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comparison segmentation_tool.xml @ 5:cee9cf693709 draft

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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_segmentation commit a7be47698f53eb4f00961192327d93e8989276a7
author galaxyp
date Mon, 11 Jun 2018 17:34:31 -0400
parents aec189b0c64d
children 80b6b96a175c
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4:aec189b0c64d 5:cee9cf693709
1 <tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.0"> 1 <tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.1">
2 <description>tool for spatial clustering</description> 2 <description>mass spectrometry imaging spatial clustering</description>
3 <requirements> 3 <requirements>
4 <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement> 4 <requirement type="package" version="1.10.0">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="0.20-35">r-lattice</requirement> 6 <requirement type="package" version="0.20-35">r-lattice</requirement>
7 </requirements> 7 </requirements>
26 </command> 26 </command>
27 <configfiles> 27 <configfiles>
28 <configfile name="MSI_segmentation"><![CDATA[ 28 <configfile name="MSI_segmentation"><![CDATA[
29 29
30 30
31 ################################# load libraries and read file ######################### 31 ################################# load libraries and read file #################
32
33 32
34 library(Cardinal) 33 library(Cardinal)
35 library(gridExtra) 34 library(gridExtra)
36 library(lattice) 35 library(lattice)
37 36
45 load('infile.RData') 44 load('infile.RData')
46 #end if 45 #end if
47 46
48 ###################################### file properties in numbers ############## 47 ###################################### file properties in numbers ##############
49 48
50 ## Number of features (mz) 49 ## Number of features (m/z)
51 maxfeatures = length(features(msidata)) 50 maxfeatures = length(features(msidata))
52 ## Range mz 51 ## Range m/z
53 minmz = round(min(mz(msidata)), digits=2) 52 minmz = round(min(mz(msidata)), digits=2)
54 maxmz = round(max(mz(msidata)), digits=2) 53 maxmz = round(max(mz(msidata)), digits=2)
55 ## Number of spectra (pixels) 54 ## Number of spectra (pixels)
56 pixelcount = length(pixels(msidata)) 55 pixelcount = length(pixels(msidata))
57 ## Range x coordinates 56 ## Range x coordinates
64 minint = round(min(spectra(msidata)[]), digits=2) 63 minint = round(min(spectra(msidata)[]), digits=2)
65 maxint = round(max(spectra(msidata)[]), digits=2) 64 maxint = round(max(spectra(msidata)[]), digits=2)
66 medint = round(median(spectra(msidata)[]), digits=2) 65 medint = round(median(spectra(msidata)[]), digits=2)
67 ## Number of intensities > 0 66 ## Number of intensities > 0
68 npeaks= sum(spectra(msidata)[]>0) 67 npeaks= sum(spectra(msidata)[]>0)
69 ## Spectra multiplied with mz (potential number of peaks) 68 ## Spectra multiplied with m/z (potential number of peaks)
70 numpeaks = ncol(spectra(msidata)[])*nrow(spectra(msidata)[]) 69 numpeaks = ncol(spectra(msidata)[])*nrow(spectra(msidata)[])
71 ## Percentage of intensities > 0 70 ## Percentage of intensities > 0
72 percpeaks = round(npeaks/numpeaks*100, digits=2) 71 percpeaks = round(npeaks/numpeaks*100, digits=2)
73 ## Number of empty TICs 72 ## Number of empty TICs
74 TICs = colSums(spectra(msidata)[]) 73 TICs = colSums(spectra(msidata)[])
75 NumemptyTIC = sum(TICs == 0) 74 NumemptyTIC = sum(TICs == 0)
76 75
77
78 ## Processing informations 76 ## Processing informations
79 processinginfo = processingData(msidata) 77 processinginfo = processingData(msidata)
80 centroidedinfo = processinginfo@centroided # TRUE or FALSE 78 centroidedinfo = processinginfo@centroided # TRUE or FALSE
81 79
82 ## if TRUE write processinginfo if no write FALSE 80 ## if TRUE write processinginfo if FALSE write FALSE
83 81
84 ## normalization 82 ## normalization
85 if (length(processinginfo@normalization) == 0) { 83 if (length(processinginfo@normalization) == 0) {
86 normalizationinfo='FALSE' 84 normalizationinfo='FALSE'
87 } else { 85 } else {
104 peakpickinginfo='FALSE' 102 peakpickinginfo='FALSE'
105 } else { 103 } else {
106 peakpickinginfo=processinginfo@peakPicking 104 peakpickinginfo=processinginfo@peakPicking
107 } 105 }
108 106
109 ############################################################################# 107 properties = c("Number of m/z features",
110 108 "Range of m/z values [Da]",
111 properties = c("Number of mz features",
112 "Range of mz values [Da]",
113 "Number of pixels", 109 "Number of pixels",
114 "Range of x coordinates", 110 "Range of x coordinates",
115 "Range of y coordinates", 111 "Range of y coordinates",
116 "Range of intensities", 112 "Range of intensities",
117 "Median of intensities", 113 "Median of intensities",
165 ######################## II) segmentation tools ############################# 161 ######################## II) segmentation tools #############################
166 ############################################################################# 162 #############################################################################
167 #set $color_string = ','.join(['"%s"' % $color.feature_color for $color in $colours]) 163 #set $color_string = ','.join(['"%s"' % $color.feature_color for $color in $colours])
168 colourvector = c($color_string) 164 colourvector = c($color_string)
169 165
166 ### preparation for images and plots:
167 #if str($image_cond.image_type) == "standard_image":
168 print("standard image")
169
170 strip_input = TRUE
171 lattice_input = FALSE
172
173 #elif str($image_cond.image_type) == "lattice_image":
174 print("lattice image")
175
176 strip_input = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))
177 lattice_input = TRUE
178
179 #end if
180
170 181
171 #if str( $segm_cond.segmentationtool ) == 'pca': 182 #if str( $segm_cond.segmentationtool ) == 'pca':
172 print('pca') 183 print('pca')
173 ##pca 184 ##pca
174 185
176 for (numberofcomponents in 1:$segm_cond.pca_ncomp) 187 for (numberofcomponents in 1:$segm_cond.pca_ncomp)
177 {component_vector[numberofcomponents]= paste0("PC", numberofcomponents)} 188 {component_vector[numberofcomponents]= paste0("PC", numberofcomponents)}
178 pca = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE, 189 pca = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE,
179 method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1)) 190 method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1))
180 191
181 print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col=colourvector)) 192 print(image(pca, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector))
182 print(plot(pca, main="PCA plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)))) 193 print(plot(pca, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input))
183 194
184 195 pcaloadings = (pca@resultData\$ncomp\$loadings) ### loading for each m/z value
185 pcaloadings = (pca@resultData\$ncomp\$loadings) ### loading for each mz value
186 pcascores = (pca@resultData\$ncomp\$scores) ### scores for each pixel 196 pcascores = (pca@resultData\$ncomp\$scores) ### scores for each pixel
187 197
188 write.table(pcaloadings, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 198 write.table(pcaloadings, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
189 write.table(pcascores, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 199 write.table(pcascores, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
200
201 ## optional output as .RData
202 #if $output_rdata:
203
204 ## save as (.RData)
205 save(pca, file="$segmentation_rdata")
206
207 #end if
190 208
191 #elif str( $segm_cond.segmentationtool ) == 'kmeans': 209 #elif str( $segm_cond.segmentationtool ) == 'kmeans':
192 print('kmeans') 210 print('kmeans')
193 ##k-means 211 ##k-means
194 212
195 skm = spatialKMeans(msidata, r=c($segm_cond.kmeans_r), k=c($segm_cond.kmeans_k), method="$segm_cond.kmeans_method") 213 skm = spatialKMeans(msidata, r=c($segm_cond.kmeans_r), k=c($segm_cond.kmeans_k), method="$segm_cond.kmeans_method")
196 print(image(skm, key=TRUE, main="K-means clustering", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col= colourvector, layout=c(1,1))) 214 print(image(skm, key=TRUE, main="K-means clustering", lattice=lattice_input, strip=strip_input, col= colourvector, layout=c(1,1)))
197 215
198 print(plot(skm, main="K-means plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), layout=c($segm_cond.kmeans_layout))) 216 print(plot(skm, main="K-means plot", lattice=lattice_input, col= colourvector, strip=strip_input, layout=c($segm_cond.kmeans_layout)))
199
200 217
201 skm_clusters = data.frame(matrix(NA, nrow = pixelcount, ncol = 0)) 218 skm_clusters = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
202 for (iteration in 1:length(skm@resultData)){ 219 for (iteration in 1:length(skm@resultData)){
203 skm_cluster = ((skm@resultData)[[iteration]]\$cluster) 220 skm_cluster = ((skm@resultData)[[iteration]]\$cluster)
204 skm_clusters = cbind(skm_clusters, skm_cluster) } 221 skm_clusters = cbind(skm_clusters, skm_cluster) }
207 skm_toplabels = topLabels(skm, n=$segm_cond.kmeans_toplabels) 224 skm_toplabels = topLabels(skm, n=$segm_cond.kmeans_toplabels)
208 225
209 write.table(skm_toplabels, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 226 write.table(skm_toplabels, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
210 write.table(skm_clusters, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 227 write.table(skm_clusters, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
211 228
229 ## optional output as .RData
230 #if $output_rdata:
231
232 ## save as (.RData)
233 save(skm, file="$segmentation_rdata")
234
235 #end if
212 236
213 #elif str( $segm_cond.segmentationtool ) == 'centroids': 237 #elif str( $segm_cond.segmentationtool ) == 'centroids':
214 print('centroids') 238 print('centroids')
215 ##centroids 239 ##centroids
216 240
217 ssc = spatialShrunkenCentroids(msidata, r=c($segm_cond.centroids_r), k=c($segm_cond.centroids_k), s=c($segm_cond.centroids_s), method="$segm_cond.centroids_method") 241 ssc = spatialShrunkenCentroids(msidata, r=c($segm_cond.centroids_r), k=c($segm_cond.centroids_k), s=c($segm_cond.centroids_s), method="$segm_cond.centroids_method")
218 print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col= colourvector,layout=c(1,1))) 242 print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=lattice_input, strip = strip_input, col= colourvector,layout=c(1,1)))
219 print(plot(ssc, main="Spatial shrunken centroids plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)),layout=c($segm_cond.centroids_layout))) 243 print(plot(ssc, main="Spatial shrunken centroids plot", lattice=lattice_input, col= colourvector, strip = strip_input,layout=c($segm_cond.centroids_layout)))
220 244
221 ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0)) 245 ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
222 for (iteration in 1:length(ssc@resultData)){ 246 for (iteration in 1:length(ssc@resultData)){
223 ssc_class = ((ssc@resultData)[[iteration]]\$classes) 247 ssc_class = ((ssc@resultData)[[iteration]]\$classes)
224 ssc_classes = cbind(ssc_classes, ssc_class) } 248 ssc_classes = cbind(ssc_classes, ssc_class) }
227 ssc_toplabels = topLabels(ssc, n=$segm_cond.centroids_toplabels) 251 ssc_toplabels = topLabels(ssc, n=$segm_cond.centroids_toplabels)
228 252
229 write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 253 write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
230 write.table(ssc_classes, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 254 write.table(ssc_classes, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
231 255
256 ## optional output as .RData
257 #if $output_rdata:
258
259 ## save as (.RData)
260 save(ssc, file="$segmentation_rdata")
261
262 #end if
232 263
233 #end if 264 #end if
234 265
235 dev.off() 266 dev.off()
236 267
275 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"> 306 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">
276 <option value="gaussian">gaussian</option> 307 <option value="gaussian">gaussian</option>
277 <option value="adaptive" selected="True">adaptive</option> 308 <option value="adaptive" selected="True">adaptive</option>
278 </param> 309 </param>
279 <param name="kmeans_toplabels" type="integer" value="500" 310 <param name="kmeans_toplabels" type="integer" value="500"
280 label="Number of toplabels (masses) which should be written in tabular output"/> 311 label="Number of toplabels (m/z) which should be written in tabular output"/>
281 <param name="kmeans_layout" type="text" value="1,1" 312 <param name="kmeans_layout" type="text" value="1,1"
282 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"/> 313 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"/>
283 </when> 314 </when>
284 315
285 <when value="centroids"> 316 <when value="centroids">
287 label="The spatial neighborhood radius of nearby pixels to consider (r)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/> 318 label="The spatial neighborhood radius of nearby pixels to consider (r)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>
288 <param name="centroids_k" type="text" value="5" 319 <param name="centroids_k" type="text" value="5"
289 label="The initial number of clusters (k)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/> 320 label="The initial number of clusters (k)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>
290 <param name="centroids_s" type="text" value="2" 321 <param name="centroids_s" type="text" value="2"
291 label="The sparsity thresholding parameter by which to shrink the t-statistics (s)" 322 label="The sparsity thresholding parameter by which to shrink the t-statistics (s)"
292 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)"/> 323 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)"/>
293 <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"> 324 <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">
294 <option value="gaussian" selected="True">gaussian</option> 325 <option value="gaussian" selected="True">gaussian</option>
295 <option value="adaptive">adaptive</option> 326 <option value="adaptive">adaptive</option>
296 </param> 327 </param>
297 <param name="centroids_toplabels" type="integer" value="500" 328 <param name="centroids_toplabels" type="integer" value="500"
298 label="Number of toplabels (masses) which should be written in tabular output"/> 329 label="Number of toplabels (m/z) which should be written in tabular output"/>
299 <param name="centroids_layout" type="text" value="1,1" 330 <param name="centroids_layout" type="text" value="1,1"
300 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"/> 331 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"/>
301 </when> 332 </when>
333 </conditional>
334 <conditional name="image_cond">
335 <param name="image_type" type="select" label="Select the image type">
336 <option value="standard_image" selected="True">standard</option>
337 <option value="lattice_image">lattice</option>
338 </param>
339 <when value="standard_image"/>
340 <when value="lattice_image"/>
302 </conditional> 341 </conditional>
303 <repeat name="colours" title="Colours for the plots" min="1" max="50"> 342 <repeat name="colours" title="Colours for the plots" min="1" max="50">
304 <param name="feature_color" type="color" label="Colours" value="#ff00ff" help="Numbers of columns should be the same as number of components"> 343 <param name="feature_color" type="color" label="Colours" value="#ff00ff" help="Numbers of columns should be the same as number of components">
305 <sanitizer> 344 <sanitizer>
306 <valid initial="string.letters,string.digits"> 345 <valid initial="string.letters,string.digits">
307 <add value="#" /> 346 <add value="#" />
308 </valid> 347 </valid>
309 </sanitizer> 348 </sanitizer>
310 </param> 349 </param>
311 </repeat> 350 </repeat>
351 <param name="output_rdata" type="boolean" display="radio" label="Results as .RData output"/>
312 </inputs> 352 </inputs>
313 <outputs> 353 <outputs>
314 <data format="pdf" name="segmentationimages" from_work_dir="segmentationpdf.pdf" label = "${tool.name} ${on_string}"/> 354 <data format="pdf" name="segmentationimages" from_work_dir="segmentationpdf.pdf" label = "$infile.display_name segmentation"/>
315 <data format="tabular" name="mzfeatures" label="Mz features ${on_string}"/> 355 <data format="tabular" name="mzfeatures" label="$infile.display_name m/z features"/>
316 <data format="tabular" name="pixeloutput" label="Pixels ${on_string}"/> 356 <data format="tabular" name="pixeloutput" label="$infile.display_name pixels"/>
357 <data format="rdata" name="segmentation_rdata" label="$infile.display_name segmentation">
358 <filter>output_rdata</filter>
359 </data>
317 </outputs> 360 </outputs>
318 <tests> 361 <tests>
319 <test> 362 <test>
320 <param name="infile" value="" ftype="imzml"> 363 <param name="infile" value="" ftype="imzml">
321 <composite_data value="Example_Continuous.imzML"/> 364 <composite_data value="Example_Continuous.imzML"/>
322 <composite_data value="Example_Continuous.ibd"/> 365 <composite_data value="Example_Continuous.ibd"/>
323 </param> 366 </param>
324 <param name="segmentationtool" value="pca"/> 367 <param name="segmentationtool" value="pca"/>
368 <param name="image_type" value="lattice_image"/>
325 <repeat name="colours"> 369 <repeat name="colours">
326 <param name="feature_color" value="#ff00ff"/> 370 <param name="feature_color" value="#ff00ff"/>
327 </repeat> 371 </repeat>
328 <repeat name="colours"> 372 <repeat name="colours">
329 <param name="feature_color" value="#0000FF"/> 373 <param name="feature_color" value="#0000FF"/>
349 <param name="feature_color" value="#0000FF"/> 393 <param name="feature_color" value="#0000FF"/>
350 </repeat> 394 </repeat>
351 <repeat name="colours"> 395 <repeat name="colours">
352 <param name="feature_color" value="#00C957"/> 396 <param name="feature_color" value="#00C957"/>
353 </repeat> 397 </repeat>
398 <param name="output_rdata" value="True"/>
354 <output name="segmentationimages" file="kmeans_analyze.pdf" compare="sim_size" delta="20000"/> 399 <output name="segmentationimages" file="kmeans_analyze.pdf" compare="sim_size" delta="20000"/>
355 <output name="mzfeatures" file="toplabels_skm.tabular" compare="sim_size"/> 400 <output name="mzfeatures" file="toplabels_skm.tabular" compare="sim_size"/>
356 <output name="pixeloutput" file="cluster_skm.tabular" compare="sim_size"/> 401 <output name="pixeloutput" file="cluster_skm.tabular" compare="sim_size"/>
402 <output name="pixeloutput" file="cluster_skm.tabular" compare="sim_size"/>
403 <output name="segmentation_rdata" file="cluster_skm.RData" compare="sim_size"/>
357 </test> 404 </test>
358 <test> 405 <test>
359 <param name="infile" value="preprocessed.RData" ftype="rdata"/> 406 <param name="infile" value="preprocessed.RData" ftype="rdata"/>
360 <param name="segmentationtool" value="centroids"/> 407 <param name="segmentationtool" value="centroids"/>
361 <param name="centroids_r" value="1,2"/> 408 <param name="centroids_r" value="1,2"/>
384 <help> 431 <help>
385 <![CDATA[ 432 <![CDATA[
386 433
387 Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_ 434 Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_
388 435
389 This tool provides three different Cardinal functions for unsupervised clustering/spatial segmentation of mass-spectrometry imaging data. 436 This tool provides three different Cardinal functions for unsupervised clustering/spatial segmentation of mass spectrometry imaging data.
390 437
391 Input data: 3 types of input data can be used: 438 Input data: 3 types of input data can be used:
392 439
393 - imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_ 440 - imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_
394 - Analyze7.5 (upload hdr, img and t2m file via the "composite" function) 441 - Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
401 - spatial shrunken centroids: Allows the number of segments to decrease according to the data. This allows automatic selection of the number of clusters 448 - spatial shrunken centroids: Allows the number of segments to decrease according to the data. This allows automatic selection of the number of clusters
402 449
403 Output: 450 Output:
404 451
405 - Pdf with the heatmaps and plots for the segmentation 452 - Pdf with the heatmaps and plots for the segmentation
406 - Tabular file with information on masses and pixels: loadings/scores (PCA), toplabels/clusters (k-means), toplabels/classes (spatial shrunken centroids) 453 - Tabular file with information on m/z and pixels: loadings/scores (PCA), toplabels/clusters (k-means), toplabels/classes (spatial shrunken centroids)
454 - Optional .RData file which contains the segmentation results and can be used for further exploration in R
407 455
408 ]]> 456 ]]>
409 </help> 457 </help>
410 <citations> 458 <citations>
411 <citation type="doi">10.1093/bioinformatics/btv146</citation> 459 <citation type="doi">10.1093/bioinformatics/btv146</citation>