Mercurial > repos > galaxyp > msi_ion_images

annotate msi_ion_images.xml @ 4:729a8bf3ffa9 draft

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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
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729a8bf3ffa9 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_ion_images commit 1c808d60243bb1eeda0cd26cb4b0a17ab05de2c0
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1 <tool id="mass_spectrometry_imaging_ion_images" name="MSI ion images" version="1.10.0.0">
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2 <description>
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3 mass spectrometry imaging heatmaps
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4 </description>
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5 <requirements>
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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 &&
729a8bf3ffa9 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_ion_images commit 1c808d60243bb1eeda0cd26cb4b0a17ab05de2c0
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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 '${infile.extra_files_path}/img' infile.img &&
729a8bf3ffa9 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_ion_images commit 1c808d60243bb1eeda0cd26cb4b0a17ab05de2c0
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18 ln -s '${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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31 library(Cardinal)
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32 library(gridExtra)
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33 library(lattice)
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35 ## Read MALDI Imaging dataset
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36
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37 #if $infile.ext == 'imzml'
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38 msidata = readImzML('infile')
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39 #elif $infile.ext == 'analyze75'
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40 msidata = readAnalyze('infile')
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41 #else
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42 load('infile.RData')
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43 #end if
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45
4
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46 ###################################### file properties in numbers ##############
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47
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48 ## Number of features (mz)
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49 maxfeatures = length(features(msidata))
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50 ## Range mz
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51 minmz = round(min(mz(msidata)), digits=2)
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52 maxmz = round(max(mz(msidata)), digits=2)
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53 ## Number of spectra (pixels)
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54 pixelcount = length(pixels(msidata))
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55 ## Range x coordinates
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56 minimumx = min(coord(msidata)[,1])
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57 maximumx = max(coord(msidata)[,1])
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58 ## Range y coordinates
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59 minimumy = min(coord(msidata)[,2])
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60 maximumy = max(coord(msidata)[,2])
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61 ## Range of intensities
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62 minint = round(min(spectra(msidata)[]), digits=2)
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63 maxint = round(max(spectra(msidata)[]), digits=2)
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64 medint = round(median(spectra(msidata)[]), digits=2)
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65 ## Number of intensities > 0
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66 npeaks= sum(spectra(msidata)[]>0)
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67 ## Spectra multiplied with mz (potential number of peaks)
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68 numpeaks = ncol(spectra(msidata)[])*nrow(spectra(msidata)[])
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69 ## Percentage of intensities > 0
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70 percpeaks = round(npeaks/numpeaks*100, digits=2)
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71 ## Number of empty TICs
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72 TICs = colSums(spectra(msidata)[])
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73 NumemptyTIC = sum(TICs == 0)
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74
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75 ## Processing informations
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76 processinginfo = processingData(msidata)
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77 centroidedinfo = processinginfo@centroided # TRUE or FALSE
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78
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79 ## if TRUE write processinginfo if no write FALSE
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80
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81 ## normalization
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82 if (length(processinginfo@normalization) == 0) {
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83 normalizationinfo='FALSE'
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84 } else {
4
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85 normalizationinfo=processinginfo@normalization
0
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86 }
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87 ## smoothing
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88 if (length(processinginfo@smoothing) == 0) {
4
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89 smoothinginfo='FALSE'
0
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90 } else {
4
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91 smoothinginfo=processinginfo@smoothing
0
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92 }
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93 ## baseline
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94 if (length(processinginfo@baselineReduction) == 0) {
4
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95 baselinereductioninfo='FALSE'
0
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96 } else {
4
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97 baselinereductioninfo=processinginfo@baselineReduction
0
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98 }
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99 ## peak picking
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100 if (length(processinginfo@peakPicking) == 0) {
4
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101 peakpickinginfo='FALSE'
0
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102 } else {
4
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103 peakpickinginfo=processinginfo@peakPicking
0
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104 }
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105
4
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106 ##################################### read and filter input masses ##############
2
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107
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108 input_list = read.delim("$massfile", header = FALSE, stringsAsFactors = FALSE)
4
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109
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110 ### in case input file had only one column with mz values but not names, duplicate mz values and use as names:
0
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111
4
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112 if (ncol(input_list) == 1)
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113 {
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114 input_list = cbind(input_list, input_list)
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115 }
2
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116
4
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117 ### calculate how many input masses are valid:
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118 inputmasses = input_list[input_list[,1]>minmz & input_list[,1]<maxmz,]
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119
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120 inputmz = inputmasses[,1]
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121 inputnames = inputmasses[,2]
1
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122
4
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123 if (length(inputmz) == 1)
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124 {
2
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125 countpixels = sum(spectra(msidata)[features(msidata, mz = inputmz), ] >0)
4
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126 percentpixels = round(countpixels/pixelcount*100, digits=1)
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127 valuesdataframe = cbind(inputmz, cbind(countpixels, percentpixels))
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128 write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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129 }else if (length(inputmz) >1) {
2
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130 countpixels = rowSums(spectra(msidata)[features(msidata, mz=inputmz),] >0)
4
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131 percentpixels = round(countpixels/pixelcount*100, digits=1)
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132 valuesdataframe = cbind(inputmz, cbind(countpixels, percentpixels))
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133 write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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134 }else{
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135 valuesdataframe = data.frame(0,0)
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136 write.table(valuesdataframe, file="$pixel_count", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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137 }
0
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138
4
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139 ############################ summarize file properties in numbers ##############
1
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140
0
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141 properties = c("Number of mz features",
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142 "Range of mz values [Da]",
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143 "Number of pixels",
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144 "Range of x coordinates",
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145 "Range of y coordinates",
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146 "Range of intensities",
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147 "Median of intensities",
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148 "Intensities > 0",
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149 "Number of zero TICs",
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150 "Preprocessing",
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151 "Normalization",
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152 "Smoothing",
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153 "Baseline reduction",
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154 "Peak picking",
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155 "Centroided",
2
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156 paste0("# valid masses in \n", "$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
1
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177
4
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178 ############################## PDF #############################################
0
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179
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180 pdf("heatmaps.pdf", fonts = "Times", pointsize = 12)
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181 plot(0,type='n',axes=FALSE,ann=FALSE)
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182 #if not $filename:
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183 #set $filename = $infile.display_name
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184 #end if
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185 title(main=paste("\nHeatmap images\n\n", "Filename:\n", "$filename"))
0
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186
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187 ############################# I) numbers ####################################
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188
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189 grid.table(property_df, rows= NULL)
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190
4
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191 ############################# II) images ####################################
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192
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193 ### only plot images when file has peaks and valid input mz:
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194
0
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195 if (npeaks > 0)
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196 {
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197 if (length(inputmz) != 0)
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198 {
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199 for (mass in 1:length(inputmz))
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200 {
1
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201 print(image(msidata, mz=inputmz[mass], strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)),
4
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202 lattice=TRUE, plusminus = $plusminus_dalton, contrast.enhance = "$image_contrast", smooth.image = "$image_smoothing",
1
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203 main= paste0(mass, ") ", inputnames[mass], " (", round(inputmz[mass], digits = 2)," ± ", $plusminus_dalton, " Da)")))
0
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204 }
1
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205 } else {print("The input masses were invalid")}
0
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206 dev.off()
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207 }else{
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208 print("inputfile has no intensities > 0")
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209 dev.off()
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210 }
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211 ]]></configfile>
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212 </configfiles>
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213 <inputs>
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214 <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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215 help="Upload composite datatype imzml (ibd+imzML) or analyze75 (hdr+img+t2m) or regular upload .RData (Cardinal MSImageSet)"/>
2
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216 <param name="filename" type="text" value="" label="Title" help="will appear in the quality report. If nothing given it will take the dataset name"/>
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217 <param name="massfile" type="data" format="tabular" label="Tabular file with masses and names"
0
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218 help="first column mass (m/z), second column mass name, tab separated file"/>
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219 <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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220 <option value="none" selected="True">none</option>
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221 <option value="suppression">suppression</option>
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222 <option value="histogram">histogram</option>
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223 </param>
2
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224 <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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225 <option value="none" selected="True">none</option>
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226 <option value="gaussian">gaussian</option>
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227 <option value="adaptive">adaptive</option>
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228 </param>
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229 <param name="plusminus_dalton" value="0.25" type="float" label="Mass range" help="plusminus mass window in Dalton"/>
0
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230 </inputs>
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231 <outputs>
3
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232 <data format="pdf" name="plots" from_work_dir="heatmaps.pdf" label = "${tool.name} ${on_string}"/>
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233 <data format="tabular" name="pixel_count" label="Number of peaks (intensity > 0) per mz"/>
0
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234 </outputs>
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235 <tests>
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236 <test>
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237 <param name="infile" value="" ftype="imzml">
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238 <composite_data value="Example_Continuous.imzML"/>
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239 <composite_data value="Example_Continuous.ibd"/>
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240 </param>
1
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241 <param name="massfile" value="inputpeptides.tabular" ftype="tabular"/>
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242 <param name="plusminus_dalton" value="0.25"/>
0
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243 <param name="filename" value="Testfile_imzml"/>
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244 <param name="image_contrast" value="histogram"/>
0
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245 <output name="plots" file="Heatmaps_imzml.pdf" compare="sim_size" delta="20000"/>
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246 <output name="pixel_count" file="tabular_imzml.tabular"/>
0
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247 </test>
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248 <test>
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249 <param name="infile" value="" ftype="analyze75">
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250 <composite_data value="Analyze75.hdr"/>
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251 <composite_data value="Analyze75.img"/>
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252 <composite_data value="Analyze75.t2m"/>
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253 </param>
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254 <param name="massfile" value="inputpeptides2.tabular" ftype="tabular"/>
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255 <param name="plusminus_dalton" value="0.5"/>
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256 <param name="filename" value="Testfile_analyze75"/>
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257 <param name="image_smoothing" value="gaussian"/>
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258 <output name="plots" file="Heatmaps_analyze75.pdf" compare="sim_size" delta="20000"/>
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259 <output name="pixel_count" file="tabular_analyze75.tabular"/>
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260 </test>
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261 <test>
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262 <param name="infile" value="preprocessed.rdata" ftype="rdata"/>
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263 <param name="massfile" value="inputpeptides.tabular" ftype="tabular"/>
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264 <param name="plusminus_dalton" value="0.5"/>
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265 <param name="filename" value="Testfile_rdata"/>
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266 <output name="plots" file="Heatmaps_rdata.pdf" compare="sim_size" delta="20000"/>
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267 <output name="pixel_count" file="tabular_rdata.tabular"/>
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268 </test>
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269 <test>
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270 <param name="infile" value="empty_spectra.rdata" ftype="rdata"/>
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271 <param name="massfile" value="inputpeptides2.tabular" ftype="tabular"/>
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272 <param name="plusminus_dalton" value="0.5"/>
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273 <param name="filename" value="Testfile_rdata"/>
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274 <output name="plots" file="Heatmaps_LM8_file16.pdf" compare="sim_size" delta="20000"/>
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275 <output name="pixel_count" file="tabular_LM8file16.tabular"/>
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276 </test>
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277 </tests>
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278 <help><![CDATA[
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279
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280
4
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281 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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282
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283 This tool uses the Cardinal image function to plot the intensity distribution of interesting masses of mass-spectrometry imaging data.
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284 Input data:
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285
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286 3 types of mass-spectrometry imaging data can be used:
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287
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288 - 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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289 - Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
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290 - Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData)
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291
4
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292 Tabular file with masses:
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293
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294 - 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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295 - first column must contain masses (separate point numbers by point, not comma)
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296 - optionally a second column with names for the masses can be provided
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297 - no empty fields or letters are allowed in the first column
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298
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299 Output:
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300
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301 - Pdf with the heatmap images
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302 - Tabular with masses that were in the mass range and their occurence over all pixels (absolute and in %)
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303
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304 Troubleshooting:
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305
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306 - no heatmaps are plotted when tabular file doesn't fulfill the criteria described above
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307 - no heatmaps are plotted when the input mass spectrometry imaging file has no intensities > 0
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308 - 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
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309
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310 ]]>
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311 </help>
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312 <citations>
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313 <citation type="doi">10.1093/bioinformatics/btv146</citation>
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314 </citations>
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315 </tool>