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comparison segmentation_tool.xml @ 7:adfef12c7e31 draft

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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_segmentation commit 8087490eb4dcaf4ead0f03eae4126780d21e5503
author galaxyp
date Fri, 06 Jul 2018 14:14:27 -0400
parents 80b6b96a175c
children 4a62874c21a3
comparison
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6:80b6b96a175c 7:adfef12c7e31
1 <tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.2"> 1 <tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.3">
2 <description>mass spectrometry imaging 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>
35 library(lattice) 35 library(lattice)
36 36
37 ## Read MALDI Imaging dataset 37 ## Read MALDI Imaging dataset
38 38
39 #if $infile.ext == 'imzml' 39 #if $infile.ext == 'imzml'
40 msidata <- readImzML('infile', mass.accuracy=$accuracy, units.accuracy = "$units") 40 #if str($processed_cond.processed_file) == "processed":
41 msidata <- readImzML('infile', mass.accuracy=$processed_cond.accuracy, units.accuracy = "$processed_cond.units")
42 #else
43 msidata <- readImzML('infile')
44 #end if
41 #elif $infile.ext == 'analyze75' 45 #elif $infile.ext == 'analyze75'
42 msidata = readAnalyze('infile') 46 msidata = readAnalyze('infile')
43 #else 47 #else
44 load('infile.RData') 48 load('infile.RData')
45 #end if 49 #end if
50
46 51
47 ## create full matrix to make processed imzML files compatible with segmentation 52 ## create full matrix to make processed imzML files compatible with segmentation
48 iData(msidata) <- iData(msidata)[] 53 iData(msidata) <- iData(msidata)[]
49 ###################################### file properties in numbers ############## 54 ###################################### file properties in numbers ##############
50 55
60 maximumx = max(coord(msidata)[,1]) 65 maximumx = max(coord(msidata)[,1])
61 ## Range y coordinates 66 ## Range y coordinates
62 minimumy = min(coord(msidata)[,2]) 67 minimumy = min(coord(msidata)[,2])
63 maximumy = max(coord(msidata)[,2]) 68 maximumy = max(coord(msidata)[,2])
64 ## Range of intensities 69 ## Range of intensities
65 minint = round(min(spectra(msidata)[]), digits=2) 70 minint = round(min(spectra(msidata)[],na.rm=TRUE), digits=2)
66 maxint = round(max(spectra(msidata)[]), digits=2) 71 maxint = round(max(spectra(msidata)[],na.rm=TRUE), digits=2)
67 medint = round(median(spectra(msidata)[]), digits=2) 72 medint = round(median(spectra(msidata)[]), digits=2)
68 ## Number of intensities > 0 73 ## Number of intensities > 0
69 npeaks= sum(spectra(msidata)[]>0) 74 npeaks= sum(spectra(msidata)[]>0, na.rm=TRUE)
70 ## Spectra multiplied with m/z (potential number of peaks) 75 ## Spectra multiplied with m/z (potential number of peaks)
71 numpeaks = ncol(spectra(msidata)[])*nrow(spectra(msidata)[]) 76 numpeaks = ncol(spectra(msidata)[])*nrow(spectra(msidata)[])
72 ## Percentage of intensities > 0 77 ## Percentage of intensities > 0
73 percpeaks = round(npeaks/numpeaks*100, digits=2) 78 percpeaks = round(npeaks/numpeaks*100, digits=2)
74 ## Number of empty TICs 79 ## Number of empty TICs
75 TICs = colSums(spectra(msidata)[]) 80 TICs = colSums(spectra(msidata)[], na.rm=TRUE)
76 NumemptyTIC = sum(TICs == 0) 81 NumemptyTIC = sum(TICs == 0)
82
83
77 84
78 ## Processing informations 85 ## Processing informations
79 processinginfo = processingData(msidata) 86 processinginfo = processingData(msidata)
80 centroidedinfo = processinginfo@centroided # TRUE or FALSE 87 centroidedinfo = processinginfo@centroided # TRUE or FALSE
81 88
191 method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1)) 198 method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1))
192 199
193 ### images in pdf file 200 ### images in pdf file
194 print(image(pca_result, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector)) 201 print(image(pca_result, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector))
195 for (PCs in 1:$segm_cond.pca_ncomp){ 202 for (PCs in 1:$segm_cond.pca_ncomp){
196 print(image(pca_result, column = c(paste0("PC",PCs)), superpose = FALSE, col.regions = risk.colors(100)))} 203 print(image(pca_result, column = c(paste0("PC",PCs)), lattice=lattice_input, superpose = FALSE, col.regions = risk.colors(100)))}
197 ### plots in pdf file 204 ### plots in pdf file
198 print(plot(pca_result, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input)) 205 print(plot(pca_result, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input))
199 for (PCs in 1:$segm_cond.pca_ncomp){ 206 for (PCs in 1:$segm_cond.pca_ncomp){
200 print(plot(pca_result, column = c(paste0("PC",PCs)), superpose = FALSE))} 207 print(plot(pca_result, column = c(paste0("PC",PCs)),superpose = FALSE))}
201 208
202
203 ### values in tabular files 209 ### values in tabular files
204 pcaloadings = (pca_result@resultData\$ncomp\$loadings) ### loading for each m/z value 210 pcaloadings = (pca_result@resultData\$ncomp\$loadings) ### loading for each m/z value
205 pcascores = (pca_result@resultData\$ncomp\$scores) ### scores for each pixel 211 pcascores = (pca_result@resultData\$ncomp\$scores) ### scores for each pixel
206 212
207 write.table(pcaloadings, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 213 write.table(pcaloadings, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
248 254
249 ssc = spatialShrunkenCentroids(msidata, r=c($segm_cond.centroids_r), k=c($segm_cond.centroids_k), s=c($segm_cond.centroids_s), method="$segm_cond.centroids_method") 255 ssc = spatialShrunkenCentroids(msidata, r=c($segm_cond.centroids_r), k=c($segm_cond.centroids_k), s=c($segm_cond.centroids_s), method="$segm_cond.centroids_method")
250 print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=lattice_input, strip = strip_input, col= colourvector,layout=c(1,1))) 256 print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=lattice_input, strip = strip_input, col= colourvector,layout=c(1,1)))
251 print(plot(ssc, main="Spatial shrunken centroids plot", lattice=lattice_input, col= colourvector, strip = strip_input,layout=c($segm_cond.centroids_layout))) 257 print(plot(ssc, main="Spatial shrunken centroids plot", lattice=lattice_input, col= colourvector, strip = strip_input,layout=c($segm_cond.centroids_layout)))
252 print(plot(ssc, mode = "tstatistics",key = TRUE, lattice=lattice_input, layout = c($segm_cond.centroids_layout), main="t-statistics", col=colourvector)) 258 print(plot(ssc, mode = "tstatistics",key = TRUE, lattice=lattice_input, layout = c($segm_cond.centroids_layout), main="t-statistics", col=colourvector))
253 print(plot(summary(ssc), main = "Number of segments",lattice=lattice_input)) 259 plot(summary(ssc), main = "Number of segments")
254 260
255 ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0)) 261 ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
256 for (iteration in 1:length(ssc@resultData)){ 262 for (iteration in 1:length(ssc@resultData)){
257 ssc_class = ((ssc@resultData)[[iteration]]\$classes) 263 ssc_class = ((ssc@resultData)[[iteration]]\$classes)
258 ssc_classes = cbind(ssc_classes, ssc_class) } 264 ssc_classes = cbind(ssc_classes, ssc_class) }
284 </configfiles> 290 </configfiles>
285 <inputs> 291 <inputs>
286 <param name="infile" type="data" format="imzml,rdata,analyze75" 292 <param name="infile" type="data" format="imzml,rdata,analyze75"
287 label="Inputfile as imzML, Analyze7.5 or Cardinal MSImageSet saved as RData" 293 label="Inputfile as imzML, Analyze7.5 or Cardinal MSImageSet saved as RData"
288 help="Upload composite datatype imzml (ibd+imzML) or analyze75 (hdr+img+t2m) or regular upload .RData (Cardinal MSImageSet)"/> 294 help="Upload composite datatype imzml (ibd+imzML) or analyze75 (hdr+img+t2m) or regular upload .RData (Cardinal MSImageSet)"/>
289 <param name="accuracy" type="float" value="50" label="Only for processed imzML files: enter mass accuracy to which the m/z values will be binned" help="This should be set to the native accuracy of the mass spectrometer, if known"/> 295 <conditional name="processed_cond">
290 <param name="units" display="radio" type="select" label="Only for processed imzML files: unit of the mass accuracy" help="either m/z or ppm"> 296 <param name="processed_file" type="select" label="Is the input file a processed imzML file ">
291 <option value="mz" >mz</option> 297 <option value="no_processed" selected="True">not a processed imzML</option>
292 <option value="ppm" selected="True" >ppm</option> 298 <option value="processed">processed imzML</option>
293 </param> 299 </param>
300 <when value="no_processed"/>
301 <when value="processed">
302 <param name="accuracy" type="float" value="50" label="Mass accuracy to which the m/z values will be binned" help="This should be set to the native accuracy of the mass spectrometer, if known"/>
303 <param name="units" display="radio" type="select" label="Unit of the mass accuracy" help="either m/z or ppm">
304 <option value="mz" >mz</option>
305 <option value="ppm" selected="True" >ppm</option>
306 </param>
307 </when>
308 </conditional>
294 <conditional name="segm_cond"> 309 <conditional name="segm_cond">
295 <param name="segmentationtool" type="select" label="Select the tool for spatial clustering"> 310 <param name="segmentationtool" type="select" label="Select the tool for spatial clustering">
296 <option value="pca" selected="True">pca</option> 311 <option value="pca" selected="True">pca</option>
297 <option value="kmeans">k-means</option> 312 <option value="kmeans">k-means</option>
298 <option value="centroids">spatial shrunken centroids</option> 313 <option value="centroids">spatial shrunken centroids</option>