Mercurial > repos > galaxyp > mass_spectrometry_imaging_segmentations
diff segmentation_tool.xml @ 7:791aedb1b8ea draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_segmentation commit 8087490eb4dcaf4ead0f03eae4126780d21e5503
| author | galaxyp |
|---|---|
| date | Fri, 06 Jul 2018 14:12:15 -0400 |
| parents | b3c8c831e51a |
| children | fa8b6f0f68d4 |
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--- a/segmentation_tool.xml Tue Jun 19 18:06:24 2018 -0400 +++ b/segmentation_tool.xml Fri Jul 06 14:12:15 2018 -0400 @@ -1,4 +1,4 @@ -<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.2"> +<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.3"> <description>mass spectrometry imaging spatial clustering</description> <requirements> <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement> @@ -37,13 +37,18 @@ ## Read MALDI Imaging dataset #if $infile.ext == 'imzml' - msidata <- readImzML('infile', mass.accuracy=$accuracy, units.accuracy = "$units") + #if str($processed_cond.processed_file) == "processed": + msidata <- readImzML('infile', mass.accuracy=$processed_cond.accuracy, units.accuracy = "$processed_cond.units") + #else + msidata <- readImzML('infile') + #end if #elif $infile.ext == 'analyze75' msidata = readAnalyze('infile') #else load('infile.RData') #end if + ## create full matrix to make processed imzML files compatible with segmentation iData(msidata) <- iData(msidata)[] ###################################### file properties in numbers ############## @@ -62,19 +67,21 @@ minimumy = min(coord(msidata)[,2]) maximumy = max(coord(msidata)[,2]) ## Range of intensities -minint = round(min(spectra(msidata)[]), digits=2) -maxint = round(max(spectra(msidata)[]), digits=2) +minint = round(min(spectra(msidata)[],na.rm=TRUE), digits=2) +maxint = round(max(spectra(msidata)[],na.rm=TRUE), digits=2) medint = round(median(spectra(msidata)[]), digits=2) ## Number of intensities > 0 -npeaks= sum(spectra(msidata)[]>0) +npeaks= sum(spectra(msidata)[]>0, na.rm=TRUE) ## Spectra multiplied with m/z (potential number of peaks) numpeaks = ncol(spectra(msidata)[])*nrow(spectra(msidata)[]) ## Percentage of intensities > 0 percpeaks = round(npeaks/numpeaks*100, digits=2) ## Number of empty TICs -TICs = colSums(spectra(msidata)[]) +TICs = colSums(spectra(msidata)[], na.rm=TRUE) NumemptyTIC = sum(TICs == 0) + + ## Processing informations processinginfo = processingData(msidata) centroidedinfo = processinginfo@centroided # TRUE or FALSE @@ -193,13 +200,12 @@ ### images in pdf file print(image(pca_result, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector)) for (PCs in 1:$segm_cond.pca_ncomp){ - print(image(pca_result, column = c(paste0("PC",PCs)), superpose = FALSE, col.regions = risk.colors(100)))} + print(image(pca_result, column = c(paste0("PC",PCs)), lattice=lattice_input, superpose = FALSE, col.regions = risk.colors(100)))} ### plots in pdf file print(plot(pca_result, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input)) for (PCs in 1:$segm_cond.pca_ncomp){ - print(plot(pca_result, column = c(paste0("PC",PCs)), superpose = FALSE))} + print(plot(pca_result, column = c(paste0("PC",PCs)),superpose = FALSE))} - ### values in tabular files pcaloadings = (pca_result@resultData\$ncomp\$loadings) ### loading for each m/z value pcascores = (pca_result@resultData\$ncomp\$scores) ### scores for each pixel @@ -250,7 +256,7 @@ print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=lattice_input, strip = strip_input, col= colourvector,layout=c(1,1))) print(plot(ssc, main="Spatial shrunken centroids plot", lattice=lattice_input, col= colourvector, strip = strip_input,layout=c($segm_cond.centroids_layout))) print(plot(ssc, mode = "tstatistics",key = TRUE, lattice=lattice_input, layout = c($segm_cond.centroids_layout), main="t-statistics", col=colourvector)) - print(plot(summary(ssc), main = "Number of segments",lattice=lattice_input)) + plot(summary(ssc), main = "Number of segments") ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0)) for (iteration in 1:length(ssc@resultData)){ @@ -286,11 +292,20 @@ <param name="infile" type="data" format="imzml,rdata,analyze75" label="Inputfile as imzML, Analyze7.5 or Cardinal MSImageSet saved as RData" help="Upload composite datatype imzml (ibd+imzML) or analyze75 (hdr+img+t2m) or regular upload .RData (Cardinal MSImageSet)"/> - <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"/> - <param name="units" display="radio" type="select" label="Only for processed imzML files: unit of the mass accuracy" help="either m/z or ppm"> - <option value="mz" >mz</option> - <option value="ppm" selected="True" >ppm</option> - </param> + <conditional name="processed_cond"> + <param name="processed_file" type="select" label="Is the input file a processed imzML file "> + <option value="no_processed" selected="True">not a processed imzML</option> + <option value="processed">processed imzML</option> + </param> + <when value="no_processed"/> + <when value="processed"> + <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"/> + <param name="units" display="radio" type="select" label="Unit of the mass accuracy" help="either m/z or ppm"> + <option value="mz" >mz</option> + <option value="ppm" selected="True" >ppm</option> + </param> + </when> + </conditional> <conditional name="segm_cond"> <param name="segmentationtool" type="select" label="Select the tool for spatial clustering"> <option value="pca" selected="True">pca</option>