<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="1.10.0">bioconductor-cardinal</requirement>
        <requirement type="package" version="2.2.1">r-gridextra</requirement>
        <requirement type="package" version="0.20-35">r-lattice</requirement>
    </requirements>
    <command detect_errors="aggressive">
<![CDATA[
        #if $infile.ext == 'imzml'
            ln -s '${infile.extra_files_path}/imzml' infile.imzML &&
            ln -s '${infile.extra_files_path}/ibd' infile.ibd &&
        #elif $infile.ext == 'analyze75'
            ln -s '${infile.extra_files_path}/hdr' infile.hdr &&
            ln -s '${infile.extra_files_path}/img' infile.img &&
            ln -s '${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 #################

library(Cardinal)
library(gridExtra)
library(lattice)

## Read MALDI Imaging dataset

#if $infile.ext == 'imzml'
    msidata = readImzML('infile')
#elif $infile.ext == 'analyze75'
    msidata = readAnalyze('infile')
#else
    load('infile.RData')
#end if


###################################### file properties in numbers ##############

## Number of features (mz)
maxfeatures = length(features(msidata))
## Range mz
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
minimumx = min(coord(msidata)[,1])
maximumx = max(coord(msidata)[,1])
## Range y coordinates
minimumy = min(coord(msidata)[,2])
maximumy = max(coord(msidata)[,2])
## Range of intensities
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)
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)[]) 
NumemptyTIC = sum(TICs == 0)

## Processing informations
processinginfo = processingData(msidata)
centroidedinfo = processinginfo@centroided # TRUE or FALSE

## 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 ##############

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:

if (ncol(input_list) == 1)
{
    input_list = cbind(input_list, input_list)
}

### calculate how many input masses are valid: 
inputmasses = input_list[input_list[,1]>minmz & input_list[,1]<maxmz,]

inputmz = inputmasses[,1]
inputnames = inputmasses[,2]

if (length(inputmz) == 1)
{
    countpixels = sum(spectra(msidata)[features(msidata, mz = inputmz), ] >0)
    percentpixels = round(countpixels/pixelcount*100, digits=1)
    valuesdataframe = cbind(inputmz, cbind(countpixels, percentpixels))
    write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
}else if (length(inputmz) >1) {
    countpixels = rowSums(spectra(msidata)[features(msidata, mz=inputmz),] >0)
    percentpixels = round(countpixels/pixelcount*100, digits=1)
    valuesdataframe = cbind(inputmz, cbind(countpixels, percentpixels))
    write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
}else{
    valuesdataframe = data.frame(0,0)
    write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
}

############################ 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",
               "Intensities > 0",
               "Number of zero TICs",
               "Preprocessing", 
               "Normalization", 
               "Smoothing",
               "Baseline reduction",
               "Peak picking",
               "Centroided", 
                paste0("# valid masses in \n", "$massfile.display_name"))

values = c(paste0(maxfeatures), 
           paste0(minmz, " - ", maxmz), 
           paste0(pixelcount), 
           paste0(minimumx, " - ", maximumx),  
           paste0(minimumy, " - ", maximumy), 
           paste0(minint, " - ", maxint), 
           paste0(medint),
           paste0(percpeaks, " %"), 
           paste0(NumemptyTIC), 
           paste0(" "),
           paste0(normalizationinfo),
           paste0(smoothinginfo),
           paste0(baselinereductioninfo),
           paste0(peakpickinginfo),
           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: 

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, 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="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>
        <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"/>
    </inputs>
    <outputs>
        <data format="pdf" name="plots" from_work_dir="heatmaps.pdf" label = "${tool.name} ${on_string}"/>
        <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"/>
                <composite_data value="Example_Continuous.ibd"/>
            </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"/>
            <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"/>
                <composite_data value="Analyze75.t2m"/>
            </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"/>
            <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="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="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. 
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 <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:

- 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 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

    ]]>
    </help>
    <citations>
        <citation type="doi">10.1093/bioinformatics/btv146</citation>
    </citations>
</tool>