Mercurial > repos > galaxyp > cardinal_segmentations

annotate segmentation.xml @ 0:be3e3cd14abc draft

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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
author galaxyp
date Mon, 01 Oct 2018 00:57:59 -0400
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be3e3cd14abc planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
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1 <tool id="cardinal_segmentations" name="MSI segmentation" version="@VERSION@.0">
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2 <description>mass spectrometry imaging spatial clustering</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="requirements">
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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 </expand>
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10 <command detect_errors="exit_code">
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11 <![CDATA[
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12
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13 @INPUT_LINKING@
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14 cat '${MSI_segmentation}' &&
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15 Rscript '${MSI_segmentation}'
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16
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17 ]]>
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18 </command>
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19 <configfiles>
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20 <configfile name="MSI_segmentation"><![CDATA[
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23 ################################# load libraries and read file #################
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25 library(Cardinal)
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26 library(gridExtra)
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27 library(lattice)
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29 @READING_MSIDATA@
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30
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32 ## create full matrix to make processed imzML files compatible with segmentation
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33 iData(msidata) <- iData(msidata)[]
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34
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35 @DATA_PROPERTIES@
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36
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37 ######################################## PDF ###################################
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38 ################################################################################
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39 ################################################################################
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42 pdf("segmentationpdf.pdf", fonts = "Times", pointsize = 12)
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43 plot(0,type='n',axes=FALSE,ann=FALSE)
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45 title(main=paste0("Spatial segmentation for file: \n\n", "$infile.display_name"))
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47
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48 ############################# I) numbers ####################################
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49 #############################################################################
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50 grid.table(property_df, rows= NULL)
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51
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52 if (npeaks > 0)
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53 {
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54
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55 ######################## II) segmentation tools #############################
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56 #############################################################################
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57 #set $color_string = ','.join(['"%s"' % $color.feature_color for $color in $colours])
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58 colourvector = c($color_string)
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59
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60 ### preparation for images and plots:
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61 #if str($image_cond.image_type) == "standard_image":
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62 print("standard image")
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63
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64 strip_input = TRUE
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65 lattice_input = FALSE
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66
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67 #elif str($image_cond.image_type) == "lattice_image":
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68 print("lattice image")
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69
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70 strip_input = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))
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71 lattice_input = TRUE
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72
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73 #end if
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75 ## set seed to make analysis reproducible
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76 set.seed($setseed)
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77
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78 #if str( $segm_cond.segmentationtool ) == 'pca':
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79 print('pca')
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80 ##pca
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81
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82 component_vector = character()
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83 for (numberofcomponents in 1:$segm_cond.pca_ncomp)
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84 {component_vector[numberofcomponents]= paste0("PC", numberofcomponents)}
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85 pca_result = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE,
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86 method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1))
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87
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88 ### images in pdf file
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89 print(image(pca_result, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector, ylim=c(maximumy+2, minimumy-2)))
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90 for (PCs in 1:$segm_cond.pca_ncomp){
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91 print(image(pca_result, column = c(paste0("PC",PCs)), lattice=lattice_input, superpose = FALSE, col.regions = risk.colors(100), ylim=c(maximumy+2, minimumy-2)))}
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92 ### plots in pdf file
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93 print(plot(pca_result, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input))
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94 for (PCs in 1:$segm_cond.pca_ncomp){
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95 print(plot(pca_result, column = c(paste0("PC",PCs)),superpose = FALSE))}
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96
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97 ### values in tabular files
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98 pcaloadings = (pca_result@resultData\$ncomp\$loadings) ### loading for each m/z value
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99 pcaloadings2 = cbind(matrix(unlist(strsplit(rownames(pcaloadings), " = ")), ncol=2, byrow=TRUE)[,2], pcaloadings)
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100 colnames(pcaloadings2) = c("mz", colnames(pcaloadings))
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101 pcascores = (pca_result@resultData\$ncomp\$scores) ### scores for each pixel
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102
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103 ## pixel names and coordinates
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104 pixel_names = gsub(", y = ", "_", rownames(pcascores))
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105 pixel_names = gsub(" = ", "y_", pixel_names)
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106 x_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,2]
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107 y_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,3]
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108 pcascores2 = data.frame(pixel_names, x_coordinates, y_coordinates, pcascores)
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109 colnames(pcascores2) = c("pixel names", "x", "y", colnames(pcascores))
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110 write.table(pcaloadings2, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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111 write.table(pcascores2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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112
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113 ## optional output as .RData
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114 #if $output_rdata:
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115 ## save as (.RData)
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116 save(pca, file="$segmentation_rdata")
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117
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118 #end if
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119
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120 #elif str( $segm_cond.segmentationtool ) == 'kmeans':
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121 print('kmeans')
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122 ##k-means
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123
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124 skm = spatialKMeans(msidata, r=c($segm_cond.kmeans_r), k=c($segm_cond.kmeans_k), method="$segm_cond.kmeans_method")
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125 print(image(skm, key=TRUE, main="K-means clustering", lattice=lattice_input, strip=strip_input, col= colourvector, layout=c(1,1), ylim=c(maximumy+2, minimumy-2)))
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126
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127 print(plot(skm, main="K-means plot", lattice=lattice_input, col= colourvector, strip=strip_input, layout=c(1,1)))
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128
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129 skm_clusters = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
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130 for (iteration in 1:length(skm@resultData)){
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131 skm_cluster = ((skm@resultData)[[iteration]]\$cluster)
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132 skm_clusters = cbind(skm_clusters, skm_cluster) }
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133
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134 ## pixel names and coordinates
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135 pixel_names = gsub(", y = ", "_", rownames(skm_clusters))
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136 pixel_names = gsub(" = ", "y_", pixel_names)
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137 x_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,2]
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138 y_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,3]
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139 skm_clusters2 = data.frame(pixel_names, x_coordinates, y_coordinates, skm_clusters)
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140 colnames(skm_clusters2) = c("pixel names", "x", "y",names(skm@resultData))
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141
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142 skm_toplabels = topLabels(skm, n=$segm_cond.kmeans_toplabels)
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143
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144 write.table(skm_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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145 write.table(skm_clusters2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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146
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147 ## optional output as .RData
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148 #if $output_rdata:
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149
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150 ## save as (.RData)
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151 save(skm, file="$segmentation_rdata")
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152
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153 #end if
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154
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155 #elif str( $segm_cond.segmentationtool ) == 'centroids':
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156 print('centroids')
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157 ##centroids
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158
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159 ssc = spatialShrunkenCentroids(msidata, r=c($segm_cond.centroids_r), k=c($segm_cond.centroids_k), s=c($segm_cond.centroids_s), method="$segm_cond.centroids_method")
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160 print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=lattice_input, strip = strip_input, col= colourvector,layout=c(1,1), ylim=c(maximumy+2, minimumy-2)))
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161 print(plot(ssc, main="Spatial shrunken centroids plot", lattice=lattice_input, col= colourvector, strip = strip_input,layout=c(1,1)))
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162 print(plot(ssc, mode = "tstatistics",key = TRUE, lattice=lattice_input, layout = c(1,1), main="t-statistics", col=colourvector))
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163 plot(summary(ssc), main = "Number of segments")
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164
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165 ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
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166 for (iteration in 1:length(ssc@resultData)){
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167 ssc_class = ((ssc@resultData)[[iteration]]\$classes)
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168 ssc_classes = cbind(ssc_classes, ssc_class) }
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169
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170 ## pixel names and coordinates
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171 pixel_names = gsub(", y = ", "_", rownames(ssc_classes))
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172 pixel_names = gsub(" = ", "y_", pixel_names)
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173 x_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,2]
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174 y_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,3]
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175 ssc_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, ssc_classes)
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176 colnames(ssc_classes2) = c("pixel names", "x", "y", names(ssc@resultData))
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177
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178 ssc_toplabels = topLabels(ssc, n=$segm_cond.centroids_toplabels)
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179
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180 write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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181 write.table(ssc_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
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182
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183 ## optional output as .RData
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184 #if $output_rdata:
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185
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186 ## save as (.RData)
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187 save(ssc, file="$segmentation_rdata")
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188
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189 #end if
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190
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191 #end if
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192
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193 dev.off()
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194
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195 }else{
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196 print("Inputfile has no intensities > 0")
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197 dev.off()
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198 }
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199
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200 ]]></configfile>
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201 </configfiles>
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202 <inputs>
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203 <expand macro="reading_msidata"/>
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204 <conditional name="segm_cond">
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205 <param name="segmentationtool" type="select" label="Select the tool for spatial clustering">
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206 <option value="pca" selected="True">pca</option>
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207 <option value="kmeans">k-means</option>
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208 <option value="centroids">spatial shrunken centroids</option>
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209 </param>
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210 <when value="pca">
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211 <param name="pca_ncomp" type="integer" value="2"
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212 label="The number of principal components to calculate"/>
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213 <param name="pca_method" type="select"
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214 label="The function used to calculate the singular value decomposition">
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215 <option value="irlba" selected="True">irlba</option>
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216 <option value="svd">svd</option>
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217 </param>
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218 <param name="pca_scale" type="boolean" truevalue="TRUE" falsevalue="FALSE" label="Scaling of data before analysis"/>
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219 </when>
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220
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221 <when value="kmeans">
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222 <param name="kmeans_r" type="text" value="2"
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223 label="The spatial neighborhood radius of nearby pixels to consider (r)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)">
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224 <expand macro="sanitizer_multiple_digits"/>
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225 </param>
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226 <param name="kmeans_k" type="text" value="3"
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227 label="The number of clusters (k)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)">
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228 <expand macro="sanitizer_multiple_digits"/>
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229 </param>
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230 <param name="kmeans_method" type="select" display="radio"
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231 label="The method to use to calculate the spatial smoothing kernels for the embedding. The 'gaussian' method refers to spatially-aware (SA) clustering, and 'adaptive' refers to spatially-aware structurally-adaptive (SASA) clustering">
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232 <option value="gaussian">gaussian</option>
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233 <option value="adaptive" selected="True">adaptive</option>
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234 </param>
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235 <param name="kmeans_toplabels" type="integer" value="500"
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236 label="Number of toplabels (m/z) which should be written in tabular output"/>
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237 </when>
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238
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239 <when value="centroids">
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240 <param name="centroids_r" type="text" value="2"
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241 label="The spatial neighborhood radius of nearby pixels to consider (r)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)">
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242 <expand macro="sanitizer_multiple_digits"/>
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243 </param>
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244 <param name="centroids_k" type="text" value="5"
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245 label="The initial number of clusters (k)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)">
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246 <expand macro="sanitizer_multiple_digits"/>
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247 </param>
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248 <param name="centroids_s" type="text" value="2"
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249 label="The sparsity thresholding parameter by which to shrink the t-statistics (s)"
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250 help="As s increases, fewer m/z features (m/z values) will be used in the spatial segmentation, and only the informative m/z features will be retained. Multiple values are allowed (e.g. 1,2,3 or 2:5)">
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251 <expand macro="sanitizer_multiple_digits"/>
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252 </param>
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253 <param name="centroids_method" type="select" display="radio" label="The method to use to calculate the spatial smoothing kernels for the embedding. The 'gaussian' method refers to spatially-aware (SA) weights, and 'adaptive' refers to spatially-aware structurally-adaptive (SASA) weights">
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254 <option value="gaussian">gaussian</option>
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255 <option value="adaptive" selected="True">adaptive</option>
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256 </param>
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257 <param name="centroids_toplabels" type="integer" value="500"
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258 label="Number of toplabels (m/z) which should be written in tabular output"/>
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259 </when>
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260 </conditional>
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261 <conditional name="image_cond">
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262 <param name="image_type" type="select" label="Select the image type">
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263 <option value="standard_image" selected="True">standard</option>
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264 <option value="lattice_image">lattice</option>
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265 </param>
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266 <when value="standard_image"/>
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267 <when value="lattice_image"/>
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268 </conditional>
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269 <repeat name="colours" title="Colours for the plots" min="1" max="50">
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270 <param name="feature_color" type="color" label="Colours" value="#ff00ff" help="Numbers of columns should be the same as number of components">
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271 <sanitizer>
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272 <valid initial="string.letters,string.digits">
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273 <add value="#" />
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274 </valid>
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275 </sanitizer>
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276 </param>
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277 </repeat>
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278 <param name="output_rdata" type="boolean" label="Results as .RData output"/>
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279 <param name="setseed" type="integer" value="1" label="set seed" help="Use same value to reproduce previous results"/>
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280 </inputs>
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281 <outputs>
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282 <data format="pdf" name="segmentationimages" from_work_dir="segmentationpdf.pdf" label = "${tool.name} on ${on_string}"/>
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283 <data format="tabular" name="mzfeatures" label="${tool.name} on ${on_string}: features"/>
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284 <data format="tabular" name="pixeloutput" label="${tool.name} on ${on_string}: pixels"/>
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285 <data format="rdata" name="segmentation_rdata" label="${tool.name} on ${on_string}: results.RData">
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286 <filter>output_rdata</filter>
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287 </data>
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288 </outputs>
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289 <tests>
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290 <test>
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291 <expand macro="infile_imzml"/>
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292 <param name="segmentationtool" value="pca"/>
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293 <param name="image_type" value="lattice_image"/>
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294 <repeat name="colours">
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295 <param name="feature_color" value="#ff00ff"/>
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296 </repeat>
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297 <repeat name="colours">
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298 <param name="feature_color" value="#0000FF"/>
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299 </repeat>
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300 <output name="segmentationimages" file="pca_imzml.pdf" compare="sim_size"/>
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301 <output name="mzfeatures" file="loadings_pca.tabular"/>
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302 <output name="pixeloutput" file="scores_pca.tabular"/>
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303 </test>
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304 <test>
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305 <expand macro="infile_analyze75"/>
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306 <param name="segmentationtool" value="kmeans"/>
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307 <param name="kmeans_r" value="1:3"/>
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308 <param name="kmeans_k" value="2,3"/>
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309 <param name="kmeans_toplabels" value="20"/>
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310 <repeat name="colours">
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311 <param name="feature_color" value="#ff00ff"/>
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312 </repeat>
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313 <repeat name="colours">
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314 <param name="feature_color" value="#0000FF"/>
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315 </repeat>
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316 <repeat name="colours">
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317 <param name="feature_color" value="#00C957"/>
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318 </repeat>
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319 <param name="output_rdata" value="True"/>
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320 <output name="segmentationimages" file="kmeans_analyze.pdf" compare="sim_size"/>
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321 <output name="mzfeatures" file="toplabels_skm.tabular"/>
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322 <output name="pixeloutput" file="cluster_skm.tabular"/>
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323 <output name="segmentation_rdata" file="cluster_skm.RData" compare="sim_size"/>
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324 </test>
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325 <test>
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326 <param name="infile" value="preprocessed.RData" ftype="rdata"/>
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327 <param name="segmentationtool" value="centroids"/>
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328 <param name="centroids_r" value="1,2"/>
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329 <param name="centroids_k" value="3"/>
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330 <param name="centroids_toplabels" value="50"/>
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331 <repeat name="colours">
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332 <param name="feature_color" value="#0000FF"/>
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333 </repeat>
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334 <repeat name="colours">
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335 <param name="feature_color" value="#00C957"/>
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336 </repeat>
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337 <repeat name="colours">
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338 <param name="feature_color" value="#B0171F"/>
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339 </repeat>
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340 <repeat name="colours">
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341 <param name="feature_color" value="#FFD700"/>
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342 </repeat>
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343 <repeat name="colours">
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344 <param name="feature_color" value="#848484"/>
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345 </repeat>
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346 <output name="segmentationimages" file="centroids_rdata.pdf" compare="sim_size"/>
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347 <output name="mzfeatures" file="toplabels_ssc.tabular"/>
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348 <output name="pixeloutput" file="classes_ssc.tabular"/>
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349 </test>
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350 </tests>
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351 <help>
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352 <![CDATA[
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353
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354 @CARDINAL_DESCRIPTION@
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355
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356 -----
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357
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358 This tool provides three different Cardinal functions for unsupervised clustering/spatial segmentation of mass spectrometry imaging data.
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359
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360 @MSIDATA_INPUT_DESCRIPTION@
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361
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362 **Options**
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363
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364 - PCA: principal component analysis
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365 - k-means: spatially-aware k-means clustering
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366 - spatial shrunken centroids: Allows the number of segments to decrease according to the data. This allows automatic selection of the number of clusters
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367
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368 **Output**
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369
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370 - Pdf with the heatmaps and plots for the segmentation
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371 - Tabular file with information on m/z and pixels: loadings/scores (PCA), toplabels/clusters (k-means), toplabels/classes (spatial shrunken centroids)
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372 - Optional .RData file which contains the segmentation results and can be used for further exploration in R using the Cardinal package
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373
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374 ]]>
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375 </help>
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376 <expand macro="citations"/>
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377 </tool>