<tool id="mass_spectrometry_imaging_ion_images" name="MSI ion images" version="1.7.0">
    <description>
        mass spectrometry imaging heatmaps
    </description>
    <requirements>
        <requirement type="package" version="1.7.0">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 &&
            cp '${infile.extra_files_path}/ibd' infile.ibd &&
        #elif $infile.ext == 'analyze75'
            cp '${infile.extra_files_path}/hdr' infile.hdr &&
            cp '${infile.extra_files_path}/img' infile.img &&
            cp '${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(KernSmooth)
library(lattice)

## Read MALDI Imaging dataset

#if $infile.ext == 'imzml'
    msidata <- readMSIData('infile.imzML')
#elif $infile.ext == 'analyze75'
    msidata <- readMSIData('infile.hdr')
#else
    load('infile.RData')
#end if

#if $massfile:
    ### Read tabular file with peptide masses for plots and heatmap images: 
    input_list = read.delim("$massfile", 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")

###################################### 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
}

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

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

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 ", "$filename"))

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 = $infile.display_name
#end if
title(main=paste("Quality control of MSI data\n\n", "Filename:", "$filename"))

############################# I) numbers ####################################
#############################################################################

grid.table(property_df, rows= NULL)

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 = $plusminusinDalton, contrast.enhance = "histogram", 
              main= paste0(mass, ") ", inputnames[mass], " (", round(inputmz[mass], digits = 2)," ± ", $plusminusinDalton, " Da)")))
      }
    } else {print("The input masses were outside the mass range")}

    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" optional="true" format="tabular" label="Text file with masses and names"
            help="first column mass (m/z), second column mass name, tab separated file"/>
        <param name="plusminusinDalton" 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 $infile.display_name"/>
    </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.csv" ftype="tabular"/>
            <param name="plusminusinDalton" value="0.25"/>
            <param name="filename" value="Testfile_imzml"/>
            <output name="plots" file="Heatmaps_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="massfile" value="inputpeptides.txt" ftype="tabular"/>
            <param name="plusminusinDalton" value="0.5"/>
            <param name="filename" value="Testfile_analyze75"/>
            <output name="plots" file="Heatmaps_analyze75.pdf" compare="sim_size" delta="20000"/>
        </test>
        <test>
            <param name="infile" value="preprocessing_results1.RData" ftype="rdata"/>
            <param name="massfile" value="inputpeptides.csv" ftype="tabular"/>
            <param name="plusminusinDalton" value="0.1"/>
            <param name="filename" value="Testfile_rdata"/>
            <output name="plots" file="Heatmaps_rdata.pdf" compare="sim_size" delta="20000"/>
        </test>
        <test>
            <param name="infile" value="LM8_file16.rdata" ftype="rdata"/>
            <param name="massfile" value="inputpeptides.txt" ftype="tabular"/>
            <param name="plusminusinDalton" value="0.1"/>
            <param name="filename" value="Testfile_rdata"/>
            <output name="plots" file="Heatmaps_LM8_file16.pdf" compare="sim_size" delta="20000"/>
        </test>
    </tests>
    <help><![CDATA[

Heatmaps for different ion masses in mass-spectrometry imaging data. 

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 heatmaps for every ion mass of interest as pdf. 

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