Mercurial > repos > galaxyp > msi_ion_images

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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_ion_images commit 5feaf3d0e0da8cef1241fecc1f4d6f81324594e6
author galaxyp
date Wed, 22 Aug 2018 13:33:23 -0400
parents dd44a98ce32c
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9a873d3e2aa9 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_ion_images commit 5feaf3d0e0da8cef1241fecc1f4d6f81324594e6
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1 <tool id="mass_spectrometry_imaging_ion_images" name="MSI ion images" version="1.10.0.5">
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2 <description>
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3 mass spectrometry imaging m/z heatmaps
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4 </description>
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5 <requirements>
4
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6 <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>
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7 <requirement type="package" version="2.2.1">r-gridextra</requirement>
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8 <requirement type="package" version="0.20-35">r-lattice</requirement>
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9 </requirements>
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10 <command detect_errors="aggressive">
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11 <![CDATA[
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12 #if $infile.ext == 'imzml'
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13 ln -s '${infile.extra_files_path}/imzml' infile.imzML &&
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14 ln -s '${infile.extra_files_path}/ibd' infile.ibd &&
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15 #elif $infile.ext == 'analyze75'
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16 ln -s '${infile.extra_files_path}/hdr' infile.hdr &&
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17 ln -s '${infile.extra_files_path}/img' infile.img &&
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18 ln -s '${infile.extra_files_path}/t2m' infile.t2m &&
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19 #else
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20 ln -s $infile infile.RData &&
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21 #end if
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22 cat '${MSI_heatmaps}' &&
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23 Rscript '${MSI_heatmaps}'
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24 ]]>
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25 </command>
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26 <configfiles>
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27 <configfile name="MSI_heatmaps"><![CDATA[
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29 ################################# load libraries and read file #################
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30
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31 library(Cardinal)
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32 library(gridExtra)
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33 library(lattice)
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34
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35 ## Read MALDI Imaging dataset
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36
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37
0
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38 #if $infile.ext == 'imzml'
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39 #if str($processed_cond.processed_file) == "processed":
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40 msidata <- readImzML('infile', mass.accuracy=$processed_cond.accuracy, units.accuracy = "$processed_cond.units")
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41 #else
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42 msidata <- readImzML('infile', attach.only=TRUE)
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43 #end if
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44 #elif $infile.ext == 'analyze75'
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45 msidata = readAnalyze('infile', attach.only=TRUE)
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46 #else
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47 loadRData <- function(fileName){
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48 load(fileName)
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49 get(ls()[ls() != "fileName"])
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50 }
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51 msidata = loadRData('infile.RData')
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52 #end if
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53
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54
4
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55 ###################################### file properties in numbers ##############
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56
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57 ## Number of features (m/z)
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58 maxfeatures = length(features(msidata))
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59 ## Range m/z
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60 minmz = round(min(mz(msidata)), digits=2)
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61 maxmz = round(max(mz(msidata)), digits=2)
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62 ## Number of spectra (pixels)
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63 pixelcount = length(pixels(msidata))
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64 ## Range x coordinates
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65 minimumx = min(coord(msidata)[,1])
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66 maximumx = max(coord(msidata)[,1])
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67 ## Range y coordinates
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68 minimumy = min(coord(msidata)[,2])
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69 maximumy = max(coord(msidata)[,2])
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70 ## Range of intensities
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71 minint = round(min(spectra(msidata)[], na.rm=TRUE), digits=2)
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72 maxint = round(max(spectra(msidata)[], na.rm=TRUE), digits=2)
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73 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
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74 ## Number of intensities > 0
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75 npeaks= sum(spectra(msidata)[]>0, na.rm=TRUE)
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76 ## Spectra multiplied with m/z (potential number of peaks)
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77 numpeaks = ncol(spectra(msidata)[])*nrow(spectra(msidata)[])
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78 ## Percentage of intensities > 0
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79 percpeaks = round(npeaks/numpeaks*100, digits=2)
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80 ## Number of empty TICs
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81 TICs = colSums(spectra(msidata)[], na.rm=TRUE)
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82 NumemptyTIC = sum(TICs == 0)
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83
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84 ## Processing informations
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85 processinginfo = processingData(msidata)
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86 centroidedinfo = processinginfo@centroided # TRUE or FALSE
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87
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88 ## if TRUE write processinginfo if no write FALSE
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89
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90 ## normalization
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91 if (length(processinginfo@normalization) == 0) {
4
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92 normalizationinfo='FALSE'
0
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93 } else {
4
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94 normalizationinfo=processinginfo@normalization
0
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95 }
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96 ## smoothing
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97 if (length(processinginfo@smoothing) == 0) {
4
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98 smoothinginfo='FALSE'
0
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99 } else {
4
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100 smoothinginfo=processinginfo@smoothing
0
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101 }
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102 ## baseline
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103 if (length(processinginfo@baselineReduction) == 0) {
4
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104 baselinereductioninfo='FALSE'
0
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105 } else {
4
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106 baselinereductioninfo=processinginfo@baselineReduction
0
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107 }
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108 ## peak picking
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109 if (length(processinginfo@peakPicking) == 0) {
4
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110 peakpickinginfo='FALSE'
0
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111 } else {
4
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112 peakpickinginfo=processinginfo@peakPicking
0
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113 }
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114
5
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115 ##################################### read and filter input m/z ##############
2
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116
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117 input_list = read.delim("$massfile", header = FALSE, stringsAsFactors = FALSE)
4
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118
5
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119 ### in case input file had only one column with m/z values but not names, duplicate m/z values and use as names:
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120 if (ncol(input_list) == 1){
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121 input_list = cbind(input_list, input_list)}
0
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122
5
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123 ### calculate how many input m/z are valid:
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124 inputmasses = input_list[input_list[,1]>minmz & input_list[,1]<maxmz,]
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125 inputmz = as.numeric(inputmasses[,1])
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126 inputnames = as.character(inputmasses[,2])
2
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127
1
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128
5
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129 ############################## PDF #############################################
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130 ################################################################################
0
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131
5
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132 pdf("heatmaps.pdf", fonts = "Times", pointsize = 12)
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133 plot(0,type='n',axes=FALSE,ann=FALSE)
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134 #if not $filename:
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135 #set $filename = $infile.display_name
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136 #end if
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137 title(main=paste("\nHeatmap images\n\n", "Filename:\n", "$filename"))
1
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138
5
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139 ############################# I) numbers ####################################
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140
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141 properties = c("Number of m/z features",
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142 "Range of m/z values",
0
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143 "Number of pixels",
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144 "Range of x coordinates",
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145 "Range of y coordinates",
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146 "Range of intensities",
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147 "Median of intensities",
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148 "Intensities > 0",
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149 "Number of zero TICs",
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150 "Preprocessing",
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151 "Normalization",
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152 "Smoothing",
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153 "Baseline reduction",
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154 "Peak picking",
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155 "Centroided",
5
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156 paste0("# valid m/z in \n", "$massfile.display_name"))
0
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157
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158 values = c(paste0(maxfeatures),
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159 paste0(minmz, " - ", maxmz),
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160 paste0(pixelcount),
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161 paste0(minimumx, " - ", maximumx),
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162 paste0(minimumy, " - ", maximumy),
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163 paste0(minint, " - ", maxint),
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164 paste0(medint),
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165 paste0(percpeaks, " %"),
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166 paste0(NumemptyTIC),
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167 paste0(" "),
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168 paste0(normalizationinfo),
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169 paste0(smoothinginfo),
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170 paste0(baselinereductioninfo),
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171 paste0(peakpickinginfo),
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172 paste0(centroidedinfo),
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173 paste0(length(inputmz), "/", length(input_list[,1])))
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174
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175 property_df = data.frame(properties, values)
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176
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177 grid.table(property_df, rows= NULL)
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178
4
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179 ############################# II) images ####################################
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180
5
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181 ### only plot images when file has peaks and valid input m/z:
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182
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183 if (npeaks > 0){
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184 if (length(inputmz) != 0){
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185 for (mass in 1:length(inputmz)){
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186
8
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187 ###standard image
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188
8
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189 #if str($image_cond.image_type) == "standard_image":
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190 print("standard image")
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191
8
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192 print(image(msidata, mz=inputmz[mass],plusminus = $plusminus_dalton, contrast.enhance = "$image_contrast",
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193 smooth.image = "$image_smoothing", strip=$strip, colorkey=$colorkey,ylim= c(maximumy+0.1*maximumy,minimumy-0.1*minimumy),
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194 main= paste0(inputnames[mass], " (", round(inputmz[mass], digits = 2)," ± ", $plusminus_dalton, " Da)")))}
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195
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196
8
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197 ###lattice image
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198
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199 #elif str($image_cond.image_type) == "lattice_image":
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200 print("lattice image")
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201
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202 #if str($strip) =="TRUE":
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203
8
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204 print(image(msidata, mz=inputmz[mass], strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)),
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205 lattice=TRUE, plusminus = $plusminus_dalton, contrast.enhance = "$image_contrast", smooth.image = "$image_smoothing",
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206 colorkey=$colorkey,ylim= c(maximumy+0.1*maximumy,minimumy-0.1*minimumy),
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207 main= paste0(inputnames[mass], " (", round(inputmz[mass], digits = 2)," ± ", $plusminus_dalton, " Da)")))}
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208
8
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209 #elif str($strip) =="FALSE":
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210
8
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211 print(image(msidata, mz=inputmz[mass], strip = $strip,
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212 lattice=TRUE, plusminus = $plusminus_dalton, contrast.enhance = "$image_contrast", smooth.image = "$image_smoothing",
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213 colorkey=$colorkey,
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214 main= paste0(inputnames[mass], " (", round(inputmz[mass], digits = 2)," ± ", $plusminus_dalton, " Da)")))}
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215
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216 #end if
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217
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218 #end if
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219
8
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220 ## optional svg output with original coordinates
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221 #if str($svg_cond.svg_pixelimage) == "yes_svg":
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222 print("svg pixel image")
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223
8
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224 svg(file="svg_pixel_output.svg")
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225 par(mar=c(0,0,0,0))
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226 image(msidata, mz = inputmz[1],strip=FALSE, ylim=c(maximumy, minimumy), plusminus = $plusminus_dalton,colorkey = FALSE,axes=FALSE, xlab=NA, ylab=NA,contrast.enhance = "$image_contrast", smooth.image = "$image_smoothing")
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227 dev.off()
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228 #end if
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229
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230 } else {print("The input m/z were invalid")}
0
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231 dev.off()
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232 }else{
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233 print("inputfile has no intensities > 0")
0
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234 dev.off()
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235 }
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236
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237
0
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238 ]]></configfile>
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239 </configfiles>
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240 <inputs>
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241 <param name="infile" type="data" format="imzml,rdata,analyze75" label="Inputfile as imzML, Analyze7.5 or Cardinal MSImageSet saved as RData"
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242 help="Upload composite datatype imzml (ibd+imzML) or analyze75 (hdr+img+t2m) or regular upload .RData (Cardinal MSImageSet)"/>
7
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243 <conditional name="processed_cond">
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244 <param name="processed_file" type="select" label="Is the input file a processed imzML file ">
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245 <option value="no_processed" selected="True">not a processed imzML</option>
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246 <option value="processed">processed imzML</option>
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247 </param>
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248 <when value="no_processed"/>
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249 <when value="processed">
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250 <param name="accuracy" type="float" value="50" label="Mass accuracy to which the m/z values will be binned" help="This should be set to the native accuracy of the mass spectrometer, if known"/>
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251 <param name="units" display="radio" type="select" label="Unit of the mass accuracy" help="either m/z or ppm">
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252 <option value="mz" >mz</option>
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253 <option value="ppm" selected="True" >ppm</option>
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254 </param>
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255 </when>
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256 </conditional>
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257 <param name="filename" type="text" value="" label="Title" help="will appear in the pdf output. If nothing given it will take the dataset name"/>
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258 <param name="massfile" type="data" format="tabular" label="Tabular file with m/z and names"
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259 help="first column m/z, second column m/z name, tab separated file"/>
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260 <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">
1
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261 <option value="none" selected="True">none</option>
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262 <option value="suppression">suppression</option>
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263 <option value="histogram">histogram</option>
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264 </param>
2
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265 <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">
1
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266 <option value="none" selected="True">none</option>
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267 <option value="gaussian">gaussian</option>
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268 <option value="adaptive">adaptive</option>
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269 </param>
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270 <param name="plusminus_dalton" value="0.25" type="float" label="M/z range" help="plusminus m/z window"/>
5
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271 <param name="strip" type="boolean" checked="True" display="radio" truevalue="TRUE" falsevalue="FALSE" label="Display m/z value in plot"/>
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272 <param name="colorkey" type="boolean" checked="True" display="radio" truevalue="TRUE" falsevalue="FALSE" label="Display colorkey in plot"/>
5
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273 <conditional name="image_cond">
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274 <param name="image_type" type="select" label="Select the image type">
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275 <option value="standard_image" selected="True">standard</option>
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276 <option value="lattice_image">lattice</option>
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277 </param>
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278 <when value="standard_image"/>
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279 <when value="lattice_image"/>
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280 </conditional>
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281 <conditional name="svg_cond">
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282 <param name="svg_pixelimage" type="select" label="Do you want to export a heatmap with the original coordinates as svg image, the first valid m/z from tabular file is used">
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283 <option value="no_svg" selected="True">No svg image export</option>
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284 <option value="yes_svg">Export svg image</option>
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285 </param>
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286 <when value="no_svg"/>
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287 <when value="yes_svg"/>
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288 </conditional>
0
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289 </inputs>
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290 <outputs>
5
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291 <data format="pdf" name="plots" from_work_dir="heatmaps.pdf" label = "$infile.display_name heatmaps"/>
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292 <data format="svg" name="svg_output" from_work_dir="svg_pixel_output.svg" label="$infile.display_name svg">
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293 <filter>svg_cond['svg_pixelimage'] == 'yes_svg'</filter>
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294 </data>
0
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295 </outputs>
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296 <tests>
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297 <test>
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298 <param name="infile" value="" ftype="imzml">
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299 <composite_data value="Example_Continuous.imzML"/>
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300 <composite_data value="Example_Continuous.ibd"/>
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301 </param>
1
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302 <param name="massfile" value="inputpeptides.tabular" ftype="tabular"/>
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303 <param name="plusminus_dalton" value="0.25"/>
0
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304 <param name="filename" value="Testfile_imzml"/>
1
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305 <param name="image_contrast" value="histogram"/>
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306 <param name="strip" value="True"/>
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307 <param name="colorkey" value="True"/>
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308 <param name="image_type" value="lattice_image"/>
0
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309 <output name="plots" file="Heatmaps_imzml.pdf" compare="sim_size" delta="20000"/>
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310 </test>
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311 <test>
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312 <param name="infile" value="" ftype="analyze75">
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313 <composite_data value="Analyze75.hdr"/>
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314 <composite_data value="Analyze75.img"/>
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315 <composite_data value="Analyze75.t2m"/>
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316 </param>
1
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317 <param name="massfile" value="inputpeptides2.tabular" ftype="tabular"/>
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318 <param name="plusminus_dalton" value="0.5"/>
0
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319 <param name="filename" value="Testfile_analyze75"/>
1
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320 <param name="image_smoothing" value="gaussian"/>
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321 <param name="strip" value="False"/>
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322 <param name="colorkey" value="True"/>
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323 <conditional name="svg_cond">
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324 <param name="svg_pixelimage" value="yes_svg"/>
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325 </conditional>
0
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326 <output name="plots" file="Heatmaps_analyze75.pdf" compare="sim_size" delta="20000"/>
8
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327 <output name="svg_output" file="analyze75.svg" compare="sim_size" delta="20000"/>
0
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328 </test>
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329 <test>
4
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330 <param name="infile" value="preprocessed.rdata" ftype="rdata"/>
1
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331 <param name="massfile" value="inputpeptides.tabular" ftype="tabular"/>
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332 <param name="plusminus_dalton" value="0.5"/>
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333 <param name="strip" value="True"/>
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334 <param name="colorkey" value="True"/>
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335 <param name="image_type" value="lattice_image"/>
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336 <param name="filename" value="Testfile_rdata"/>
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337 <output name="plots" file="Heatmaps_rdata.pdf" compare="sim_size" delta="20000"/>
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338 </test>
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339 <test>
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340 <param name="infile" value="empty_spectra.rdata" ftype="rdata"/>
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341 <param name="massfile" value="inputpeptides2.tabular" ftype="tabular"/>
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342 <param name="plusminus_dalton" value="0.5"/>
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343 <param name="strip" value="True"/>
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344 <param name="colorkey" value="False"/>
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345 <param name="filename" value="Testfile_rdata"/>
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346 <output name="plots" file="Heatmaps_LM8_file16.pdf" compare="sim_size" delta="20000"/>
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347 </test>
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348 </tests>
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349 <help><![CDATA[
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350
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351
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352 Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_
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353
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354 This tool uses the Cardinal image function to plot the intensity distribution of interesting m/z of mass spectrometry imaging data.
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355 Input data:
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356
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357 3 types of mass spectrometry imaging data can be used:
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358
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359 - imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_
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360 - Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
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361 - Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData)
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362
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363 Tabular file with m/z:
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364
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365 - 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))
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366 - first column must contain m/z (separate point numbers by point, not comma)
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367 - optionally a second column with names for the m/z can be provided
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368 - no empty fields or letters are allowed in the first column
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369
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370 Output:
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371
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372 - Pdf with the heatmap images
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373 - optional svg file of a mass-spectrometry imaging m/z heatmap
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374
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375 Troubleshooting:
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376
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377 - no heatmaps are plotted when tabular file doesn't fulfill the criteria described above
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378 - no heatmaps are plotted when the input mass spectrometry imaging file has no intensities > 0
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379 - the contrast enhance and image smoothing functions require a certain number of m/z with intensities > 0 (empirical value > 2% of spectra)
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380 - the standard image function should work for all files while the lattice function works not on every file (nicely)
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381
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382 ]]>
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383 </help>
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384 <citations>
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385 <citation type="doi">10.1093/bioinformatics/btv146</citation>
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386 </citations>
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387 </tool>