msi_qualitycontrol: msi_qualitycontrol.xml comparison

comparison msi_qualitycontrol.xml @ 4:fef8bd551236 draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/msi_qualitycontrol commit edbf2a6cb50fb04d0db56a7557a64e3bb7a0806a

author	galaxyp
date	Thu, 01 Mar 2018 08:25:48 -0500
parents	f6aa0cff777c
children	ac786240ef07

comparison

equal deleted inserted replaced

-:f6aa0cff777c
+:fef8bd551236
-<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.7.0.2">
+<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.7.0.3">
 <description>
 mass spectrometry imaging QC
 </description>
 <requirements>
 <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement>
 library(ggplot2)
 library(RColorBrewer)
 library(gridExtra)
 library(KernSmooth)
-## Read MALDI Imagind dataset
+## Read MALDI Imaging dataset
 #if $infile.ext == 'imzml'
-msidata <- readMSIData('infile.imzML')
+msidata = readMSIData('infile.imzML')
 #elif $infile.ext == 'analyze75'
-msidata <- readMSIData('infile.hdr')
+msidata = readMSIData('infile.hdr')
 #else
 load('infile.RData')
 #end if
-#if $inputpeptidefile:
-### Read tabular file with peptide masses for plots and heatmap images:
-input_list = read.delim("$inputpeptidefile", header = FALSE, na.strings=c("","NA", "#NUM!", "#ZAHL!"), stringsAsFactors = FALSE)
-if (ncol(input_list) == 1)
-{
-input_list = cbind(input_list, input_list)
-}
-#else
-input_list = data.frame(0, 0)
-#end if
-colnames(input_list)[1:2] = c("mz", "name")
-#if $inputcalibrants:
-### Read tabular file with calibrant masses:
-calibrant_list = read.delim("$inputcalibrants", header = FALSE, na.strings=c("","NA", "#NUM!", "#ZAHL!"), stringsAsFactors = FALSE)
-if (ncol(calibrant_list) == 1)
-{
-calibrant_list = cbind(calibrant_list, calibrant_list)
-}
-#else
-calibrant_list = data.frame(0,0)
-#end if
-colnames(calibrant_list)[1:2] = c("mz", "name")
 ###################################### file properties in numbers ######################
 ## Number of features (mz)
 peakpickinginfo='FALSE'
 } else {
 peakpickinginfo=processinginfo@peakPicking
 }
-### calculate how many input peptide masses are valid:
+### Read tabular file with peptide masses for plots and heatmap images:
-inputpeptides = input_list[input_list[,1]>minmz & input_list[,1]<maxmz,]
+#if $peptide_file:
-### calculate how many input calibrant masses are valid:
-inputcalibrants = calibrant_list[calibrant_list[,1]>minmz & calibrant_list[,1]<maxmz,]
+input_list = read.delim("$peptide_file", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE)
+if (ncol(input_list) == 1)
+{
+input_list = cbind(input_list, input_list)
+}
+### calculate how many input peptide masses are valid:
+inputpeptides = input_list[input_list[,1]>minmz & input_list[,1]<maxmz,]
+number_peptides_in = length(input_list[,1])
+number_peptides_valid = length(inputpeptides)
+#else
+inputpeptides = data.frame(0,0)
+number_peptides_in = 0
+number_peptides_valid = 0
+#end if
+colnames(inputpeptides) = c("mz", "name")
+#if $calibrant_file:
+### Read tabular file with calibrant masses:
+calibrant_list = read.delim("$calibrant_file", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE)
+if (ncol(calibrant_list) == 1)
+{
+calibrant_list = cbind(calibrant_list, calibrant_list)
+}
+### calculate how many input calibrant masses are valid:
+inputcalibrants = calibrant_list[calibrant_list[,1]>minmz & calibrant_list[,1]<maxmz,]
+number_calibrants_in = length(calibrant_list[,1])
+number_calibrants_valid = length(inputcalibrants)
+#else
+inputcalibrants = data.frame(0,0)
+number_calibrants_in = 0
+number_calibrants_valid = 0
+#end if
+colnames(inputcalibrants) = c("mz", "name")
 ### bind inputcalibrants and inputpeptides together, to make heatmap on both lists
 inputs_all = rbind(inputcalibrants[,1:2], inputpeptides[,1:2])
 inputmasses = inputs_all[,1]
 "Normalization",
 "Smoothing",
 "Baseline reduction",
 "Peak picking",
 "Centroided",
-paste0("# valid masses in ", "$filename"))
+paste0("# peptides in ", "$peptide_file.display_name"),
+paste0("# calibrants in ", "$calibrant_file.display_name"))
 values = c(paste0(maxfeatures),
 paste0(minmz, " - ", maxmz),
 paste0(pixelcount),
 paste0(minimumx, " - ", maximumx),
 paste0(normalizationinfo),
 paste0(smoothinginfo),
 paste0(baselinereductioninfo),
 paste0(peakpickinginfo),
 paste0(centroidedinfo),
-paste0(length(inputmasses), "/", length(calibrant_list[,1])+length(input_list[,1])))
+paste0(number_peptides_valid, " / " , number_peptides_in),
+paste0(number_calibrants_valid, " / ", number_calibrants_in))
 property_df = data.frame(properties, values)
 ######################################## PDF #############################################
 plot_colorByDensity = function(x1,x2,
 ylim=c(min(x2),max(x2)),
 xlim=c(min(x1),max(x1)),
 xlab="",ylab="",main=""){
-df <- data.frame(x1,x2)
+df = data.frame(x1,x2)
-x <- densCols(x1,x2, colramp=colorRampPalette(c("black", "white")))
+x = densCols(x1,x2, colramp=colorRampPalette(c("black", "white")))
-df\$dens <- col2rgb(x)[1,] + 1L
+df\$dens = col2rgb(x)[1,] + 1L
-cols <-  colorRampPalette(c("#000099", "#00FEFF", "#45FE4F","#FCFF00", "#FF9400", "#FF3100"))(256)
+cols = colorRampPalette(c("#000099", "#00FEFF", "#45FE4F","#FCFF00", "#FF9400", "#FF3100"))(256)
-df\$col <- cols[df\$dens]
+df\$col = cols[df\$dens]
 plot(x2~x1, data=df[order(df\$dens),],
 ylim=ylim,xlim=xlim,pch=20,col=col,
 cex=1,xlab=xlab,ylab=ylab,las=1,
 main=main)
 }
-## Variables for plots
-xrange = 1
-yrange = 1
-maxx = max(coord(msidata)[,1])+xrange
-minx = min(coord(msidata)[,1])-xrange
-maxy = max(coord(msidata)[,2])+yrange
-miny = min(coord(msidata)[,2])-yrange
 ############################################################################
 ## 1) Acquisition image
 if (length(inputcalibrantmasses) != 0)
 {   for (calibrantnr in 1:length(inputcalibrantmasses))
 {
 calibrantmz = inputcalibrantmasses[calibrantnr]
-calibrantfeaturemin = features(msidata, mz=calibrantmz-$plusminusinDalton)
+calibrantfeaturemin = features(msidata, mz=calibrantmz-$plusminus_dalton)
-calibrantfeaturemax = features(msidata, mz=calibrantmz+$plusminusinDalton)
+calibrantfeaturemax = features(msidata, mz=calibrantmz+$plusminus_dalton)
 if (calibrantfeaturemin == calibrantfeaturemax)
 {
 calibrantintensity = spectra(msidata)[calibrantfeaturemin,]
 pixelmatrix = rbind(pixelmatrix, calibrantintensity)
 }
 countvector= as.factor(colSums(pixelmatrix>0))
 countdf= cbind(coord(msidata), countvector)
-mycolours = c("black","grey", "darkblue", "blue", "green" , "red", "yellow", "magenta", "olivedrap1", "lightseagreen")
+mycolours = c("black","grey", "darkblue", "blue", "green" , "red", "yellow", "magenta", "olivedrab1", "lightseagreen")
 print(ggplot(countdf, aes(x=x, y=y, fill=countvector))
 + geom_tile() + coord_fixed()
 + ggtitle("2) Number of calibrants per pixel")
 + theme_bw()
 + theme(text=element_text(family="ArialMT", face="bold", size=12))
 + scale_fill_manual(values = mycolours[1:length(countvector)],
 na.value = "black", name = "# calibrants"))
-}else{print("2) The inputcalibrant masses were outside the mass range")}
+}else{print("2) The inputcalibrant masses were not provided or outside the mass range")}
 ############# new 2b) image of foldchanges (log2 intensity ratios) between two masses in the same spectrum
 #if $calibrantratio:
 mzdown1 = features(msidata, mz = mass1-2)
 mzup1 = features(msidata, mz = mass1+3)
 mzdown2 = features(msidata, mz = mass2-2)
 mzup2 = features(msidata, mz = mass2+3)
-### plot the part which was chosen, with chosen value in blue, distance in blue, maxmass in red, xlim fixed to 5 Da window
+### find mass in the given range with the highest intensity (will be plotted in red)
 if (mzrowdown1 == mzrowup1)
 {
-maxmassrow1 = spectra(msidata)[mzrowup1,]
+maxmass1 = mz(msidata)[ mzrowdown1]
-maxmass1 = mz(msidata)[mzrowup1][which.max(maxmassrow1)]
+}else{ ### for all masses in the massrange calculate mean intensity over all pixels and take mass which has highest mean
-}else{
 maxmassrow1 = rowMeans(spectra(msidata)[mzrowdown1:mzrowup1,])
 maxmass1 = mz(msidata)[mzrowdown1:mzrowup1][which.max(maxmassrow1)]
 }
 if (mzrowdown2 == mzrowup2)
 {
-maxmassrow2 = spectra(msidata)[mzrowup2,]
+maxmass2 = mz(msidata)[mzrowup2]
-maxmass2 = mz(msidata)[mzrowup2][which.max(maxmassrow2)]
 }else{
 maxmassrow2 = rowMeans(spectra(msidata)[mzrowdown2:mzrowup2,])
 maxmass2 = mz(msidata)[mzrowdown2:mzrowup2][which.max(maxmassrow2)]
 }
+### plot the part which was chosen, with chosen value in blue, distance in blue, maxmass in red, xlim fixed to 5 Da window
 par(mfrow=c(2,1), oma=c(0,0,2,0))
 plot(msidata[mzdown1:mzup1,], pixel = 1:pixelcount, main=paste0("average spectrum ", mass1, " Da"))
 abline(v=c(mass1-distance, mass1, mass1+distance), col="blue",lty=c(3,5,3))
 abline(v=maxmass1, col="red", lty=5)
 ### filter spectra for maxmass to have two vectors, which can be divided
 mass1vector = spectra(msidata)[features(msidata, mz = maxmass1),]
 mass2vector = spectra(msidata)[features(msidata, mz = maxmass2),]
 foldchange = log2(mass1vector/mass2vector)
 ratiomatrix = cbind(foldchange, coord(msidata))
 print(ggplot(ratiomatrix, aes(x=x, y=y, fill=foldchange), colour=colo)
 par(mfrow=c(1,1), mar=c(5.1, 4.1, 4.1, 2.1), mgp=c(3, 1, 0), las=0)
 if (length(inputmasses) != 0)
 {   for (mass in 1:length(inputmasses))
 {
-image(msidata, mz=inputmasses[mass], plusminus=$plusminusinDalton,
+image(msidata, mz=inputmasses[mass], plusminus=$plusminus_dalton,
-main= paste0("3", LETTERS[mass], ") ", inputnames[mass], " (", round(inputmasses[mass], digits = 2)," ± ", $plusminusinDalton, " Da)"),
+main= paste0("3", LETTERS[mass], ") ", inputnames[mass], " (", round(inputmasses[mass], digits = 2)," ± ", $plusminus_dalton, " Da)"),
-contrast.enhance = "histogram", ylim=c(maxy+1, 0))
+contrast.enhance = "histogram", ylim=c(maximumy+2, 0))
 }
 } else {print("3) The inputpeptide masses were outside the mass range")}
 ## 4) Number of peaks per pixel - image
 + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange")
 ,space = "Lab", na.value = "black", name = "# peaks"))
 ## 5) TIC image
 TICcoordarray=cbind(coord(msidata), TICs)
-colo <- colorRampPalette(
+colo = colorRampPalette(
 c("blue", "cyan", "green", "yellow","red"))
 print(ggplot(TICcoordarray, aes(x=x, y=y, fill=TICs), colour=colo)
 + geom_tile() + coord_fixed()
 + ggtitle("5) Total Ion Chromatogram")
 + theme_bw()
 secondhighestmz = names(sort(table(round(highestmz_matrix\$highestmzinDa, digits=0)), decreasing=TRUE)[2])
 secondhighestmz_pixel = which(round(highestmz_matrix\$highestmzinDa, digits=0) == secondhighestmz)[1]
 ## 7) pca image for two components
-pca <- PCA(msidata, ncomp=2)
+pca = PCA(msidata, ncomp=2)
 par(mfrow = c(2,1))
 plot(pca, col=c("black", "darkgrey"), main="7) PCA for two components")
-image(pca, col=c("black", "white"),ylim=c(maxy+1, 0))
+image(pca, col=c("black", "white"),ylim=c(maximumy+2, 0))
 ############################# III) properties over acquisition (spectra index)##########
 ##############################################################################
 hist(peaksperpixel, main="", las=1, xlab = "Number of peaks per spectrum", ylab="")
 title(main="8b) Number of peaks per spectrum", line=2)
 title(ylab="Frequency = # spectra", line=4)
 abline(v=median(peaksperpixel), col="blue")
-## 9a) TIC per spectrum -  density scatterplot
+## 9a) TIC per spectrum - density scatterplot
 zero=0
 par(mfrow = c(2,1), mar=c(5,6,4,2))
 plot_colorByDensity(pixels(msidata), TICs,  ylab = "", xlab = "", main="9a) TIC per pixel")
 title(xlab="Spectra index \n (= Acquisition time)", line=3)
 title(ylab = "Total ion chromatogram intensity", line=4)
 plot(msidata, pixel =round(length(pixelnumber)/2, digits=0), main="Spectrum in middle of acquisition")
 plot(msidata, pixel = highestmz_pixel, main= paste0("Spectrum at ", rownames(coord(msidata)[highestmz_pixel,])))
 plot(msidata, pixel = secondhighestmz_pixel, main= paste0("Spectrum at ", rownames(coord(msidata)[secondhighestmz_pixel,])))
 ## 15) Zoomed in mass spectra for calibrants
-plusminusvalue = $plusminusinDalton
+plusminusvalue = $plusminus_dalton
 x = 1
 if (length(inputcalibrantmasses) != 0)
 {
 for (calibrant in inputcalibrantmasses)
 minmasspixel = features(msidata, mz=calibrant-1)
 maxmasspixel = features(msidata, mz=calibrant+3)
 par(mfrow = c(2, 2), oma=c(0,0,2,0))
 plot(msidata[minmasspixel:maxmasspixel,], pixel = 1:length(pixelnumber), main= "average spectrum")
 abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3))
-plot(msidata[minmasspixel:maxmasspixel,], pixel =round(pixelnumber/2, digits=0), main="pixel in middle of acquisition")
+plot(msidata[minmasspixel:maxmasspixel,], pixel =round(length(pixelnumber)/2, digits=0), main="pixel in middle of acquisition")
 abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3))
 plot(msidata[minmasspixel:maxmasspixel,], pixel = highestmz_pixel,main= paste0("Spectrum at ", rownames(coord(msidata)[highestmz_pixel,])))
 abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3))
 plot(msidata[minmasspixel:maxmasspixel,], pixel = secondhighestmz_pixel,  main= paste0("Spectrum at ", rownames(coord(msidata)[secondhighestmz_pixel,])))
 abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3))
 </configfiles>
 <inputs>
 <param name="infile" type="data" format="imzml,rdata,analyze75" label="Inputfile as imzML, Analyze7.5 or Cardinal MSImageSet saved as RData"
 help="Upload composite datatype imzml (ibd+imzML) or analyze75 (hdr+img+t2m) or regular upload .RData (Cardinal MSImageSet)"/>
 <param name="filename" type="text" value="" optional="true" label="Title" help="will appear in the quality report. If nothing given it will take the dataset name."/>
-<param name="inputpeptidefile" type="data" optional="true" format="txt, csv" label="Text file with peptidemasses and names"
+<param name="peptide_file" type="data" optional="true" format="tabular" label="Text file with peptidemasses and names"
 help="first column peptide m/z, second column peptide name, tab separated file"/>
-<param name="inputcalibrants" type="data" optional="true" format="txt,csv"
+<param name="calibrant_file" type="data" optional="true" format="tabular"
 label="Internal calibrants"
 help="Used for plot number of calibrant per spectrum and for zoomed in mass spectra"/>
-<param name="plusminusinDalton" value="0.25" type="text" label="Mass range" help="plusminus mass window in Dalton"/>
+<param name="plusminus_dalton" value="0.25" type="text" label="Mass range" help="plusminus mass window in Dalton"/>
 <repeat name="calibrantratio" title="Plot fold change of two masses for each spectrum" min="0" max="10">
 <param name="mass1" value="1111" type="float" label="Mass 1" help="First mass in Dalton"/>
 <param name="mass2" value="2222" type="float" label="Mass 2" help="Second mass in Dalton"/>
 <param name="distance" value="0.25" type="float" label="Distance in Dalton" help="Distance in Da used to find peak maximum from input masses in both directions"/>
 <param name="filenameratioplot" type="text" optional="true" label="Title" help="Optional title for fold change plot."/>
 <test>
 <param name="infile" value="" ftype="imzml">
 <composite_data value="Example_Continuous.imzML" />
 <composite_data value="Example_Continuous.ibd" />
 </param>
-<param name="inputpeptidefile" value="inputpeptides.csv" ftype="csv"/>
+<param name="peptide_file" value="inputpeptides.csv" ftype="csv"/>
-<param name="inputcalibrants" ftype="txt" value="inputcalibrantfile1.txt"/>
+<param name="calibrant_file" ftype="txt" value="inputcalibrantfile1.txt"/>
-<param name="plusminusinDalton" value="0.25"/>
+<param name="plusminus_dalton" value="0.25"/>
 <param name="filename" value="Testfile_imzml"/>
 <repeat name="calibrantratio">
 <param name="mass1" value="111"/>
 <param name="mass2" value="222"/>
 <param name="distance" value="0.25"/>
+<param name="filenameratioplot" value = "Ratio of mass1 (111) / mass2 (222)"/>
 </repeat>
 <output name="plots" file="Testfile_qualitycontrol_imzml.pdf" compare="sim_size" delta="20000"/>
 </test>
 <test>
 <param name="infile" value="" ftype="analyze75">
 <composite_data value="Analyze75.hdr"/>
 <composite_data value="Analyze75.img"/>
 <composite_data value="Analyze75.t2m"/>
 </param>
-<param name="inputpeptidefile" value="inputpeptides.txt" ftype="txt"/>
+<param name="peptide_file" value="inputpeptides.txt" ftype="txt"/>
-<param name="inputcalibrants" ftype="txt" value="inputcalibrantfile2.txt"/>
+<param name="calibrant_file" ftype="txt" value="inputcalibrantfile2.txt"/>
-<param name="plusminusinDalton" value="0.5"/>
+<param name="plusminus_dalton" value="0.5"/>
 <param name="filename" value="Testfile_analyze75"/>
 <output name="plots" file="Testfile_qualitycontrol_analyze75.pdf" compare="sim_size" delta="20000"/>
 </test>
 <test>
 <param name="infile" value="preprocessing_results1.RData" ftype="rdata"/>
-<param name="inputpeptidefile" value="inputpeptides.csv" ftype="txt"/>
+<param name="peptide_file" value="inputpeptides.csv" ftype="txt"/>
-<param name="inputcalibrants" ftype="txt" value="inputcalibrantfile1.txt"/>
+<param name="calibrant_file" ftype="txt" value="inputcalibrantfile1.txt"/>
-<param name="plusminusinDalton" value="0.1"/>
+<param name="plusminus_dalton" value="0.1"/>
 <param name="filename" value="Testfile_rdata"/>
 <output name="plots" file="Testfile_qualitycontrol_rdata.pdf" compare="sim_size" delta="20000"/>
 </test>
 <test>
 <param name="infile" value="LM8_file16.rdata" ftype="rdata"/>
-<param name="inputpeptidefile" value="inputpeptides.txt" ftype="txt"/>
+<param name="peptide_file" value="inputpeptides.txt" ftype="txt"/>
-<param name="inputcalibrants" ftype="txt" value="inputcalibrantfile2.txt"/>
+<param name="calibrant_file" ftype="txt" value="inputcalibrantfile2.txt"/>
-<param name="plusminusinDalton" value="0.1"/>
+<param name="plusminus_dalton" value="0.1"/>
 <param name="filename" value="Testfile_rdata"/>
 <output name="plots" file="LM8_file16output.pdf" compare="sim_size" delta="20000"/>
 </test>
 </tests>
 <help>
 <![CDATA[
-Quality control for maldi imaging mass spectrometry data.
+Quality control for maldi imaging mass spectrometry data. The output of this tool contains key values and plots of the imaging data as pdf.
+For additional beautiful heatmap images use the MSI ion images tool and to plot more mass spectra use the MSI massspectra tool.
 Input data: 3 types of input data can be used:
 - imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <http://ms-imaging.org/wp/introduction/>`_
 - Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
 - Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData)
-The output of this tool contains key values and plots of the imaging data as pdf.
 ]]>
 </help>
 <citations>
 <citation type="doi">10.1093/bioinformatics/btv146</citation>

Mercurial > repos > galaxyp > msi_qualitycontrol

comparison msi_qualitycontrol.xml @ 4:fef8bd551236 draft