msi_ion_images: msi_ion_images.xml comparison

comparison msi_ion_images.xml @ 4:729a8bf3ffa9 draft

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_ion_images commit 1c808d60243bb1eeda0cd26cb4b0a17ab05de2c0

author	galaxyp
date	Mon, 28 May 2018 12:34:05 -0400
parents	e045a8d295eb
children	a851b4e8fba7

comparison

equal deleted inserted replaced

-:e045a8d295eb
+:729a8bf3ffa9
-<tool id="mass_spectrometry_imaging_ion_images" name="MSI ion images" version="1.7.0.3">
+<tool id="mass_spectrometry_imaging_ion_images" name="MSI ion images" version="1.10.0.0">
 <description>
 mass spectrometry imaging heatmaps
 </description>
 <requirements>
-<requirement type="package" version="1.7.0">bioconductor-cardinal</requirement>
+<requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>
 <requirement type="package" version="2.2.1">r-gridextra</requirement>
-<requirement type="package" version="2.23-15">r-kernsmooth</requirement>
 <requirement type="package" version="0.20-35">r-lattice</requirement>
 </requirements>
 <command detect_errors="aggressive">
 <![CDATA[
 #if $infile.ext == 'imzml'
-cp '${infile.extra_files_path}/imzml' infile.imzML &&
+ln -s '${infile.extra_files_path}/imzml' infile.imzML &&
-cp '${infile.extra_files_path}/ibd' infile.ibd &&
+ln -s '${infile.extra_files_path}/ibd' infile.ibd &&
 #elif $infile.ext == 'analyze75'
-cp '${infile.extra_files_path}/hdr' infile.hdr &&
+ln -s '${infile.extra_files_path}/hdr' infile.hdr &&
-cp '${infile.extra_files_path}/img' infile.img &&
+ln -s '${infile.extra_files_path}/img' infile.img &&
-cp '${infile.extra_files_path}/t2m' infile.t2m &&
+ln -s '${infile.extra_files_path}/t2m' infile.t2m &&
 #else
 ln -s $infile infile.RData &&
 #end if
 cat '${MSI_heatmaps}' &&
 Rscript '${MSI_heatmaps}'
 ]]>
 </command>
 <configfiles>
 <configfile name="MSI_heatmaps"><![CDATA[
-################################# load libraries and read file #########################
+################################# load libraries and read file #################
 library(Cardinal)
 library(gridExtra)
-library(KernSmooth)
 library(lattice)
 ## Read MALDI Imaging dataset
 #if $infile.ext == 'imzml'
-msidata = readMSIData('infile.imzML')
+msidata = readImzML('infile')
 #elif $infile.ext == 'analyze75'
-msidata = readMSIData('infile.hdr')
+msidata = readAnalyze('infile')
 #else
 load('infile.RData')
 #end if
-###################################### file properties in numbers ######################
+###################################### file properties in numbers ##############
 ## Number of features (mz)
 maxfeatures = length(features(msidata))
 ## Range mz
 minmz = round(min(mz(msidata)), digits=2)
 ## if TRUE write processinginfo if no write FALSE
 ## normalization
 if (length(processinginfo@normalization) == 0) {
 normalizationinfo='FALSE'
 } else {
 normalizationinfo=processinginfo@normalization
 }
 ## smoothing
 if (length(processinginfo@smoothing) == 0) {
 smoothinginfo='FALSE'
 } else {
 smoothinginfo=processinginfo@smoothing
 }
 ## baseline
 if (length(processinginfo@baselineReduction) == 0) {
 baselinereductioninfo='FALSE'
 } else {
 baselinereductioninfo=processinginfo@baselineReduction
 }
 ## peak picking
 if (length(processinginfo@peakPicking) == 0) {
 peakpickinginfo='FALSE'
 } else {
 peakpickinginfo=processinginfo@peakPicking
 }
+##################################### read and filter input masses ##############
-### Read tabular file with peptide masses for heatmap images:
 input_list = read.delim("$massfile", header = FALSE, stringsAsFactors = FALSE)
-if (ncol(input_list) == 1)
-{
+### in case input file had only one column with mz values but not names, duplicate mz values and use as names:
-input_list = cbind(input_list, input_list)
-}
+if (ncol(input_list) == 1)
+{
-### calculate how many input masses are valid:
+input_list = cbind(input_list, input_list)
-inputmasses = input_list[input_list[,1]>minmz & input_list[,1]<maxmz,]
+}
-inputmz = inputmasses[,1]
+### calculate how many input masses are valid:
-inputnames = inputmasses[,2]
+inputmasses = input_list[input_list[,1]>minmz & input_list[,1]<maxmz,]
-if (nrow(input_list) == 1)
+inputmz = inputmasses[,1]
-{
+inputnames = inputmasses[,2]
+if (length(inputmz) == 1)
+{
 countpixels = sum(spectra(msidata)[features(msidata, mz = inputmz), ] >0)
-}else {
+percentpixels = round(countpixels/pixelcount*100, digits=1)
+valuesdataframe = cbind(inputmz, cbind(countpixels, percentpixels))
+write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
+}else if (length(inputmz) >1) {
 countpixels = rowSums(spectra(msidata)[features(msidata, mz=inputmz),] >0)
-}
+percentpixels = round(countpixels/pixelcount*100, digits=1)
+valuesdataframe = cbind(inputmz, cbind(countpixels, percentpixels))
-percentpixels = round(countpixels/pixelcount*100, digits=1)
+write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
-valuesdataframe = cbind(inputmz, cbind(countpixels, percentpixels))
+}else{
+valuesdataframe = data.frame(0,0)
+write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
-write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
+}
-colnames(input_list)[1:2] = c("mz", "name")
+############################ summarize file properties in numbers ##############
 properties = c("Number of mz features",
 "Range of mz values [Da]",
 "Number of pixels",
 "Range of x coordinates",
 "Range of y coordinates",
 paste0(length(inputmz), "/", length(input_list[,1])))
 property_df = data.frame(properties, values)
+############################## PDF #############################################
-######################################## PDF #############################################
-##########################################################################################
-##########################################################################################
 pdf("heatmaps.pdf", fonts = "Times", pointsize = 12)
 plot(0,type='n',axes=FALSE,ann=FALSE)
 #if not $filename:
 #set $filename = $infile.display_name
 #end if
-title(main=paste("Quality control of MSI data\n\n", "Filename:", "$filename"))
+title(main=paste("\nHeatmap images\n\n", "Filename:\n", "$filename"))
 ############################# I) numbers ####################################
-#############################################################################
 grid.table(property_df, rows= NULL)
+############################# II) images ####################################
+### only plot images when file has peaks and valid input mz:
 if (npeaks > 0)
 {
 if (length(inputmz) != 0)
 {
 for (mass in 1:length(inputmz))
 {
 print(image(msidata, mz=inputmz[mass], strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)),
-lattice=TRUE, ylim = c(maximumy+1,minimumy-1), plusminus = $plusminus_dalton, contrast.enhance = "$image_contrast", smooth.image = "$image_smoothing",
+lattice=TRUE, plusminus = $plusminus_dalton, contrast.enhance = "$image_contrast", smooth.image = "$image_smoothing",
 main= paste0(mass, ") ", inputnames[mass], " (", round(inputmz[mass], digits = 2)," ± ", $plusminus_dalton, " Da)")))
 }
 } else {print("The input masses were invalid")}
 dev.off()
 }else{
 print("inputfile has no intensities > 0")
 dev.off()
 }
 ]]></configfile>
 </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="" label="Title" help="will appear in the quality report. If nothing given it will take the dataset name"/>
-<param name="massfile" type="data" format="tabular" label="Text file with masses and names"
+<param name="massfile" type="data" format="tabular" label="Tabular file with masses and names"
 help="first column mass (m/z), second column mass name, tab separated file"/>
 <param name="image_contrast" type="select" label="Select a contrast enhancement function for the heatmap images" help="The 'histogram' equalization method flatterns the distribution of intensities. The hotspot 'suppression' method uses thresholding to reduce the intensities of hotspots">
 <option value="none" selected="True">none</option>
 <option value="suppression">suppression</option>
 <option value="histogram">histogram</option>
 <param name="image_smoothing" value="gaussian"/>
 <output name="plots" file="Heatmaps_analyze75.pdf" compare="sim_size" delta="20000"/>
 <output name="pixel_count" file="tabular_analyze75.tabular"/>
 </test>
 <test>
-<param name="infile" value="preprocessing_results1.RData" ftype="rdata"/>
+<param name="infile" value="preprocessed.rdata" ftype="rdata"/>
 <param name="massfile" value="inputpeptides.tabular" ftype="tabular"/>
-<param name="plusminus_dalton" value="0.1"/>
+<param name="plusminus_dalton" value="0.5"/>
 <param name="filename" value="Testfile_rdata"/>
 <output name="plots" file="Heatmaps_rdata.pdf" compare="sim_size" delta="20000"/>
 <output name="pixel_count" file="tabular_rdata.tabular"/>
 </test>
 <test>
-<param name="infile" value="LM8_file16.rdata" ftype="rdata"/>
+<param name="infile" value="empty_spectra.rdata" ftype="rdata"/>
 <param name="massfile" value="inputpeptides2.tabular" ftype="tabular"/>
-<param name="plusminus_dalton" value="0.1"/>
+<param name="plusminus_dalton" value="0.5"/>
 <param name="filename" value="Testfile_rdata"/>
 <output name="plots" file="Heatmaps_LM8_file16.pdf" compare="sim_size" delta="20000"/>
 <output name="pixel_count" file="tabular_LM8file16.tabular"/>
 </test>
 </tests>
 <help><![CDATA[
-Heatmaps for different masses in mass-spectrometry imaging data as pdf output.
+Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_
+This tool uses the Cardinal image function to plot the intensity distribution of interesting masses of mass-spectrometry imaging data.
 Input data:
 3 types of mass-spectrometry imaging 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/>`_
+- imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_
 - Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
 - Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData)
-tabular file with masses:
+Tabular file with masses:
-- tab separated file (.tabular), datatype in Galaxy must be tabular (if Galaxy auto-detection was wrong, datatype can be changed by pressing the pen button)
+- tab separated file (.tabular), datatype in Galaxy must be tabular otherwise file will not appear in selection window (if Galaxy auto-detection was wrong, datatype can be changed by pressing button with the pen (edit attributes))
 - first column must contain masses (separate point numbers by point, not comma)
 - optionally a second column with names for the masses can be provided
-- no empty fields or letters are allowed (tool crashes with empty fields and a single letter prohibits generation of images)
+- no empty fields or letters are allowed in the first column
-Trouble shooting:
+Output:
-- no heatmaps are plotted when tabular file contains letters or point numbers with commas or when the input MSI file had no intensities > 0
-- contrast enhance functions need masses with intensities > 0 in about 1.5% of all pixels - tool crashes when contrast enhance is used on too few intensities
+- Pdf with the heatmap images
+- Tabular with masses that were in the mass range and their occurence over all pixels (absolute and in %)
+Troubleshooting:
+- no heatmaps are plotted when tabular file doesn't fulfill the criteria described above
+- no heatmaps are plotted when the input mass spectrometry imaging file has no intensities > 0
+- out of thetabular file only masses with > 1.5-2% pixel coverage can be used with the contrast enhance and image smoothing functions, as both crash when a mass has not enough intensity values
 ]]>
 </help>
 <citations>
 <citation type="doi">10.1093/bioinformatics/btv146</citation>

Mercurial > repos > galaxyp > msi_ion_images

comparison msi_ion_images.xml @ 4:729a8bf3ffa9 draft