msi_ion_images: msi_ion_images.xml comparison

comparison msi_ion_images.xml @ 5:2b9fa240e261 draft

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_ion_images commit a7be47698f53eb4f00961192327d93e8989276a7

author	galaxyp
date	Mon, 11 Jun 2018 17:33:52 -0400
parents	9746576123c9
children	5a5b5a8fa8a0

comparison

equal deleted inserted replaced

-:9746576123c9
+:2b9fa240e261
-<tool id="mass_spectrometry_imaging_ion_images" name="MSI ion images" version="1.10.0.0">
+<tool id="mass_spectrometry_imaging_ion_images" name="MSI ion images" version="1.10.0.1">
 <description>
-mass spectrometry imaging heatmaps
+mass spectrometry imaging m/z heatmaps
 </description>
 <requirements>
 <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>
 <requirement type="package" version="2.2.1">r-gridextra</requirement>
 <requirement type="package" version="0.20-35">r-lattice</requirement>
 #end if
 ###################################### file properties in numbers ##############
-## Number of features (mz)
+## Number of features (m/z)
 maxfeatures = length(features(msidata))
-## Range mz
+## Range m/z
 minmz = round(min(mz(msidata)), digits=2)
 maxmz = round(max(mz(msidata)), digits=2)
 ## Number of spectra (pixels)
 pixelcount = length(pixels(msidata))
 ## Range x coordinates
 minint = round(min(spectra(msidata)[]), digits=2)
 maxint = round(max(spectra(msidata)[]), digits=2)
 medint = round(median(spectra(msidata)[]), digits=2)
 ## Number of intensities > 0
 npeaks= sum(spectra(msidata)[]>0)
-## Spectra multiplied with mz (potential number of peaks)
+## Spectra multiplied with m/z (potential number of peaks)
 numpeaks = ncol(spectra(msidata)[])*nrow(spectra(msidata)[])
 ## Percentage of intensities > 0
 percpeaks = round(npeaks/numpeaks*100, digits=2)
 ## Number of empty TICs
 TICs = colSums(spectra(msidata)[])
 peakpickinginfo='FALSE'
 } else {
 peakpickinginfo=processinginfo@peakPicking
 }
-##################################### read and filter input masses ##############
+##################################### read and filter input m/z ##############
 input_list = read.delim("$massfile", header = FALSE, stringsAsFactors = FALSE)
-### in case input file had only one column with mz values but not names, duplicate mz values and use as names:
+### in case input file had only one column with m/z values but not names, duplicate m/z values and use as names:
+if (ncol(input_list) == 1){
-if (ncol(input_list) == 1)
+input_list = cbind(input_list, input_list)}
-{
-input_list = cbind(input_list, input_list)
+### calculate how many input m/z are valid:
-}
-### calculate how many input masses are valid:
 inputmasses = input_list[input_list[,1]>minmz & input_list[,1]<maxmz,]
+inputmz = as.numeric(inputmasses[,1])
-inputmz = inputmasses[,1]
+inputnames = as.character(inputmasses[,2])
-inputnames = inputmasses[,2]
-if (length(inputmz) == 1)
+############################## PDF #############################################
-{
+################################################################################
-countpixels = sum(spectra(msidata)[features(msidata, mz = inputmz), ] >0)
-percentpixels = round(countpixels/pixelcount*100, digits=1)
+pdf("heatmaps.pdf", fonts = "Times", pointsize = 12)
-valuesdataframe = cbind(inputmz, cbind(countpixels, percentpixels))
+plot(0,type='n',axes=FALSE,ann=FALSE)
-write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
+#if not $filename:
-}else if (length(inputmz) >1) {
+#set $filename = $infile.display_name
-countpixels = rowSums(spectra(msidata)[features(msidata, mz=inputmz),] >0)
+#end if
-percentpixels = round(countpixels/pixelcount*100, digits=1)
+title(main=paste("\nHeatmap images\n\n", "Filename:\n", "$filename"))
-valuesdataframe = cbind(inputmz, cbind(countpixels, percentpixels))
-write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
+############################# I) numbers ####################################
-}else{
-valuesdataframe = data.frame(0,0)
+properties = c("Number of m/z features",
-write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
+"Range of m/z values [Da]",
-}
-############################ 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",
 "Range of intensities",
 "Median of intensities",
 "Normalization",
 "Smoothing",
 "Baseline reduction",
 "Peak picking",
 "Centroided",
-paste0("# valid masses in \n", "$massfile.display_name"))
+paste0("# valid m/z in \n", "$massfile.display_name"))
 values = c(paste0(maxfeatures),
 paste0(minmz, " - ", maxmz),
 paste0(pixelcount),
 paste0(minimumx, " - ", maximumx),
 paste0(centroidedinfo),
 paste0(length(inputmz), "/", length(input_list[,1])))
 property_df = data.frame(properties, values)
-############################## PDF #############################################
-pdf("heatmaps.pdf", fonts = "Times", pointsize = 12)
-plot(0,type='n',axes=FALSE,ann=FALSE)
-#if not $filename:
-#set $filename = $infile.display_name
-#end if
-title(main=paste("\nHeatmap images\n\n", "Filename:\n", "$filename"))
-############################# I) numbers ####################################
 grid.table(property_df, rows= NULL)
 ############################# II) images ####################################
-### only plot images when file has peaks and valid input mz:
+### only plot images when file has peaks and valid input m/z:
-if (npeaks > 0)
+if (npeaks > 0){
-{
+if (length(inputmz) != 0){
-if (length(inputmz) != 0)
+for (mass in 1:length(inputmz)){
-{
-for (mass in 1:length(inputmz))
+###standard image
-{
-print(image(msidata, mz=inputmz[mass], strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)),
+#if str($image_cond.image_type) == "standard_image":
-lattice=TRUE, plusminus = $plusminus_dalton, contrast.enhance = "$image_contrast", smooth.image = "$image_smoothing",
+print("standard image")
-main= paste0(mass, ") ", inputnames[mass], " (", round(inputmz[mass], digits = 2)," ± ", $plusminus_dalton, " Da)")))
-}
+print(image(msidata, mz=inputmz[mass],plusminus = $plusminus_dalton, contrast.enhance = "$image_contrast",
-} else {print("The input masses were invalid")}
+smooth.image = "$image_smoothing", strip=$strip, colorkey=$colorkey,
+main= paste0(inputnames[mass], " (", round(inputmz[mass], digits = 2)," ± ", $plusminus_dalton, " Da)")))}
+###lattice image
+#elif str($image_cond.image_type) == "lattice_image":
+print("lattice image")
+#if str($strip) =="TRUE":
+print(image(msidata, mz=inputmz[mass], strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)),
+lattice=TRUE, plusminus = $plusminus_dalton, contrast.enhance = "$image_contrast", smooth.image = "$image_smoothing",
+colorkey=$colorkey,
+main= paste0(inputnames[mass], " (", round(inputmz[mass], digits = 2)," ± ", $plusminus_dalton, " Da)")))}
+#elif str($strip) =="FALSE":
+print(image(msidata, mz=inputmz[mass], strip = $strip,
+lattice=TRUE, plusminus = $plusminus_dalton, contrast.enhance = "$image_contrast", smooth.image = "$image_smoothing",
+colorkey=$colorkey,
+main= paste0(inputnames[mass], " (", round(inputmz[mass], digits = 2)," ± ", $plusminus_dalton, " Da)")))}
+#end if
+#end if
+} else {print("The input m/z 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="filename" type="text" value="" label="Title" help="will appear in the pdf output. If nothing given it will take the dataset name"/>
-<param name="massfile" type="data" format="tabular" label="Tabular file with masses and names"
+<param name="massfile" type="data" format="tabular" label="Tabular file with m/z and names"
-help="first column mass (m/z), second column mass name, tab separated file"/>
+help="first column m/z, second column m/z 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>
 <param name="image_smoothing" type="select" label="Select an image smoothing function for the heatmap images" help="The 'gaussian' smoothing method smooths images with a simple gaussian kernel. The 'adaptive' method uses bilateral filtering to preserve edges">
 <option value="none" selected="True">none</option>
 <option value="gaussian">gaussian</option>
 <option value="adaptive">adaptive</option>
 </param>
-<param name="plusminus_dalton" value="0.25" type="float" label="Mass range" help="plusminus mass window in Dalton"/>
+<param name="plusminus_dalton" value="0.25" type="float" label="M/z range" help="plusminus m/z window in Dalton"/>
+<param name="strip" type="boolean" checked="True" display="radio" truevalue="TRUE" falsevalue="FALSE" label="Display m/z value in plot"/>
+<param name="colorkey" type="boolean" checked="True" display="radio"  truevalue="TRUE" falsevalue="FALSE" label="Display colorkey in plot"/>
+<conditional name="image_cond">
+<param name="image_type" type="select" label="Select the image type">
+<option value="standard_image" selected="True">standard</option>
+<option value="lattice_image">lattice</option>
+</param>
+<when value="standard_image"/>
+<when value="lattice_image"/>
+</conditional>
 </inputs>
 <outputs>
-<data format="pdf" name="plots" from_work_dir="heatmaps.pdf" label = "${tool.name} ${on_string}"/>
+<data format="pdf" name="plots" from_work_dir="heatmaps.pdf" label = "$infile.display_name heatmaps"/>
-<data format="tabular" name="pixel_count" label="Number of peaks (intensity > 0) per mz"/>
 </outputs>
 <tests>
 <test>
 <param name="infile" value="" ftype="imzml">
 <composite_data value="Example_Continuous.imzML"/>
 </param>
 <param name="massfile" value="inputpeptides.tabular" ftype="tabular"/>
 <param name="plusminus_dalton" value="0.25"/>
 <param name="filename" value="Testfile_imzml"/>
 <param name="image_contrast" value="histogram"/>
+<param name="strip" value="True"/>
+<param name="colorkey" value="True"/>
+<param name="image_type" value="lattice_image"/>
 <output name="plots" file="Heatmaps_imzml.pdf" compare="sim_size" delta="20000"/>
-<output name="pixel_count" file="tabular_imzml.tabular"/>
 </test>
 <test>
 <param name="infile" value="" ftype="analyze75">
 <composite_data value="Analyze75.hdr"/>
 <composite_data value="Analyze75.img"/>
 </param>
 <param name="massfile" value="inputpeptides2.tabular" ftype="tabular"/>
 <param name="plusminus_dalton" value="0.5"/>
 <param name="filename" value="Testfile_analyze75"/>
 <param name="image_smoothing" value="gaussian"/>
+<param name="strip" value="False"/>
+<param name="colorkey" value="True"/>
 <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="preprocessed.rdata" ftype="rdata"/>
 <param name="massfile" value="inputpeptides.tabular" ftype="tabular"/>
 <param name="plusminus_dalton" value="0.5"/>
+<param name="strip" value="True"/>
+<param name="colorkey" value="True"/>
+<param name="image_type" value="lattice_image"/>
 <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="empty_spectra.rdata" ftype="rdata"/>
 <param name="massfile" value="inputpeptides2.tabular" ftype="tabular"/>
 <param name="plusminus_dalton" value="0.5"/>
+<param name="strip" value="True"/>
+<param name="colorkey" value="False"/>
 <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[
 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.
+This tool uses the Cardinal image function to plot the intensity distribution of interesting m/z of mass spectrometry imaging data.
 Input data:
-3 types of mass-spectrometry imaging data can be used:
+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 <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 m/z:
 - 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)
+- first column must contain m/z (separate point numbers by point, not comma)
-- optionally a second column with names for the masses can be provided
+- optionally a second column with names for the m/z can be provided
 - no empty fields or letters are allowed in the first column
 Output:
 - 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
+- the contrast enhance and image smoothing functions require a certain number of m/z with intensities > 0 (empirical value > 2% of spectra)
+- the standard image function should work for all files while the lattice function works not on every file (nicely)
 ]]>
 </help>
 <citations>
 <citation type="doi">10.1093/bioinformatics/btv146</citation>

Mercurial > repos > galaxyp > msi_ion_images

comparison msi_ion_images.xml @ 5:2b9fa240e261 draft