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comparison quality_report.xml @ 11:f396c176f366 draft

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"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit ca89f8e007c6b17f7c30066729e05b8686ab975a"
author galaxyp
date Sun, 27 Sep 2020 11:11:53 +0000
parents f365bad862c9
children ecaebe7c7b54
comparison
equal deleted inserted replaced
10:f365bad862c9 11:f396c176f366
58 msidata_coordinates = data.frame(coord(msidata)\$x, coord(msidata)\$y, c(1:ncol(msidata))) 58 msidata_coordinates = data.frame(coord(msidata)\$x, coord(msidata)\$y, c(1:ncol(msidata)))
59 colnames(msidata_coordinates) = c("x", "y", "pixel_index") 59 colnames(msidata_coordinates) = c("x", "y", "pixel_index")
60 merged_annotation = merge(msidata_coordinates, annotation_input, by=c("x", "y"), all.x=TRUE) 60 merged_annotation = merge(msidata_coordinates, annotation_input, by=c("x", "y"), all.x=TRUE)
61 merged_annotation[is.na(merged_annotation)] = "NA" 61 merged_annotation[is.na(merged_annotation)] = "NA"
62 merged_annotation = merged_annotation[order(merged_annotation\$pixel_index),] 62 merged_annotation = merged_annotation[order(merged_annotation\$pixel_index),]
63 msidata\$annotation = as.factor(merged_annotation[,4]) 63 msidata\$annotation = as.character(merged_annotation[,4])
64 64
65 #end if 65 #end if
66 66
67 ###################### calculation of data properties ################################ 67 ###################### calculation of data properties ################################
68 @DATA_PROPERTIES_INRAM@ 68 @DATA_PROPERTIES_INRAM@
69 69
70
70 ## Median intensities 71 ## Median intensities
71 medint = round(median(spectra(msidata), na.rm=TRUE), digits=2) 72 medint = round(median(int_matrix), digits=2)
72 ## Spectra multiplied with m/z (potential number of peaks) 73 ## Spectra multiplied with m/z (potential number of peaks)
73 numpeaks = ncol(msidata)*nrow(msidata) 74 numpeaks = as.numeric(ncol(msidata)*nrow(msidata))
74 ## Percentage of intensities > 0 75 ## Percentage of intensities > 0
75 percpeaks = round(npeaks/numpeaks*100, digits=2) 76 percpeaks = round(npeaks/numpeaks*100, digits=2)
76 ## Number of empty TICs 77 ## Number of empty TICs
77 TICs = pixelApply(msidata, sum) 78 TICs = pixelApply(msidata, sum, na.rm=TRUE)
78 NumemptyTIC = sum(TICs == 0) 79 NumemptyTIC = sum(TICs == 0)
79 ## Median und sd TIC 80 ## Median und sd TIC
80 medTIC = round(median(TICs), digits=1) 81 medTIC = round(median(TICs), digits=1)
81 sdTIC = round(sd(TICs), digits=0) 82 sdTIC = round(sd(TICs), digits=0)
82 ## Median and sd # peaks per spectrum 83 ## Median and sd # peaks per spectrum
181 182
182 ################### 0) overview for combined data ########################### 183 ################### 0) overview for combined data ###########################
183 184
184 ### only for previously combined data, same plot as in combine QC pdf 185 ### only for previously combined data, same plot as in combine QC pdf
185 186
186 if (!is.null(levels(msidata\$annotation))){ 187 if (!is.null(unique(msidata\$annotation))){
187 188
188 number_combined = length(levels(msidata\$annotation)) 189 number_combined = length(unique(msidata\$annotation))
189 190
190 position_df = data.frame(coord(msidata)\$x, coord(msidata)\$y, msidata\$annotation) 191 position_df = data.frame(coord(msidata)\$x, coord(msidata)\$y, msidata\$annotation)
191 colnames(position_df) = c("x", "y","annotation") 192 colnames(position_df) = c("x", "y","annotation")
193 print(position_df)
192 194
193 combine_plot = ggplot(position_df, aes(x=x, y=y, fill=annotation))+ 195 combine_plot = ggplot(position_df, aes(x=x, y=y, fill=annotation))+
194 geom_tile() + 196 geom_tile() +
195 coord_fixed()+ 197 coord_fixed()+
196 ggtitle("Spatial orientation of pixel annotations")+ 198 ggtitle("Spatial orientation of pixel annotations")+
412 rm(TICcoordarray) 414 rm(TICcoordarray)
413 gc() 415 gc()
414 416
415 ############################### 6b) median int image ############################### 417 ############################### 6b) median int image ###############################
416 418
417 median_int = pixelApply(msidata, median) 419 median_int = pixelApply(msidata, median, na.rm=TRUE)
418 420
419 median_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, median_int) 421 median_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, median_int)
420 colnames(median_coordarray) = c("x", "y", "median_int") 422 colnames(median_coordarray) = c("x", "y", "median_int")
421 print(ggplot(median_coordarray, aes(x=x, y=y, fill=median_int))+ 423 print(ggplot(median_coordarray, aes(x=x, y=y, fill=median_int))+
422 geom_tile() + coord_fixed() + 424 geom_tile() + coord_fixed() +
431 rm(median_coordarray) 433 rm(median_coordarray)
432 gc() 434 gc()
433 435
434 ############################### 6c) max int image ############################### 436 ############################### 6c) max int image ###############################
435 437
436 max_int = pixelApply(msidata, max) 438 max_int = pixelApply(msidata, max, na.rm=TRUE)
437 439
438 max_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, max_int) 440 max_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, max_int)
439 colnames(max_coordarray) = c("x", "y", "max_int") 441 colnames(max_coordarray) = c("x", "y", "max_int")
440 print(ggplot(max_coordarray, aes(x=x, y=y, fill=max_int))+ 442 print(ggplot(max_coordarray, aes(x=x, y=y, fill=max_int))+
441 geom_tile() + coord_fixed() + 443 geom_tile() + coord_fixed() +
493 print(image(pca, run="infile", col=c("black", "white"), strip=FALSE, ylim= c(maximumy+0.2*maximumy,minimumy-1), layout=FALSE)) 495 print(image(pca, run="infile", col=c("black", "white"), strip=FALSE, ylim= c(maximumy+0.2*maximumy,minimumy-1), layout=FALSE))
494 496
495 par(oma=c(0,0,0,1))## margin for image legend 497 par(oma=c(0,0,0,1))## margin for image legend
496 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))) 498 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)))
497 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))) 499 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)))
498 ## remove pca to clean up RAM space 500 ## remove pca to clean up space
499 rm(pca) 501 rm(pca)
500 gc() 502 gc()
501 503
502 #end if 504 #end if
503 505
511 513
512 plot_colorByDensity(pixels(msidata), peaksperpixel, ylab = "", xlab = "", main="Number of peaks per spectrum") 514 plot_colorByDensity(pixels(msidata), peaksperpixel, ylab = "", xlab = "", main="Number of peaks per spectrum")
513 title(xlab="Spectra index", line=3) 515 title(xlab="Spectra index", line=3)
514 title(ylab="Number of peaks", line=4) 516 title(ylab="Number of peaks", line=4)
515 517
516 if (!is.null(levels(msidata\$annotation))){ 518 if (!is.null(unique(msidata\$annotation))){
517 abline(v=abline_vector, lty = 3)} 519 abline(v=abline_vector, lty = 3)}
518 520
519 ## 9b) histogram 521 ## 9b) histogram
520 522
521 hist(peaksperpixel, main="", las=1, xlab = "Number of peaks per spectrum", ylab="") 523 hist(peaksperpixel, main="", las=1, xlab = "Number of peaks per spectrum", ylab="")
523 title(ylab="Frequency = # spectra", line=4) 525 title(ylab="Frequency = # spectra", line=4)
524 abline(v=median(peaksperpixel), col="blue") 526 abline(v=median(peaksperpixel), col="blue")
525 527
526 ## 9c) additional histogram to show contribution of annotation groups 528 ## 9c) additional histogram to show contribution of annotation groups
527 529
528 if (!is.null(levels(msidata\$annotation))){ 530 if (!is.null(unique(msidata\$annotation))){
529 531
530 df_9 = data.frame(peaksperpixel, msidata\$annotation) 532 df_9 = data.frame(peaksperpixel, msidata\$annotation)
531 colnames(df_9) = c("Npeaks", "annotation") 533 colnames(df_9) = c("Npeaks", "annotation")
532 534
533 hist_9 = ggplot(df_9, aes(x=Npeaks, fill=annotation)) + 535 hist_9 = ggplot(df_9, aes(x=Npeaks, fill=annotation)) +
534 geom_histogram()+ theme_bw()+ 536 geom_histogram()+ theme_bw()+
535 theme(text=element_text(family="ArialMT", face="bold", size=12))+ 537 theme(text=element_text(family="ArialMT", face="bold", size=12))+
536 theme(plot.title = element_text(hjust = 0.5))+ 538 theme(plot.title = element_text(hjust = 0.5))+
537 theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = 8))+ 539 theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = 8))+
553 plot_colorByDensity(pixels(msidata), TICs, ylab = "", xlab = "", main="TIC per spectrum") 555 plot_colorByDensity(pixels(msidata), TICs, ylab = "", xlab = "", main="TIC per spectrum")
554 } 556 }
555 557
556 title(xlab="Spectra index", line=3) 558 title(xlab="Spectra index", line=3)
557 title(ylab = "Total ion current intensity", line=4) 559 title(ylab = "Total ion current intensity", line=4)
558 if (!is.null(levels(msidata\$annotation))){ 560 if (!is.null(unique(msidata\$annotation))){
559 abline(v=abline_vector, lty = 3)} 561 abline(v=abline_vector, lty = 3)}
560 562
561 ## 10b) histogram 563 ## 10b) histogram
562 hist((TICs), main="", las=1, xlab = "TIC per spectrum", ylab="") 564 hist(TICs, main="", las=1, xlab = "TIC per spectrum", ylab="")
563 title(main= "TIC per spectrum", line=2) 565 title(main= "TIC per spectrum", line=2)
564 title(ylab="Frequency = # spectra", line=4) 566 title(ylab="Frequency = # spectra", line=4)
565 abline(v=median(TICs[TICs>0]), col="blue") 567 abline(v=median(TICs[TICs>0]), col="blue")
566 568
567 ## 10c) additional histogram to show annotation contributions 569 ## 10c) additional histogram to show annotation contributions
568 if (!is.null(levels(msidata\$annotation))){ 570 if (!is.null(unique(msidata\$annotation))){
569 df_10 = data.frame((TICs), msidata\$annotation) 571 df_10 = data.frame((TICs), msidata\$annotation)
570 colnames(df_10) = c("TICs", "annotation") 572 colnames(df_10) = c("TICs", "annotation")
571 573
572 hist_10 = ggplot(df_10, aes(x=TICs, fill=annotation)) + 574 hist_10 = ggplot(df_10, aes(x=TICs, fill=annotation)) +
573 geom_histogram()+ theme_bw()+ 575 geom_histogram()+ theme_bw()+
631 ########################## 14) Intensity distribution ###################### 633 ########################## 14) Intensity distribution ######################
632 634
633 par(mfrow = c(2,1), mar=c(5,6,4,2)) 635 par(mfrow = c(2,1), mar=c(5,6,4,2))
634 636
635 ## 14a) Median intensity over spectra 637 ## 14a) Median intensity over spectra
636 medianint_spectra = pixelApply(msidata, median) 638 medianint_spectra = pixelApply(msidata, median, na.rm=TRUE)
637 plot(medianint_spectra, main="Median intensity per spectrum",las=1, xlab="Spectra index", ylab="") 639 plot(medianint_spectra, main="Median intensity per spectrum",las=1, xlab="Spectra index", ylab="")
638 title(ylab="Median spectrum intensity", line=4) 640 title(ylab="Median spectrum intensity", line=4)
639 if (!is.null(levels(msidata\$annotation))){ 641 if (!is.null(unique(msidata\$annotation))){
640 abline(v=abline_vector, lty = 3)} 642 abline(v=abline_vector, lty = 3)}
641 643
642 ## 14b) histogram: 644 ## 14b) histogram:
643 hist(as.matrix(spectra(msidata)), main="", xlab = "", ylab="", las=1) 645 hist(int_matrix, main="", xlab = "", ylab="", las=1)
644 title(main="Intensity histogram", line=2) 646 title(main="Intensity histogram", line=2)
645 title(xlab="intensities") 647 title(xlab="intensities")
646 title(ylab="Frequency", line=4) 648 title(ylab="Frequency", line=4)
647 abline(v=median(as.matrix(spectra(msidata))[(as.matrix(spectra(msidata))>0)], na.rm=TRUE), col="blue") 649 abline(v=median(int_matrix)[(as.matrix(spectra(msidata))>0)], col="blue")
648 650
649 651
650 ## 14c) histogram to show contribution of annotation groups 652 ## 14c) histogram to show contribution of annotation groups
651 653
652 if (!is.null(levels(msidata\$annotation))){ 654 if (!is.null(unique(msidata\$annotation))){
653 655
654 df_13 = data.frame(matrix(,ncol=2, nrow=0)) 656 df_13 = data.frame(matrix(,ncol=2, nrow=0))
655 for (subsample in levels(msidata\$annotation)){ 657 for (subsample in unique(msidata\$annotation)){
656 log2_int_subsample = spectra(msidata)[,msidata\$annotation==subsample] 658 log2_int_subsample = spectra(msidata)[,msidata\$annotation==subsample]
657 df_subsample = data.frame(as.numeric(log2_int_subsample)) 659 df_subsample = data.frame(as.numeric(log2_int_subsample))
658 df_subsample\$annotation = subsample 660 df_subsample\$annotation = subsample
659 df_13 = rbind(df_13, df_subsample)} 661 df_13 = rbind(df_13, df_subsample)}
660 df_13\$annotation = as.factor(df_13\$annotation) 662 df_13\$annotation = as.factor(df_13\$annotation)
666 labs(title="Intensities per sample", x="intensities", y = "Frequency") + 668 labs(title="Intensities per sample", x="intensities", y = "Frequency") +
667 theme(plot.title = element_text(hjust = 0.5))+ 669 theme(plot.title = element_text(hjust = 0.5))+
668 theme(legend.position="bottom",legend.direction="vertical")+ 670 theme(legend.position="bottom",legend.direction="vertical")+
669 theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = 8))+ 671 theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = 8))+
670 guides(fill=guide_legend(ncol=5,byrow=TRUE))+ 672 guides(fill=guide_legend(ncol=5,byrow=TRUE))+
671 geom_vline(xintercept = median(spectra(msidata)[(spectra(msidata)>0)]), size = 1, colour = "black",linetype = "dashed") 673 geom_vline(xintercept = median(int_matrix)[(int_matrix>0)], size = 1, colour = "black",linetype = "dashed")
672 print(hist_13) 674 print(hist_13)
673 675
674 ## 14d) boxplots to visualize in a different way the intensity distributions 676 ## 14d) boxplots to visualize in a different way the intensity distributions
675 par(mfrow = c(1,1), cex.axis=1.3, cex.lab=1.3, mar=c(13.1,4.1,5.1,2.1)) 677 par(mfrow = c(1,1), cex.axis=1.3, cex.lab=1.3, mar=c(10,4.1,5.1,2.1))
676 678
677 mean_matrix = matrix(,ncol=0, nrow = nrow(msidata)) 679 mean_matrix = matrix(,ncol=0, nrow = nrow(msidata))
678 for (subsample in levels(msidata\$annotation)){ 680 for (subsample in unique(msidata\$annotation)){
679 mean_mz_sample = rowMeans(spectra(msidata)[,msidata\$annotation==subsample],na.rm=TRUE) 681 mean_mz_sample = rowMeans(spectra(msidata)[,msidata\$annotation==subsample],na.rm=TRUE)
680 mean_matrix = cbind(mean_matrix, mean_mz_sample)} 682 mean_matrix = cbind(mean_matrix, mean_mz_sample)}
681 683
682 boxplot(log10(mean_matrix), ylab = "Log10 mean intensity per m/z", main="Log10 mean m/z intensities per annotation group", xaxt = "n") 684 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")
683 (axis(1, at = c(1:number_combined), labels=levels(msidata\$annotation), las=2)) 685 (axis(1, at = c(1:number_combined), cex.axis=0.9, labels=unique(msidata\$annotation), las=2))
684 686
685 ## 14e) Heatmap of mean intensities of annotation groups 687 ## 14e) Heatmap of mean intensities of annotation groups
686 688
687 colnames(mean_matrix) = levels(msidata\$annotation) 689 colnames(mean_matrix) = unique(msidata\$annotation)
688 mean_matrix[is.na(mean_matrix)] = 0 690 mean_matrix[is.na(mean_matrix)] = 0
689 heatmap.parameters <- list(mean_matrix, 691 heatmap.parameters <- list(mean_matrix,
690 show_rownames = T, show_colnames = T, 692 show_rownames = T, show_colnames = T,
691 main = "Heatmap of mean intensities per annotation group") 693 main = "Heatmap of mean intensities per annotation group")
692 par(oma=c(3,0,0,0)) 694 par(oma=c(5,0,0,0))
693 print(heatmap(mean_matrix),margins = c(10, 10)) 695 heatmap(mean_matrix)
694 696
695 697
696 ## 14f) PCA of mean intensities of annotation groups 698 ## 14f) PCA of mean intensities of annotation groups
697 699 par(mar=c(4.1, 4.1, 4.1, 8.5))
698 ## define annotation by colour 700 ## define annotation by colour
699 annotation_colour = rainbow(length(levels(msidata\$annotation)))[as.factor(levels(msidata\$annotation))] 701 annotation_colour = rainbow(length(unique(msidata\$annotation)))[as.factor(unique(msidata\$annotation))]
700 ## transform and scale dataframe 702 ## transform and scale dataframe
701 pca = prcomp(t(mean_matrix),center=FALSE,scale.=FALSE) 703 pca = prcomp(t(mean_matrix),center=FALSE,scale.=FALSE)
702 ## plot single plot 704 ## plot single plot
703 plot(pca\$x[,c(1,2)],col=annotation_colour,pch=19) 705 plot(pca\$x[,c(1,2)],col=annotation_colour,pch=19)
706 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)
704 ## plot pca with colours for max first 5 PCs 707 ## plot pca with colours for max first 5 PCs
705 pc_comp = ifelse(ncol(pca\$x)<5 , ncol(pca\$x), 5) 708 pc_comp = ifelse(ncol(pca\$x)<5 , ncol(pca\$x), 5)
706 pairs(pca\$x[,1:pc_comp],col=annotation_colour,pch=19) 709 pairs(pca\$x[,1:pc_comp],col=annotation_colour,pch=19)
707 legend("bottom", horiz = TRUE, legend=levels(msidata\$annotation), col=rainbow(length(levels(msidata\$annotation))), pch=19)
708 710
709 } 711 }
710 712
711 ################################## VI) Mass spectra and m/z accuracy ######################## 713 ################################## VI) Mass spectra and m/z accuracy ########################
712 ############################################################################ 714 ############################################################################
719 721
720 ## find three equal m/z ranges for the average mass spectra plots: 722 ## find three equal m/z ranges for the average mass spectra plots:
721 third_mz_range = round(nrow(msidata_no_NA)/3,0) 723 third_mz_range = round(nrow(msidata_no_NA)/3,0)
722 724
723 par(cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1)) 725 par(cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
724 print(plot(msidata_no_NA, run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum")) 726 print(plot(msidata_no_NA, run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum", col="black"))
725 print(plot(msidata_no_NA[1:third_mz_range,], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum")) 727 print(plot(msidata_no_NA[1:third_mz_range,], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))
726 print(plot(msidata_no_NA[third_mz_range:(2*third_mz_range),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum")) 728 print(plot(msidata_no_NA[third_mz_range:(2*third_mz_range),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))
727 print(plot(msidata_no_NA[(2*third_mz_range):nrow(msidata_no_NA),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum")) 729 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"))
728 730
729 ## plot one average mass spectrum for each pixel annotation group 731 ## plot one average mass spectrum for each pixel annotation group
730 732
731 if (!is.null(levels(msidata\$annotation))){ 733 if (!is.null(unique(msidata\$annotation))){
732 ## print legend only for less than 10 samples 734 ## print legend only for less than 10 samples
733 if (length(levels(msidata\$annotation)) < 10){ 735 if (length(unique(msidata\$annotation)) < 10){
734 key_legend = TRUE 736 key_legend = TRUE
735 }else{key_legend = FALSE} 737 }else{key_legend = FALSE}
736 par(mfrow = c(1,1), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1)) 738 par(mfrow = c(1,1), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
737 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")) 739 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"))
738 } 740 }
739 741
740 ## plot 4 random mass spectra 742 ## plot 4 random mass spectra
741 ## find four random, not empty pixel to plot their spectra in the following plots: 743 ## find four random, not empty pixel to plot their spectra in the following plots:
742 pixel_vector = sample(which(TICs != 0),4) 744 pixel_vector = sample(which(TICs != 0),4)
743 745
744 par(mfrow = c(2, 2), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1)) 746 par(mfrow = c(2, 2), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
745 print(plot(msidata_no_NA, pixel = pixel_vector)) 747 print(plot(msidata_no_NA, pixel = pixel_vector, col="black"))
746 748
747 749
748 ################### 16) Zoomed in mass spectra for calibrants ############## 750 ################### 16) Zoomed in mass spectra for calibrants ##############
749 751
750 count = 1 752 count = 1
788 790
789 ## plotting of 4 spectra in one page 791 ## plotting of 4 spectra in one page
790 par(oma=c(0,0,2,0)) 792 par(oma=c(0,0,2,0))
791 ## average plot 793 ## average plot
792 794
793 print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum")) 795 print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum", col="black"))
794 abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3)) 796 abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
795 abline(v=c(maxvalue), col="red", lty=2) 797 abline(v=c(maxvalue), col="red", lty=2)
796 abline(v=c(mzvalue), col="green2", lty=4) 798 abline(v=c(mzvalue), col="green2", lty=4)
797 ## average plot including points per data point 799 ## average plot including points per data point
798 print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=FALSE, strip=FALSE, main="Average spectrum with data points")) 800 print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=FALSE, strip=FALSE, main="Average spectrum with data points", col="black"))
799 points(mz(msidata_no_NA[minmasspixel1:maxmasspixel1,]), rowMeans(spectra(msidata_no_NA)[minmasspixel1:maxmasspixel1,,drop=FALSE]), col="blue", pch=20) 801 points(mz(msidata_no_NA[minmasspixel1:maxmasspixel1,]), rowMeans(spectra(msidata_no_NA)[minmasspixel1:maxmasspixel1,,drop=FALSE]), col="blue", pch=20)
800 ## plot of third average plot 802 ## plot of third average plot
801 print(plot(msidata_no_NA[minmasspixel2:maxmasspixel2,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum")) 803 print(plot(msidata_no_NA[minmasspixel2:maxmasspixel2,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum", col="black"))
802 abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3)) 804 abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
803 abline(v=c(maxvalue), col="red", lty=2) 805 abline(v=c(maxvalue), col="red", lty=2)
804 abline(v=c(mzvalue), col="green2", lty=4) 806 abline(v=c(mzvalue), col="green2", lty=4)
805 ## plot of fourth average plot 807 ## plot of fourth average plot
806 print(plot(msidata_no_NA[minmasspixel3:maxmasspixel3,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum")) 808 print(plot(msidata_no_NA[minmasspixel3:maxmasspixel3,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum", col="black"))
807 abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3)) 809 abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
808 abline(v=c(maxvalue), col="red", lty=2) 810 abline(v=c(maxvalue), col="red", lty=2)
809 abline(v=c(mzvalue), col="green2", lty=4) 811 abline(v=c(mzvalue), col="green2", lty=4)
810 title(paste0("theor. m/z: ", round(inputcalibrants[count,1], digits=4)), col.main="blue", outer=TRUE, line=0, adj=0.074) 812 title(paste0("theor. m/z: ", round(inputcalibrants[count,1], digits=4)), col.main="blue", outer=TRUE, line=0, adj=0.074)
811 title(paste0("most abundant m/z: ", round(maxvalue, digits=4)), col.main="red", outer=TRUE, line=0, adj=0.49) 813 title(paste0("most abundant m/z: ", round(maxvalue, digits=4)), col.main="red", outer=TRUE, line=0, adj=0.49)
812 title(paste0("closest m/z: ", round(mzvalue, digits=4)), col.main="green2", outer=TRUE, line=0, adj=0.93) 814 title(paste0("closest m/z: ", round(mzvalue, digits=4)), col.main="green2", outer=TRUE, line=0, adj=0.93)
813 815
814 ### 16b) one large extra plot with different colours for different pixel annotation groups 816 ### 16b) one large extra plot with different colours for different pixel annotation groups
815 817
816 if (!is.null(levels(msidata\$annotation))){ 818 if (!is.null(unique(msidata\$annotation))){
817 if (number_combined < 10){ 819 if (number_combined < 10){
818 key_zoomed = TRUE 820 key_zoomed = TRUE
819 }else{key_zoomed = FALSE} 821 }else{key_zoomed = FALSE}
820 par(mfrow = c(1, 1)) 822 par(mfrow = c(1, 1))
821 print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", strip=FALSE,main="Average spectrum per annotation group", 823 print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", strip=FALSE,main="Average spectrum per annotation group",
908 910
909 plot(0,0,type="n", ylim=c(min(ppm_df, na.rm=TRUE),max(ppm_df, na.rm=TRUE)), xlim = c(1,ncol(filtered_data)),xlab = "Spectra index", ylab = "m/z difference in ppm", main="Difference m/z with max. average intensity vs. theor. m/z\n(per spectrum)") 911 plot(0,0,type="n", ylim=c(min(ppm_df, na.rm=TRUE),max(ppm_df, na.rm=TRUE)), xlim = c(1,ncol(filtered_data)),xlab = "Spectra index", ylab = "m/z difference in ppm", main="Difference m/z with max. average intensity vs. theor. m/z\n(per spectrum)")
910 for (each_cal in 1:ncol(ppm_df)){ 912 for (each_cal in 1:ncol(ppm_df)){
911 lines(ppm_df[,each_cal], col=mycolours[each_cal], type="p")} 913 lines(ppm_df[,each_cal], col=mycolours[each_cal], type="p")}
912 legend("topright", inset=c(-0.2,0), xpd = TRUE, bty="n", cex=0.8,legend=inputcalibrantmasses, col=mycolours[1:ncol(ppm_df)],lty=1) 914 legend("topright", inset=c(-0.2,0), xpd = TRUE, bty="n", cex=0.8,legend=inputcalibrantmasses, col=mycolours[1:ncol(ppm_df)],lty=1)
913 if (!is.null(levels(msidata\$annotation))){ 915 if (!is.null(unique(msidata\$annotation))){
914 abline(v=abline_vector, lty = 3)}} 916 abline(v=abline_vector, lty = 3)}}
915 917
916 ### make x-y-images for mz accuracy 918 ### make x-y-images for mz accuracy
917 919
918 ppm_dataframe = data.frame(coord(msidata)\$x, coord(msidata)\$y, ppm_df) 920 ppm_dataframe = data.frame(coord(msidata)\$x, coord(msidata)\$y, ppm_df)