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comparison segmentation_tool.xml @ 1:d4158c9955ea draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/msi_segmentation commit edbf2a6cb50fb04d0db56a7557a64e3bb7a0806a
author galaxyp
date Thu, 01 Mar 2018 08:26:19 -0500
parents 0c1a9b68f436
children f66c5789deac
comparison
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0:0c1a9b68f436 1:d4158c9955ea
1 <tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.7.0"> 1 <tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.7.0.1">
2 <description>tool for spatial clustering</description> 2 <description>tool for spatial clustering</description>
3 <requirements> 3 <requirements>
4 <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement> 4 <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement>
5 <requirement type="package" version="2.2.1">r-gridextra</requirement> 5 <requirement type="package" version="2.2.1">r-gridextra</requirement>
6 <requirement type="package" version="2.23-15">r-kernsmooth</requirement> 6 <requirement type="package" version="2.23-15">r-kernsmooth</requirement>
38 library(lattice) 38 library(lattice)
39 39
40 ## Read MALDI Imaging dataset 40 ## Read MALDI Imaging dataset
41 41
42 #if $infile.ext == 'imzml' 42 #if $infile.ext == 'imzml'
43 msidata <- readMSIData('infile.imzML') 43 msidata = readMSIData('infile.imzML')
44 #elif $infile.ext == 'analyze75' 44 #elif $infile.ext == 'analyze75'
45 msidata <- readMSIData('infile.hdr') 45 msidata = readMSIData('infile.hdr')
46 #else 46 #else
47 load('infile.RData') 47 load('infile.RData')
48 #end if 48 #end if
49 49
50 ###################################### file properties in numbers ###################### 50 ###################################### file properties in numbers ######################
175 ##pca 175 ##pca
176 176
177 component_vector = character() 177 component_vector = character()
178 for (numberofcomponents in 1:$segm_cond.pca_ncomp) 178 for (numberofcomponents in 1:$segm_cond.pca_ncomp)
179 {component_vector[numberofcomponents]= paste0("PC", numberofcomponents)} 179 {component_vector[numberofcomponents]= paste0("PC", numberofcomponents)}
180 pca <- PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE, 180 pca = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE,
181 method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1)) 181 method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1))
182 182
183 print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.pca_imagecontrast", smooth.image = "$segm_cond.pca_imagesmoothing", col=colourvector)) 183 print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.pca_imagecontrast", smooth.image = "$segm_cond.pca_imagesmoothing", col=colourvector, ylim=c(maximumy+2, 0)))
184 print(plot(pca, main="PCA plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)))) 184 print(plot(pca, main="PCA plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))))
185 185
186 186
187 pcaloadings = (pca@resultData\$ncomp\$loadings) ### loading for each mz value 187 pcaloadings = (pca@resultData\$ncomp\$loadings) ### loading for each mz value
188 pcascores = (pca@resultData\$ncomp\$scores) ### scores for each pixel 188 pcascores = (pca@resultData\$ncomp\$scores) ### scores for each pixel
192 192
193 #elif str( $segm_cond.segmentationtool ) == 'kmeans': 193 #elif str( $segm_cond.segmentationtool ) == 'kmeans':
194 print('kmeans') 194 print('kmeans')
195 ##k-means 195 ##k-means
196 196
197 skm <- spatialKMeans(msidata, r=$segm_cond.kmeans_r, k=$segm_cond.kmeans_k, method="$segm_cond.kmeans_method") 197 skm = spatialKMeans(msidata, r=$segm_cond.kmeans_r, k=$segm_cond.kmeans_k, method="$segm_cond.kmeans_method")
198 print(image(skm, key=TRUE, main="K-means clustering", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.kmeans_imagecontrast", col= colourvector, smooth.image = "$segm_cond.kmeans_imagesmoothing")) 198 print(image(skm, key=TRUE, main="K-means clustering", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.kmeans_imagecontrast", col= colourvector, smooth.image = "$segm_cond.kmeans_imagesmoothing", ylim=c(maximumy+2, 0)))
199 print(plot(skm, main="K-means plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)))) 199 print(plot(skm, main="K-means plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))))
200 200
201 201
202 skm_clusters = (skm@resultData\$r\$cluster) 202 skm_clusters = (skm@resultData\$r\$cluster)
203 skm_toplabels = topLabels(skm, n=500) 203 skm_toplabels = topLabels(skm, n=500)
208 208
209 #elif str( $segm_cond.segmentationtool ) == 'centroids': 209 #elif str( $segm_cond.segmentationtool ) == 'centroids':
210 print('centroids') 210 print('centroids')
211 ##centroids 211 ##centroids
212 212
213 ssc <- spatialShrunkenCentroids(msidata, r=$segm_cond.centroids_r, k=$segm_cond.centroids_k, s=$segm_cond.centroids_s, method="$segm_cond.centroids_method") 213 ssc = spatialShrunkenCentroids(msidata, r=$segm_cond.centroids_r, k=$segm_cond.centroids_k, s=$segm_cond.centroids_s, method="$segm_cond.centroids_method")
214 print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.centroids_imagecontrast", col= colourvector, smooth.image = "$segm_cond.centroids_imagesmoothing")) 214 print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.centroids_imagecontrast", col= colourvector, smooth.image = "$segm_cond.centroids_imagesmoothing", ylim=c(maximumy+2, 0)))
215 print(plot(ssc, main="Spatial shrunken centroids plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)))) 215 print(plot(ssc, main="Spatial shrunken centroids plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))))
216 216
217 ssc_classes = (ssc@resultData\$r\$classes) 217 ssc_classes = (ssc@resultData\$r\$classes)
218 ssc_toplabels = topLabels(ssc, n=500) 218 ssc_toplabels = topLabels(ssc, n=500)
219 219
340 </repeat> 340 </repeat>
341 <repeat name="colours"> 341 <repeat name="colours">
342 <param name="feature_color" value="#0000FF"/> 342 <param name="feature_color" value="#0000FF"/>
343 </repeat> 343 </repeat>
344 <output name="segmentationimages" file="pca_imzml.pdf" compare="sim_size" delta="20000"/> 344 <output name="segmentationimages" file="pca_imzml.pdf" compare="sim_size" delta="20000"/>
345 <output name="mzfeatures" file="pcaloadings_results1.txt" compare="sim_size"/> 345 <output name="mzfeatures" file="loadings_pca.tabular" compare="sim_size"/>
346 <output name="pixeloutput" file="pcascores_results1.txt" compare="sim_size"/> 346 <output name="pixeloutput" file="scores_pca.tabular" compare="sim_size"/>
347 </test> 347 </test>
348 <test> 348 <test>
349 <param name="infile" value="" ftype="analyze75"> 349 <param name="infile" value="" ftype="analyze75">
350 <composite_data value="Analyze75.hdr" /> 350 <composite_data value="Analyze75.hdr" />
351 <composite_data value="Analyze75.img" /> 351 <composite_data value="Analyze75.img" />
360 </repeat> 360 </repeat>
361 <repeat name="colours"> 361 <repeat name="colours">
362 <param name="feature_color" value="#00C957"/> 362 <param name="feature_color" value="#00C957"/>
363 </repeat> 363 </repeat>
364 <output name="segmentationimages" file="kmeans_imzml.pdf" compare="sim_size" delta="20000"/> 364 <output name="segmentationimages" file="kmeans_imzml.pdf" compare="sim_size" delta="20000"/>
365 <output name="mzfeatures" file="toplabels_results1.txt" compare="sim_size"/> 365 <output name="mzfeatures" file="toplabels_skm.tabular" compare="sim_size"/>
366 <output name="pixeloutput" file="cluster_results1.txt" compare="sim_size"/> 366 <output name="pixeloutput" file="cluster_skm.tabular" compare="sim_size"/>
367 </test> 367 </test>
368 <test> 368 <test>
369 <param name="infile" value="preprocessing_results1.RData" ftype="rdata"/> 369 <param name="infile" value="preprocessing_results1.RData" ftype="rdata"/>
370 <param name="segmentationtool" value="centroids"/> 370 <param name="segmentationtool" value="centroids"/>
371 <repeat name="colours"> 371 <repeat name="colours">
382 </repeat> 382 </repeat>
383 <repeat name="colours"> 383 <repeat name="colours">
384 <param name="feature_color" value="#848484"/> 384 <param name="feature_color" value="#848484"/>
385 </repeat> 385 </repeat>
386 <output name="segmentationimages" file="centroids_imzml.pdf" compare="sim_size" delta="20000"/> 386 <output name="segmentationimages" file="centroids_imzml.pdf" compare="sim_size" delta="20000"/>
387 <output name="mzfeatures" file="toplabels_results1.txt" compare="sim_size"/> 387 <output name="mzfeatures" file="toplabels_ssc.tabular" compare="sim_size"/>
388 <output name="pixeloutput" file="classes_results1.txt" compare="sim_size"/> 388 <output name="pixeloutput" file="classes_ssc.tabular" compare="sim_size"/>
389 </test> 389 </test>
390 </tests> 390 </tests>
391 <help> 391 <help>
392 <![CDATA[ 392 <![CDATA[
393 393