Mercurial > repos > galaxyp > cardinal_quality_report
changeset 11:f396c176f366 draft
"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit ca89f8e007c6b17f7c30066729e05b8686ab975a"
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--- a/macros.xml Thu Sep 24 11:44:48 2020 +0000 +++ b/macros.xml Sun Sep 27 11:11:53 2020 +0000 @@ -117,6 +117,13 @@ <token name="@DATA_PROPERTIES_INRAM@"><![CDATA[ ########################### QC numbers ######################## ## including intensity calculations which need data in RAM + + int_matrix = as.matrix(spectra(msidata)) ## only load once into RAM, then re-use + ## Number of NA in spectra matrix + NAcount = sum(is.na(int_matrix)) + ## replace NA with zero to calculate data properties based on intensity matrix, no change in msidata + int_matrix[is.na(int_matrix)] <- 0 + ## Number of features (mz) maxfeatures = length(features(msidata)) ## Range mz @@ -131,14 +138,12 @@ minimumy = min(coord(msidata)[,2]) maximumy = max(coord(msidata)[,2]) ## Range of intensities - minint = round(min(as.matrix(spectra(msidata)), na.rm=TRUE), digits=2) - maxint = round(max(as.matrix(spectra(msidata)), na.rm=TRUE), digits=2) + minint = round(min(int_matrix), digits=2) + maxint = round(max(int_matrix), digits=2) ## Number of intensities > 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",
--- a/quality_report.xml Thu Sep 24 11:44:48 2020 +0000 +++ b/quality_report.xml Sun Sep 27 11:11:53 2020 +0000 @@ -60,21 +60,22 @@ merged_annotation = merge(msidata_coordinates, annotation_input, by=c("x", "y"), all.x=TRUE) merged_annotation[is.na(merged_annotation)] = "NA" merged_annotation = merged_annotation[order(merged_annotation\$pixel_index),] - msidata\$annotation = as.factor(merged_annotation[,4]) + msidata\$annotation = as.character(merged_annotation[,4]) #end if ###################### calculation of data properties ################################ @DATA_PROPERTIES_INRAM@ + ## Median intensities -medint = round(median(spectra(msidata), na.rm=TRUE), digits=2) +medint = round(median(int_matrix), digits=2) ## Spectra multiplied with m/z (potential number of peaks) -numpeaks = ncol(msidata)*nrow(msidata) +numpeaks = as.numeric(ncol(msidata)*nrow(msidata)) ## Percentage of intensities > 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) @@ -183,12 +184,13 @@ ### 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() + @@ -414,7 +416,7 @@ ############################### 6b) median int image ############################### - median_int = pixelApply(msidata, median) + 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") @@ -433,7 +435,7 @@ ############################### 6c) max int image ############################### - max_int = pixelApply(msidata, max) + 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") @@ -495,7 +497,7 @@ 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() @@ -513,7 +515,7 @@ title(xlab="Spectra index", line=3) title(ylab="Number of peaks", line=4) - if (!is.null(levels(msidata\$annotation))){ + if (!is.null(unique(msidata\$annotation))){ abline(v=abline_vector, lty = 3)} ## 9b) histogram @@ -525,11 +527,11 @@ ## 9c) additional histogram to show contribution of annotation groups - if (!is.null(levels(msidata\$annotation))){ + if (!is.null(unique(msidata\$annotation))){ 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))+ @@ -555,17 +557,17 @@ 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") @@ -633,26 +635,26 @@ par(mfrow = c(2,1), mar=c(5,6,4,2)) ## 14a) Median intensity over spectra - medianint_spectra = pixelApply(msidata, median) + 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(levels(msidata\$annotation))){ + if (!is.null(unique(msidata\$annotation))){ abline(v=abline_vector, lty = 3)} ## 14b) histogram: - hist(as.matrix(spectra(msidata)), main="", xlab = "", ylab="", las=1) + 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(as.matrix(spectra(msidata))[(as.matrix(spectra(msidata))>0)], na.rm=TRUE), col="blue") + abline(v=median(int_matrix)[(as.matrix(spectra(msidata))>0)], col="blue") ## 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 +670,43 @@ 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) } @@ -721,20 +723,20 @@ 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")) + 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 - if (!is.null(levels(msidata\$annotation))){ + if (!is.null(unique(msidata\$annotation))){ ## print legend only for less than 10 samples - if (length(levels(msidata\$annotation)) < 10){ + 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(levels(msidata\$annotation))),superpose=TRUE, main="Average mass spectra for annotation groups")) + 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 @@ -742,7 +744,7 @@ 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)) + print(plot(msidata_no_NA, pixel = pixel_vector, col="black")) ################### 16) Zoomed in mass spectra for calibrants ############## @@ -790,20 +792,20 @@ 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 +815,7 @@ ### 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} @@ -910,7 +912,7 @@ 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
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--- a/test-data/out8.imzml.txt Thu Sep 24 11:44:48 2020 +0000 +++ b/test-data/out8.imzml.txt Sun Sep 27 11:11:53 2020 +0000 @@ -1,4 +1,4 @@ imzML file: -total 348 --rw-rw-r-- 1 meli meli 335976 Aug 23 17:42 ibd --rw-rw-r-- 1 meli meli 12402 Aug 23 17:42 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