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comparison quality_report.xml @ 18:d426a9107a6c draft default tip

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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 91e77c139cb3b7c6d67727dc39140dd79355fa0c
author galaxyp
date Thu, 04 Jul 2024 13:35:30 +0000
parents 23d0394b5908
children
comparison
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17:a7fb9b395ddf 18:d426a9107a6c
1 <tool id="cardinal_quality_report" name="MSI Qualitycontrol" version="@VERSION@.0"> 1 <tool id="cardinal_quality_report" name="MSI Qualitycontrol" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="22.05">
2 <description> 2 <description>
3 mass spectrometry imaging QC 3 mass spectrometry imaging QC
4 </description> 4 </description>
5 <macros> 5 <macros>
6 <import>macros.xml</import> 6 <import>macros.xml</import>
7 </macros> 7 </macros>
8 <expand macro="requirements"> 8 <expand macro="requirements"/>
9 <requirement type="package" version="2.3">r-gridextra</requirement>
10 <requirement type="package" version="3.3.5">r-ggplot2</requirement>
11 <requirement type="package" version="1.1_2">r-rcolorbrewer</requirement>
12 <requirement type="package" version="2.23_20">r-kernsmooth</requirement>
13 <requirement type="package" version="1.1.1">r-scales</requirement>
14 <requirement type="package" version="1.0.12">r-pheatmap</requirement>
15 </expand>
16 <command detect_errors="exit_code"> 9 <command detect_errors="exit_code">
17 <![CDATA[ 10 <![CDATA[
18 @INPUT_LINKING@ 11 @INPUT_LINKING@
19 cat '${cardinal_qualitycontrol_script}' && 12 cat '${cardinal_qualitycontrol_script}' &&
20 Rscript '${cardinal_qualitycontrol_script}' 13 Rscript '${cardinal_qualitycontrol_script}'
39 if (class(msidata) == "MSImageSet"){ 32 if (class(msidata) == "MSImageSet"){
40 msidata = as(msidata, "MSImagingExperiment") 33 msidata = as(msidata, "MSImagingExperiment")
41 run(msidata) = "infile" 34 run(msidata) = "infile"
42 } 35 }
43 36
37
44 ## remove duplicated coordinates 38 ## remove duplicated coordinates
45 msidata <- msidata[,!duplicated(coord(msidata))] 39 msidata <- msidata[,!duplicated(coord(msidata))]
40
46 41
47 ## optional annotation from tabular file to obtain pixel groups (otherwise all pixels are considered to be one sample) 42 ## optional annotation from tabular file to obtain pixel groups (otherwise all pixels are considered to be one sample)
48 43
49 #if str($tabular_annotation.load_annotation) == 'yes_annotation': 44 #if str($tabular_annotation.load_annotation) == 'yes_annotation':
50 45
78 NumemptyTIC = sum(TICs == 0) 73 NumemptyTIC = sum(TICs == 0)
79 ## Median und sd TIC 74 ## Median und sd TIC
80 medTIC = round(median(TICs), digits=1) 75 medTIC = round(median(TICs), digits=1)
81 sdTIC = round(sd(TICs), digits=0) 76 sdTIC = round(sd(TICs), digits=0)
82 ## Median and sd # peaks per spectrum 77 ## Median and sd # peaks per spectrum
83 medpeaks = round(median(colSums(spectra(msidata)>0, na.rm=TRUE), na.rm=TRUE), digits=0) 78 medpeaks = round(median(colSums(as.matrix(spectra(msidata))>0, na.rm=TRUE), na.rm=TRUE), digits=0)
84 sdpeaks = round(sd(colSums(spectra(msidata)>0, na.rm=TRUE), na.rm=TRUE), digits=0) 79 sdpeaks = round(sd(colSums(as.matrix(spectra(msidata))>0, na.rm=TRUE), na.rm=TRUE), digits=0)
85 ##max window size 80 ## max window size
86 max_window = round(mz(msidata)[nrow(msidata)]-mz(msidata)[nrow(msidata)-1], digits=2) 81 max_window = round(mz(msidata)[nrow(msidata)]-mz(msidata)[nrow(msidata)-1], digits=2)
87 ## Processing informations 82 ## Processing informations
88 centroidedinfo = centroided(msidata) 83 centroidedinfo = centroided(msidata)
89 84
85
90 ############## Read and filter tabular file with m/z ########################### 86 ############## Read and filter tabular file with m/z ###########################
91 87
92 ### reading m/z input (calibrant) file: 88 ### reading m/z input (calibrant) file:
93 89
94 #if $calibrant_file: 90 #if $calibrant_file:
95 91
96 calibrant_list = read.delim("$calibrant_file", header = $calibrant_header, na.strings=c(" ","","NA"), stringsAsFactors = FALSE) 92 calibrant_list = read.delim("$calibrant_file", header = $calibrant_header, na.strings=c(" ","","NA"), stringsAsFactors = FALSE)
97 calibrant_list = calibrant_list[,c($mz_column, $name_column)] 93 calibrant_list = calibrant_list[,c($mz_column, $name_column)]
94 calibrant_list = calibrant_list[order(calibrant_list[,1]),]
98 95
99 ### calculate how many input calibrant m/z are valid: 96 ### calculate how many input calibrant m/z are valid:
100 97
101 inputcalibrants = calibrant_list[calibrant_list[,1]>minmz & calibrant_list[,1]<maxmz,] 98 inputcalibrants = calibrant_list[calibrant_list[,1]>minmz & calibrant_list[,1]<maxmz,]
102 number_calibrants_in = length(calibrant_list[,1]) 99 number_calibrants_in = length(calibrant_list[,1])
242 239
243 ## matrix with calibrants in columns and in rows if there is peak intensity in range or not 240 ## matrix with calibrants in columns and in rows if there is peak intensity in range or not
244 pixelmatrix = matrix(ncol=ncol(msidata), nrow = 0) 241 pixelmatrix = matrix(ncol=ncol(msidata), nrow = 0)
245 242
246 ## plot only possible when there is at least one valid calibrant 243 ## plot only possible when there is at least one valid calibrant
244
247 if (length(inputcalibrantmasses) != 0){ 245 if (length(inputcalibrantmasses) != 0){
248 246
249 ## calculate plusminus values in m/z for each calibrant 247 ## calculate plusminus values in m/z for each calibrant
250 plusminusvalues = rep($plusminus_ppm/1000000, length(inputcalibrantmasses))*inputcalibrantmasses 248 plusminusvalues = rep($plusminus_ppm/1000000, length(inputcalibrantmasses))*inputcalibrantmasses
251 249
252 ## filter for m/z window of each calibrant and calculate if sum of peak intensities > 0 250 ## filter for m/z window of each calibrant and calculate if sum of peak intensities > 0
253 251
254 for (mass in 1:length(inputcalibrantmasses)){ 252 for (mass in 1:length(inputcalibrantmasses)){
255 253
256 filtered_data = msidata[mz(msidata) >= inputcalibrantmasses[mass]-plusminusvalues[mass] & mz(msidata) <= inputcalibrantmasses[mass]+plusminusvalues[mass],] 254 filtered_data = msidata[mz(msidata) >= inputcalibrantmasses[mass]-plusminusvalues[mass] & mz(msidata) <= inputcalibrantmasses[mass]+plusminusvalues[mass],]
257 255
258 if (nrow(filtered_data) > 0 & sum(spectra(filtered_data),na.rm=TRUE) > 0){ 256 if (nrow(filtered_data) > 0 & sum(as.matrix(spectra(filtered_data)),na.rm=TRUE) > 0){
259 257
260 ## intensity of all m/z > 0 258 ## intensity of all m/z > 0
261 intensity_sum = colSums(spectra(filtered_data), na.rm=TRUE) > 0 259 intensity_sum = colSums(as.matrix(spectra(filtered_data)[1,]), na.rm=TRUE) > 0
262 260
263 ###}else if(nrow(filtered_data) == 1 & sum(spectra(filtered_data), na.rm=TRUE) > 0){ 261 ###}else if(nrow(filtered_data) == 1 & sum(as.matrix(spectra(filtered_data)), na.rm=TRUE) > 0){
264 262
265 ## intensity of only m/z > 0 263 ## intensity of only m/z > 0
266 intensity_sum = colSums(spectra(filtered_data), na.rm=TRUE) > 0 264 intensity_sum = colSums(as.matrix(spectra(filtered_data)[1,]), na.rm=TRUE) > 0
267 265
268 }else{ 266 }else{
269 267 intensity_sum = rep(FALSE, ncol(filtered_data))}
270 intensity_sum = rep(FALSE, ncol(filtered_data))} 268
271 269 ## for each pixel add sum of intensities > 0 in the given m/z range
272 ## for each pixel add sum of intensities > 0 in the given m/z range 270 pixelmatrix = rbind(pixelmatrix, intensity_sum)
273 pixelmatrix = rbind(pixelmatrix, intensity_sum)
274 } 271 }
272
275 273
276 ## for each pixel count TRUE (each calibrant m/z range with intensity > 0 is TRUE) 274 ## for each pixel count TRUE (each calibrant m/z range with intensity > 0 is TRUE)
277 countvector= as.factor(colSums(pixelmatrix, na.rm=TRUE)) 275 countvector= as.factor(colSums(pixelmatrix, na.rm=TRUE))
278 countdf= data.frame(coord(msidata)\$x, coord(msidata)\$y, countvector) ## add pixel coordinates to counts 276 countdf= data.frame(coord(msidata)\$x, coord(msidata)\$y, countvector) ## add pixel coordinates to counts
279 colnames(countdf) = c("x", "y", "countvector") 277 colnames(countdf) = c("x", "y", "countvector")
290 288
291 ## remove countdf to clean up RAM space 289 ## remove countdf to clean up RAM space
292 rm(countdf) 290 rm(countdf)
293 gc() 291 gc()
294 292
295 }else{print("2) The inputcalibrant m/z were not provided or outside the m/z range")} 293 }else{plot.new()
294 text(0.5, 0.5, "The input calibrant m/z were not provided \n or outside the m/z range.", cex = 1.5)
295 print("2) The input calibrant m/z were not provided or outside the m/z range")}
296 296
297 ########################## 3) fold change image ########################### 297 ########################## 3) fold change image ###########################
298 298
299 #if $calibrantratio: 299 #if $calibrantratio:
300 #for $foldchanges in $calibrantratio: 300 #for $foldchanges in $calibrantratio:
377 ## if there are not enough intensities in the mz range skip creating an image 377 ## if there are not enough intensities in the mz range skip creating an image
378 print(paste0("Not enough intensities > 0 for m/z ", inputcalibrants[,1][mass])) 378 print(paste0("Not enough intensities > 0 for m/z ", inputcalibrants[,1][mass]))
379 } 379 }
380 ) 380 )
381 } 381 }
382 } else {print("4) The input peptide and calibrant m/z were not provided or outside the m/z range")} 382 } else {plot.new()
383 text(0.5, 0.5, "The input peptide and calibrant m/z were not \n provided or outside the m/z range.", cex = 1.5)
384 print("4) The input peptide and calibrant m/z were not provided or outside the m/z range")}
383 385
384 #end if 386 #end if
385 387
386 #################### 5) Number of peaks per pixel - image ################## 388 #################### 5) Number of peaks per pixel - image ##################
387 389
388 ## here every intensity value > 0 counts as peak 390 ## here every intensity value > 0 counts as peak
389 peaksperpixel = colSums(spectra(msidata)> 0, na.rm=TRUE) 391 peaksperpixel = colSums(int_matrix> 0, na.rm=TRUE)
390 peakscoordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, peaksperpixel) 392 peakscoordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, peaksperpixel)
391 colnames(peakscoordarray) = c("x", "y", "peaksperpixel") 393 colnames(peakscoordarray) = c("x", "y", "peaksperpixel")
392 394
393 print(ggplot(peakscoordarray, aes(x=x, y=y, fill=peaksperpixel))+ 395 print(ggplot(peakscoordarray, aes(x=x, y=y, fill=peaksperpixel))+
394 geom_tile() + coord_fixed() + 396 geom_tile() + coord_fixed() +
493 gc() 495 gc()
494 496
495 #end if 497 #end if
496 498
497 ########################## 8) optional pca image for two components ################# 499 ########################## 8) optional pca image for two components #################
498 500 ### PCA removed until next Update
499 #if $do_pca: 501 ####if $do_pca:
500 502
501 set.seed(1) 503 set.seed(1)
502 pca = PCA(msidata, ncomp=2) 504 ##pca = PCA(msidata, ncomp=2)
503 505
504 ## plot overview image and plot and PC1 and 2 images 506 ## plot overview image and plot and PC1 and 2 images
505 print(plot(pca, col=c("black", "darkgrey"), main="PCA for two components", layout=c(2,1), strip=FALSE)) 507 ##print(plot(pca, col=c("black", "darkgrey"), main="PCA for two components", layout=c(2,1), strip=FALSE))
506 print(image(pca, run="infile", col=c("black", "white"), strip=FALSE, ylim= c(maximumy+0.2*maximumy,minimumy-1), layout=FALSE)) 508 ##print(image(pca, run="infile", col=c("black", "white"), strip=FALSE, ylim= c(maximumy+0.2*maximumy,minimumy-1), layout=FALSE))
507 509
508 par(oma=c(0,0,0,1))## margin for image legend 510 ##par(oma=c(0,0,0,1))## margin for image legend
509 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))) 511 ##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)))
510 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))) 512 ##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)))
511 ## remove pca to clean up space 513 ## remove pca to clean up space
512 rm(pca) 514 ##rm(pca)
513 gc() 515 ##gc()
514 516
515 #end if 517 ####end if
516 518
517 ################## III) properties over spectra index ###################### 519 ################## III) properties over spectra index ######################
518 ############################################################################ 520 ############################################################################
519 print("properties over pixels") 521 print("properties over pixels")
520 par(mfrow = c(2,1), mar=c(5,6,4,2)) 522 par(mfrow = c(2,1), mar=c(5,6,4,2))
610 612
611 ########################## 12) Number of peaks per m/z ##################### 613 ########################## 12) Number of peaks per m/z #####################
612 614
613 #if $report_depth: 615 #if $report_depth:
614 616
615 peakspermz = rowSums(spectra(msidata) > 0, na.rm=TRUE) 617 peakspermz = rowSums(int_matrix > 0, na.rm=TRUE)
616 618
617 par(mfrow = c(2,1), mar=c(5,6,4,4.5)) 619 par(mfrow = c(2,1), mar=c(5,6,4,4.5))
618 ## 12a) scatterplot 620 ## 12a) scatterplot
619 plot_colorByDensity(mz(msidata),peakspermz, main= "Number of peaks per m/z", ylab ="") 621 plot_colorByDensity(mz(msidata),peakspermz, main= "Number of peaks per m/z", ylab ="")
620 title(xlab="m/z", line=2.5) 622 title(xlab="m/z", line=2.5)
663 ## 14b) histogram: 665 ## 14b) histogram:
664 hist(int_matrix, main="", xlab = "", ylab="", las=1) 666 hist(int_matrix, main="", xlab = "", ylab="", las=1)
665 title(main="Intensity histogram", line=2) 667 title(main="Intensity histogram", line=2)
666 title(xlab="intensities") 668 title(xlab="intensities")
667 title(ylab="Frequency", line=4) 669 title(ylab="Frequency", line=4)
668 abline(v=median(int_matrix)[(as.matrix(spectra(msidata))>0)], col="blue") 670 abline(v=median(int_matrix)[(int_matrix>0)], col="blue")
669 671
670 #end if 672 #end if
671 673
672 ## 14c) histogram to show contribution of annotation groups 674 ## 14c) histogram to show contribution of annotation groups
673 675
674 if (!is.null(unique(msidata\$annotation))){ 676 if (!is.null(unique(msidata\$annotation))){
675 677
676 df_13 = data.frame(matrix(,ncol=2, nrow=0)) 678 df_13 = data.frame(matrix(,ncol=2, nrow=0))
677 for (subsample in unique(msidata\$annotation)){ 679 for (subsample in unique(msidata\$annotation)){
678 log2_int_subsample = spectra(msidata)[,msidata\$annotation==subsample] 680 log2_int_subsample = int_matrix[,msidata\$annotation==subsample]
679 df_subsample = data.frame(as.numeric(log2_int_subsample)) 681 df_subsample = data.frame(as.numeric(log2_int_subsample))
680 df_subsample\$annotation = subsample 682 df_subsample\$annotation = subsample
681 df_13 = rbind(df_13, df_subsample)} 683 df_13 = rbind(df_13, df_subsample)}
682 df_13\$annotation = as.factor(df_13\$annotation) 684 df_13\$annotation = as.factor(df_13\$annotation)
683 colnames(df_13) = c("int", "annotation") 685 colnames(df_13) = c("int", "annotation")
696 ## 14d) boxplots to visualize in a different way the intensity distributions 698 ## 14d) boxplots to visualize in a different way the intensity distributions
697 par(mfrow = c(1,1), cex.axis=1.3, cex.lab=1.3, mar=c(10,4.1,5.1,2.1)) 699 par(mfrow = c(1,1), cex.axis=1.3, cex.lab=1.3, mar=c(10,4.1,5.1,2.1))
698 700
699 mean_matrix = matrix(,ncol=0, nrow = nrow(msidata)) 701 mean_matrix = matrix(,ncol=0, nrow = nrow(msidata))
700 for (subsample in unique(msidata\$annotation)){ 702 for (subsample in unique(msidata\$annotation)){
701 mean_mz_sample = rowMeans(spectra(msidata)[,msidata\$annotation==subsample],na.rm=TRUE) 703 mean_mz_sample = rowMeans(int_matrix[,msidata\$annotation==subsample],na.rm=TRUE)
702 mean_matrix = cbind(mean_matrix, mean_mz_sample)} 704 mean_matrix = cbind(mean_matrix, mean_mz_sample)}
703 705
704 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") 706 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")
705 (axis(1, at = c(1:number_combined), cex.axis=0.9, labels=unique(msidata\$annotation), las=2)) 707 (axis(1, at = c(1:number_combined), cex.axis=0.9, labels=unique(msidata\$annotation), las=2))
706 708
707 ## 14e) Heatmap of mean intensities of annotation groups 709 ## 14e) Heatmap of mean intensities of annotation groups
708 710
709 colnames(mean_matrix) = unique(msidata\$annotation) 711 colnames(mean_matrix) = unique(msidata\$annotation)
712 print(mean_matrix)
713
710 mean_matrix[is.na(mean_matrix)] = 0 714 mean_matrix[is.na(mean_matrix)] = 0
711 heatmap.parameters <- list(mean_matrix, 715 heatmap.parameters <- list(mean_matrix,
712 show_rownames = T, show_colnames = T, 716 show_rownames = T, show_colnames = T,
713 main = "Heatmap of mean intensities per annotation group") 717 main = "Heatmap of mean intensities per annotation group")
714 par(oma=c(5,0,0,0)) 718 par(oma=c(5,0,0,0))
805 809
806 810
807 ### find m/z with the highest mean intensity in m/z range (red line in plot 16) and calculate ppm difference for plot 17 811 ### find m/z with the highest mean intensity in m/z range (red line in plot 16) and calculate ppm difference for plot 17
808 filtered_data = msidata_no_NA[mz(msidata_no_NA) >= inputcalibrantmasses[mass]-plusminusvalues[mass] & mz(msidata_no_NA) <= inputcalibrantmasses[mass]+plusminusvalues[mass],] 812 filtered_data = msidata_no_NA[mz(msidata_no_NA) >= inputcalibrantmasses[mass]-plusminusvalues[mass] & mz(msidata_no_NA) <= inputcalibrantmasses[mass]+plusminusvalues[mass],]
809 813
810 if (nrow(filtered_data) > 0 & sum(spectra(filtered_data)) > 0){ 814 if (nrow(filtered_data) > 0 & sum(as.matrix(spectra(filtered_data))) > 0){
811 maxmassrow = featureApply(filtered_data, mean) ## for each m/z average intensity is calculated 815 maxmassrow = featureApply(filtered_data, mean) ## for each m/z average intensity is calculated
812 maxvalue = mz(filtered_data)[which.max(maxmassrow)] ### m/z with highest average intensity in m/z range 816 maxvalue = mz(filtered_data)[which.max(maxmassrow)] ### m/z with highest average intensity in m/z range
813 mzdifference = maxvalue - inputcalibrantmasses[mass] ### difference: theoretical value - closest m/z value 817 mzdifference = maxvalue - inputcalibrantmasses[mass] ### difference: theoretical value - closest m/z value
814 ppmdifference = mzdifference/inputcalibrantmasses[mass]*1000000 ### calculate ppm for accuracy measurement 818 ppmdifference = mzdifference/inputcalibrantmasses[mass]*1000000 ### calculate ppm for accuracy measurement
815 }else{ 819 }else{
832 abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3)) 836 abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
833 abline(v=c(maxvalue), col="red", lty=2) 837 abline(v=c(maxvalue), col="red", lty=2)
834 abline(v=c(mzvalue), col="green2", lty=4) 838 abline(v=c(mzvalue), col="green2", lty=4)
835 ## average plot including points per data point 839 ## average plot including points per data point
836 print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=FALSE, strip=FALSE, main="Average spectrum with data points", col="black")) 840 print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=FALSE, strip=FALSE, main="Average spectrum with data points", col="black"))
837 points(mz(msidata_no_NA[minmasspixel1:maxmasspixel1,]), rowMeans(spectra(msidata_no_NA)[minmasspixel1:maxmasspixel1,,drop=FALSE]), col="blue", pch=20) 841 points(mz(msidata_no_NA[minmasspixel1:maxmasspixel1,]), rowMeans(as.matrix(spectra(msidata_no_NA))[minmasspixel1:maxmasspixel1,,drop=FALSE]), col="blue", pch=20)
838 ## plot of third average plot 842 ## plot of third average plot
839 print(plot(msidata_no_NA[minmasspixel2:maxmasspixel2,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum", col="black")) 843 print(plot(msidata_no_NA[minmasspixel2:maxmasspixel2,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum", col="black"))
840 abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3)) 844 abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
841 abline(v=c(maxvalue), col="red", lty=2) 845 abline(v=c(maxvalue), col="red", lty=2)
842 abline(v=c(mzvalue), col="green2", lty=4) 846 abline(v=c(mzvalue), col="green2", lty=4)
910 print(diff_plot2) 914 print(diff_plot2)
911 915
912 #end if 916 #end if
913 917
914 #################### 19) ppm difference over pixels ##################### 918 #################### 19) ppm difference over pixels #####################
919 print("ppm difference over pixels")
915 920
916 par(mfrow = c(1,1)) 921 par(mfrow = c(1,1))
917 count = 1 922 count = 1
918 ppm_df = as.data.frame(matrix(,ncol=0, nrow = ncol(msidata))) 923 ppm_df = as.data.frame(matrix(,ncol=0, nrow = ncol(msidata)))
924
919 for (calibrant in inputcalibrantmasses){ 925 for (calibrant in inputcalibrantmasses){
920 ### find m/z with the highest mean intensity in m/z range, if no m/z in the range, ppm differences for this calibrant will be NA 926 ### find m/z with the highest mean intensity in m/z range, if no m/z in the range, ppm differences for this calibrant will be NA
921 filtered_data = msidata[mz(msidata) >= calibrant-plusminusvalues[count] & mz(msidata) <= calibrant+plusminusvalues[count],] 927 filtered_data = msidata[mz(msidata) >= calibrant-plusminusvalues[count] & mz(msidata) <= calibrant+plusminusvalues[count],]
922 928
923 if (nrow(filtered_data) > 0){ 929 if (nrow(filtered_data) > 0){
976 theme(text=element_text(family="ArialMT", face="bold", size=12))+ 982 theme(text=element_text(family="ArialMT", face="bold", size=12))+
977 scale_fill_gradient2(low = "navy", mid = "grey", high = "red", midpoint = 0 ,space = "Lab", na.value = "black", name = "ppm\nerror"))} 983 scale_fill_gradient2(low = "navy", mid = "grey", high = "red", midpoint = 0 ,space = "Lab", na.value = "black", name = "ppm\nerror"))}
978 984
979 #end if 985 #end if
980 986
981 }else{print("plot 16+17+18+19) The inputcalibrant m/z were not provided or outside the m/z range")} 987 }else{
988 plot.new()
989 text(0.5, 0.5, "plot 16+17+18+19) The input calibrant m/z were not provided \n or outside the m/z range", cex = 1.5)
990 print("plot 16+17+18+19) The input calibrant m/z were not provided or outside the m/z range")}
982 }else{ 991 }else{
983 print("inputfile has no intensities > 0") 992 plot.new()
993 text(0.5, 0.5, "The input file has no intensities > 0", cex = 1.5)
994 print("input file has no intensities > 0")
984 } 995 }
985 dev.off() 996 dev.off()
986 997
987 998
988 ]]></configfile> 999 ]]></configfile>
1000 <when value="no_annotation"/> 1011 <when value="no_annotation"/>
1001 </conditional> 1012 </conditional>
1002 <expand macro="pdf_filename"/> 1013 <expand macro="pdf_filename"/>
1003 <expand macro="reading_2_column_mz_tabular" optional="true"/> 1014 <expand macro="reading_2_column_mz_tabular" optional="true"/>
1004 <param name="plusminus_ppm" value="200" type="float" label="ppm range" help="Will be added in both directions to input calibrant m/z"/> 1015 <param name="plusminus_ppm" value="200" type="float" label="ppm range" help="Will be added in both directions to input calibrant m/z"/>
1005 <param name="do_pca" type="boolean" label="PCA with 2 components"/> 1016 <!--param name="do_pca" type="boolean" label="PCA with 2 components"/-->
1006 <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"/> 1017 <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"/>
1007 <repeat name="calibrantratio" title="Plot fold change of two m/z" min="0" max="10"> 1018 <repeat name="calibrantratio" title="Plot fold change of two m/z" min="0" max="10">
1008 <param name="mass1" value="1111" type="float" label="M/z 1" help="First m/z"/> 1019 <param name="mass1" value="1111" type="float" label="M/z 1" help="First m/z"/>
1009 <param name="mass2" value="2222" type="float" label="M/z 2" help="Second m/z"/> 1020 <param name="mass2" value="2222" type="float" label="M/z 2" help="Second m/z"/>
1010 <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."/> 1021 <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."/>
1038 <param name="calibrant_file" value="inputcalibrantfile1.tabular" ftype="tabular"/> 1049 <param name="calibrant_file" value="inputcalibrantfile1.tabular" ftype="tabular"/>
1039 <param name="mz_column" value="1"/> 1050 <param name="mz_column" value="1"/>
1040 <param name="name_column" value="1"/> 1051 <param name="name_column" value="1"/>
1041 <param name="plusminus_ppm" value="100"/> 1052 <param name="plusminus_ppm" value="100"/>
1042 <param name="filename" value="Testfile_imzml"/> 1053 <param name="filename" value="Testfile_imzml"/>
1043 <param name="do_pca" value="True"/> 1054 <!--param name="do_pca" value="True"/-->
1044 <repeat name="calibrantratio"> 1055 <repeat name="calibrantratio">
1045 <param name="mass1" value="328.9"/> 1056 <param name="mass1" value="328.9"/>
1046 <param name="mass2" value="398.8"/> 1057 <param name="mass2" value="398.8"/>
1047 <param name="distance" value="500"/> 1058 <param name="distance" value="500"/>
1048 <param name="filenameratioplot" value = "Ratio of mz 328.9 and mz 398.8"/> 1059 <param name="filenameratioplot" value = "Ratio of mz 328.9 and mz 398.8"/>
1053 <expand macro="infile_analyze75"/> 1064 <expand macro="infile_analyze75"/>
1054 <conditional name="tabular_annotation"> 1065 <conditional name="tabular_annotation">
1055 <param name="load_annotation" value="no_annotation"/> 1066 <param name="load_annotation" value="no_annotation"/>
1056 </conditional> 1067 </conditional>
1057 <param name="filename" value="Testfile_analyze75"/> 1068 <param name="filename" value="Testfile_analyze75"/>
1058 <param name="do_pca" value="True"/> 1069 <!--param name="do_pca" value="True"/-->
1059 <output name="QC_report" file="QC_analyze75.pdf" compare="sim_size"/> 1070 <output name="QC_report" file="QC_analyze75.pdf" compare="sim_size"/>
1060 </test> 1071 </test>
1061 <test> 1072 <test>
1062 <param name="infile" value="3_files_combined.RData" ftype="rdata"/> 1073 <param name="infile" value="3_files_combined.RData" ftype="rdata"/>
1063 <conditional name="tabular_annotation"> 1074 <conditional name="tabular_annotation">
1071 <param name="calibrant_file" value="inputcalibrantfile1.tabular" ftype="tabular"/> 1082 <param name="calibrant_file" value="inputcalibrantfile1.tabular" ftype="tabular"/>
1072 <param name="mz_column" value="1"/> 1083 <param name="mz_column" value="1"/>
1073 <param name="name_column" value="1"/> 1084 <param name="name_column" value="1"/>
1074 <param name="plusminus_ppm" value="100"/> 1085 <param name="plusminus_ppm" value="100"/>
1075 <param name="filename" value="Testfile_rdata"/> 1086 <param name="filename" value="Testfile_rdata"/>
1076 <param name="do_pca" value="True"/> 1087 <!--param name="do_pca" value="True"/-->
1077 <output name="QC_report" file="QC_rdata.pdf" compare="sim_size"/> 1088 <output name="QC_report" ftype="pdf">
1089 <assert_contents>
1090 <has_size value="1276311" delta="100"/>
1091 </assert_contents>
1092 </output>
1078 </test> 1093 </test>
1079 <test> 1094 <test>
1080 <param name="infile" value="empty_spectra.rdata" ftype="rdata"/> 1095 <param name="infile" value="empty_spectra.rdata" ftype="rdata"/>
1081 <conditional name="tabular_annotation"> 1096 <conditional name="tabular_annotation">
1082 <param name="load_annotation" value="no_annotation"/> 1097 <param name="load_annotation" value="no_annotation"/>
1083 </conditional> 1098 </conditional>
1084 <param name="calibrant_file" value="inputcalibrantfile2.txt"/> 1099 <param name="calibrant_file" value="inputcalibrantfile2.txt"/>
1085 <param name="mz_column" value="1"/> 1100 <param name="mz_column" value="1"/>
1086 <param name="name_column" value="2"/> 1101 <param name="name_column" value="2"/>
1087 <param name="filename" value="Testfile_rdata"/> 1102 <param name="filename" value="Testfile_rdata"/>
1088 <param name="do_pca" value="False"/> 1103 <!--param name="do_pca" value="False"/-->
1089 <output name="QC_report" file="QC_empty_spectra.pdf" compare="sim_size"/> 1104 <output name="QC_report" file="QC_empty_spectra.pdf" compare="sim_size"/>
1090 </test> 1105 </test>
1091 <test> 1106 <test>
1092 <param name="infile" value="" ftype="imzml"> 1107 <param name="infile" value="" ftype="imzml">
1093 <composite_data value="Example_Processed.imzML"/> 1108 <composite_data value="Example_Processed.imzML"/>