Mercurial > repos > galaxyp > mass_spectrometry_imaging_segmentations

comparison segmentation_tool.xml @ 2:f66c5789deac draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/msi_segmentation commit ed7d3e6f1a09c78c8f71cc1bdc1a20249767f646
author galaxyp
date Sun, 11 Mar 2018 10:39:01 -0400
parents d4158c9955ea
children 830c6df59603
comparison
equal deleted inserted replaced
1:d4158c9955ea 2:f66c5789deac
1 <tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.7.0.1"> 1 <tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.7.0.2">
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>
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, ylim=c(maximumy+2, 0))) 183 print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), 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=c($segm_cond.kmeans_r), k=c($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 = "$segm_cond.kmeans_imagesmoothing", ylim=c(maximumy+2, 0))) 198 print(image(skm, key=TRUE, main="K-means clustering", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col= colourvector, ylim=c(maximumy+2, 0), layout=c(1,1)))
199 print(plot(skm, main="K-means plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)))) 199
200 200 print(plot(skm, main="K-means plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), layout=c($segm_cond.kmeans_layout)))
201 201
202 skm_clusters = (skm@resultData\$r\$cluster) 202
203 skm_toplabels = topLabels(skm, n=500) 203 skm_clusters = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
204 for (iteration in 1:length(skm@resultData)){
205 skm_cluster = ((skm@resultData)[[iteration]]\$cluster)
206 skm_clusters = cbind(skm_clusters, skm_cluster) }
207 colnames(skm_clusters) = names((skm@resultData))
208
209 skm_toplabels = topLabels(skm, n=$segm_cond.kmeans_toplabels)
204 210
205 write.table(skm_toplabels, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 211 write.table(skm_toplabels, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
206 write.table(skm_clusters, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 212 write.table(skm_clusters, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
207 213
208 214
209 #elif str( $segm_cond.segmentationtool ) == 'centroids': 215 #elif str( $segm_cond.segmentationtool ) == 'centroids':
210 print('centroids') 216 print('centroids')
211 ##centroids 217 ##centroids
212 218
213 ssc = spatialShrunkenCentroids(msidata, r=$segm_cond.centroids_r, k=$segm_cond.centroids_k, s=$segm_cond.centroids_s, method="$segm_cond.centroids_method") 219 ssc = spatialShrunkenCentroids(msidata, r=c($segm_cond.centroids_r), k=c($segm_cond.centroids_k), s=c($segm_cond.centroids_s), method="$segm_cond.centroids_method")
214 print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.centroids_imagecontrast", col= colourvector, smooth.image = "$segm_cond.centroids_imagesmoothing", ylim=c(maximumy+2, 0))) 220 print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col= colourvector, ylim=c(maximumy+2, 0),layout=c(1,1)))
215 print(plot(ssc, main="Spatial shrunken centroids plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)))) 221 print(plot(ssc, main="Spatial shrunken centroids plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)),layout=c($segm_cond.centroids_layout)))
216 222
217 ssc_classes = (ssc@resultData\$r\$classes) 223 ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
218 ssc_toplabels = topLabels(ssc, n=500) 224 for (iteration in 1:length(ssc@resultData)){
225 ssc_class = ((ssc@resultData)[[iteration]]\$classes)
226 ssc_classes = cbind(ssc_classes, ssc_class) }
227 colnames(ssc_classes) = names((ssc@resultData))
228
229 ssc_toplabels = topLabels(ssc, n=$segm_cond.centroids_toplabels)
219 230
220 write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 231 write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
221 write.table(ssc_classes, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 232 write.table(ssc_classes, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
222 233
223 234
253 <param name="pca_scale" type="select" display="radio" optional="False" 264 <param name="pca_scale" type="select" display="radio" optional="False"
254 label="Shoud the data be scaled first?"> 265 label="Shoud the data be scaled first?">
255 <option value="TRUE">yes</option> 266 <option value="TRUE">yes</option>
256 <option value="FALSE" selected="True">no</option> 267 <option value="FALSE" selected="True">no</option>
257 </param> 268 </param>
258 <param name="pca_imagecontrast" type="select" label="Select a contrast enhancement function." help="The 'histogram' equalization method flatterns the distribution of intensities. The hotspot 'suppression' method uses thresholding to reduce the intensities of hotspots">
259 <option value="none" selected="True">none</option>
260 <option value="suppression">suppression</option>
261 <option value="histogram">histogram</option>
262 </param>
263 <param name="pca_imagesmoothing" type="select" label="Select an image smoothing function." help="The 'gaussian' smoothing method smooths images with a simple gaussian kernel. The 'adaptive' method uses bilateral filtering to preserve edges.">
264 <option value="none" selected="True">none</option>
265 <option value="gaussian">gaussian</option>
266 <option value="adaptive">adaptive</option>
267 </param>
268 </when> 269 </when>
269 270
270 <when value="kmeans"> 271 <when value="kmeans">
271 <param name="kmeans_r" type="text" value="2" 272 <param name="kmeans_r" type="text" value="2"
272 label="The spatial neighborhood radius of nearby pixels to consider (r)."/> 273 label="The spatial neighborhood radius of nearby pixels to consider (r)." help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>
273 <param name="kmeans_k" type="text" value="3" 274 <param name="kmeans_k" type="text" value="3"
274 label="The number of clusters (k)."/> 275 label="The number of clusters (k)." help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>
275 <param name="kmeans_method" type="select" display="radio" 276 <param name="kmeans_method" type="select" display="radio"
276 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."> 277 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.">
277 <option value="gaussian">gaussian</option> 278 <option value="gaussian">gaussian</option>
278 <option value="adaptive" selected="True">adaptive</option> 279 <option value="adaptive" selected="True">adaptive</option>
279 </param> 280 </param>
280 <param name="kmeans_imagecontrast" type="select" label="Select a contrast enhancement function." help="The 'histogram' equalization method flatterns the distribution of intensities. The hotspot 'suppression' method uses thresholding to reduce the intensities of hotspots"> 281 <param name="kmeans_toplabels" type="integer" value="500"
281 <option value="none" selected="True">none</option> 282 label="Number of toplabels (masses) which should be written in tabular output"/>
282 <option value="suppression">suppression</option> 283 <param name="kmeans_layout" type="text" value="1,1"
283 <option value="histogram">histogram</option> 284 label="Number of rows and columns to plot pictures in pdf output" help="e.g. 1,1 means 1 plot per page; 2,3 means 2 rows with 3 plots each = 6 plots per page"/>
284 </param> 285 </when>
285 <param name="kmeans_imagesmoothing" type="select" label="Select an image smoothing function." help="The 'gaussian' smoothing method smooths images with a simple gaussian kernel. The 'adaptive' method uses bilateral filtering to preserve edges.">
286 <option value="none" selected="True">none</option>
287 <option value="gaussian">gaussian</option>
288 <option value="adaptive">adaptive</option>
289 </param>
290 </when>
291 286
292 <when value="centroids"> 287 <when value="centroids">
293 <param name="centroids_r" type="text" value="2" 288 <param name="centroids_r" type="text" value="2"
294 label="The spatial neighborhood radius of nearby pixels to consider (r)."/> 289 label="The spatial neighborhood radius of nearby pixels to consider (r)." help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>
295 <param name="centroids_k" type="text" value="5" 290 <param name="centroids_k" type="text" value="5"
296 label="The initial number of clusters (k)."/> 291 label="The initial number of clusters (k)." help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>
297 <param name="centroids_s" type="integer" value="2" 292 <param name="centroids_s" type="text" value="2"
298 label="The sparsity thresholding parameter by which to shrink the t-statistics (s)." 293 label="The sparsity thresholding parameter by which to shrink the t-statistics (s)."
299 help="As s increases, fewer mass features (m/z values) will be used in the spatial segmentation, and only the informative mass features will be retained."/> 294 help="As s increases, fewer mass features (m/z values) will be used in the spatial segmentation, and only the informative mass features will be retained. Multiple values are allowed (e.g. 1,2,3 or 2:5)."/>
300 <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."> 295 <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.">
301 <option value="gaussian" selected="True">gaussian</option> 296 <option value="gaussian" selected="True">gaussian</option>
302 <option value="adaptive">adaptive</option> 297 <option value="adaptive">adaptive</option>
303 </param> 298 </param>
304 <param name="centroids_imagecontrast" type="select" label="Select a contrast enhancement function." help="The 'histogram' equalization method flatterns the distribution of intensities. The hotspot 'suppression' method uses thresholding to reduce the intensities of hotspots"> 299 <param name="centroids_toplabels" type="integer" value="500"
305 <option value="none" selected="True">none</option> 300 label="Number of toplabels (masses) which should be written in tabular output"/>
306 <option value="suppression">suppression</option> 301 <param name="centroids_layout" type="text" value="1,1"
307 <option value="histogram">histogram</option> 302 label="Number of rows and columns to plot pictures in pdf output" help="e.g. 1,1 means 1 plot per page; 2,3 means 2 rows with 3 plots each = 6 plots per page"/>
308 </param>
309 <param name="centroids_imagesmoothing" type="select" label="Select an image smoothing function." help="The 'gaussian' smoothing method smooths images with a simple gaussian kernel. The 'adaptive' method uses bilateral filtering to preserve edges.">
310 <option value="none" selected="True">none</option>
311 <option value="gaussian">gaussian</option>
312 <option value="adaptive">adaptive</option>
313 </param>
314 </when> 303 </when>
315 </conditional> 304 </conditional>
316 <repeat name="colours" title="Colours for the plots" min="1" max="50"> 305 <repeat name="colours" title="Colours for the plots" min="1" max="50">
317 <param name="feature_color" type="color" label="Colours" value="#ff00ff" help="Numbers of columns should be the same as number of components"> 306 <param name="feature_color" type="color" label="Colours" value="#ff00ff" help="Numbers of columns should be the same as number of components">
318 <sanitizer> 307 <sanitizer>
350 <composite_data value="Analyze75.hdr" /> 339 <composite_data value="Analyze75.hdr" />
351 <composite_data value="Analyze75.img" /> 340 <composite_data value="Analyze75.img" />
352 <composite_data value="Analyze75.t2m" /> 341 <composite_data value="Analyze75.t2m" />
353 </param> 342 </param>
354 <param name="segmentationtool" value="kmeans"/> 343 <param name="segmentationtool" value="kmeans"/>
344 <param name="kmeans_r" value="1:3"/>
345 <param name="kmeans_k" value="2,3"/>
355 <repeat name="colours"> 346 <repeat name="colours">
356 <param name="feature_color" value="#ff00ff"/> 347 <param name="feature_color" value="#ff00ff"/>
357 </repeat> 348 </repeat>
358 <repeat name="colours"> 349 <repeat name="colours">
359 <param name="feature_color" value="#0000FF"/> 350 <param name="feature_color" value="#0000FF"/>
366 <output name="pixeloutput" file="cluster_skm.tabular" compare="sim_size"/> 357 <output name="pixeloutput" file="cluster_skm.tabular" compare="sim_size"/>
367 </test> 358 </test>
368 <test> 359 <test>
369 <param name="infile" value="preprocessing_results1.RData" ftype="rdata"/> 360 <param name="infile" value="preprocessing_results1.RData" ftype="rdata"/>
370 <param name="segmentationtool" value="centroids"/> 361 <param name="segmentationtool" value="centroids"/>
362 <param name="centroids_r" ftype="text" value="1,2"/>
363 <param name="centroids_k" ftype="text" value="5"/>
364 <param name="centroids_toplabels" ftype="integer" value="100"/>
371 <repeat name="colours"> 365 <repeat name="colours">
372 <param name="feature_color" value="#0000FF"/> 366 <param name="feature_color" value="#0000FF"/>
373 </repeat> 367 </repeat>
374 <repeat name="colours"> 368 <repeat name="colours">
375 <param name="feature_color" value="#00C957"/> 369 <param name="feature_color" value="#00C957"/>