 meli meli 146896 Sep 27 10:53 ibd
+-rw-rw-r-- 1 meli meli 18221 Sep 27 10:53 imzml
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/out7.imzml.txt
--- a/test-data/out7.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/out7.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 116
--rw-r--r-- 1 meli meli 95976 Mär 24 19:26 ibd
--rw-r--r-- 1 meli meli 18199 Mär 24 19:26 imzml
+-rw-rw-r-- 1 meli meli 95976 Sep 27 10:54 ibd
+-rw-rw-r-- 1 meli meli 18199 Sep 27 10:54 imzml
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--- a/test-data/out8.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/out8.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
-total 348
--rw-r--r-- 1 meli meli 335976 Mär 24 19:27 ibd
--rw-r--r-- 1 meli meli 12402 Mär 24 19:27 imzml
+total 260
+-rw-rw-r-- 1 meli meli 248824 Sep 27 11:44 ibd
+-rw-rw-r-- 1 meli meli 12397 Sep 27 11:44 imzml
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--- a/test-data/picked.imzml Wed May 13 17:55:17 2020 +0000
+++ b/test-data/picked.imzml Sun Aug 29 07:16:49 2021 +0000
@@ -8,8 +8,8 @@
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/picked.imzml.txt
--- a/test-data/picked.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/picked.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 192
--rw-r--r-- 1 meli meli 157984 Apr 21 12:42 ibd
--rw-r--r-- 1 meli meli 35012 Apr 21 12:42 imzml
+-rw-rw-r-- 1 meli meli 157984 Aug 30 13:47 ibd
+-rw-rw-r-- 1 meli meli 35012 Aug 30 13:47 imzml
diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/picked_QC.pdf
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--- a/test-data/pixels_test6.tabular Wed May 13 17:55:17 2020 +0000
+++ b/test-data/pixels_test6.tabular Sun Aug 29 07:16:49 2021 +0000
@@ -1,25 +1,25 @@
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--- a/test-data/preprocessing_results1.imzml Wed May 13 17:55:17 2020 +0000
+++ b/test-data/preprocessing_results1.imzml Sun Aug 29 07:16:49 2021 +0000
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/preprocessing_results1.imzml.txt
--- a/test-data/preprocessing_results1.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/preprocessing_results1.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 24
--rw-r--r-- 1 meli meli 216 Mai 10 17:37 ibd
--rw-r--r-- 1 meli meli 18090 Mai 10 17:37 imzml
+-rw-rw-r-- 1 meli meli 336 Nov 29 01:14 ibd
+-rw-rw-r-- 1 meli meli 18092 Nov 29 01:14 imzml
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--- a/test-data/preprocessing_results2.imzml Wed May 13 17:55:17 2020 +0000
+++ b/test-data/preprocessing_results2.imzml Sun Aug 29 07:16:49 2021 +0000
@@ -9,8 +9,8 @@
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/preprocessing_results2.imzml.txt
--- a/test-data/preprocessing_results2.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/preprocessing_results2.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 64
--rw-r--r-- 1 meli meli 37404 Mai 10 17:38 ibd
--rw-r--r-- 1 meli meli 22796 Mai 10 17:38 imzml
+-rw-rw-r-- 1 meli meli 37404 Nov 29 18:03 ibd
+-rw-rw-r-- 1 meli meli 22796 Nov 29 18:03 imzml
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+++ b/test-data/preprocessing_results3.imzml Sun Aug 29 07:16:49 2021 +0000
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/preprocessing_results3.imzml.txt
--- a/test-data/preprocessing_results3.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/preprocessing_results3.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 24
--rw-r--r-- 1 meli meli 960 Mai 10 17:39 ibd
--rw-r--r-- 1 meli meli 18112 Mai 10 17:39 imzml
+-rw-rw-r-- 1 meli meli 1456 Nov 29 18:04 ibd
+-rw-rw-r-- 1 meli meli 18133 Nov 29 18:04 imzml
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@@ -9,8 +9,8 @@
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/preprocessing_results4.imzml.txt
--- a/test-data/preprocessing_results4.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/preprocessing_results4.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 84
--rw-r--r-- 1 meli meli 63256 Mai 10 17:40 ibd
--rw-r--r-- 1 meli meli 18199 Mai 10 17:40 imzml
+-rw-rw-r-- 1 meli meli 62696 Aug 28 16:41 ibd
+-rw-rw-r-- 1 meli meli 18200 Aug 28 16:41 imzml
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--- a/test-data/preprocessing_results5.imzml Wed May 13 17:55:17 2020 +0000
+++ b/test-data/preprocessing_results5.imzml Sun Aug 29 07:16:49 2021 +0000
@@ -9,8 +9,8 @@
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diff -r 060440cf66f3 -r e4dda61bb5c8 test-data/preprocessing_results5.imzml.txt
--- a/test-data/preprocessing_results5.imzml.txt Wed May 13 17:55:17 2020 +0000
+++ b/test-data/preprocessing_results5.imzml.txt Sun Aug 29 07:16:49 2021 +0000
@@ -1,4 +1,4 @@
imzML file:
total 428
--rw-r--r-- 1 meli meli 415936 Mai 10 17:40 ibd
--rw-r--r-- 1 meli meli 18569 Mai 10 17:40 imzml
+-rw-rw-r-- 1 meli meli 415936 Oct 5 19:59 ibd
+-rw-rw-r-- 1 meli meli 18569 Oct 5 19:59 imzml
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