Mercurial > repos > galaxyp > mass_spectrometry_imaging_segmentations
diff segmentation_tool.xml @ 6:80b6b96a175c draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_segmentation commit 37da74ed68228b16efbdbde776e7c38cc06eb5d5
| author | galaxyp |
|---|---|
| date | Tue, 19 Jun 2018 18:08:36 -0400 |
| parents | cee9cf693709 |
| children | adfef12c7e31 |
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--- a/segmentation_tool.xml Mon Jun 11 17:34:31 2018 -0400 +++ b/segmentation_tool.xml Tue Jun 19 18:08:36 2018 -0400 @@ -1,4 +1,4 @@ -<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.1"> +<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.2"> <description>mass spectrometry imaging spatial clustering</description> <requirements> <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement> @@ -37,13 +37,15 @@ ## Read MALDI Imaging dataset #if $infile.ext == 'imzml' - msidata = readImzML('infile') + msidata <- readImzML('infile', mass.accuracy=$accuracy, units.accuracy = "$units") #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 ############## ## Number of features (m/z) @@ -105,7 +107,7 @@ } properties = c("Number of m/z features", - "Range of m/z values [Da]", + "Range of m/z values", "Number of pixels", "Range of x coordinates", "Range of y coordinates", @@ -157,7 +159,6 @@ if (npeaks > 0) { - ######################## II) segmentation tools ############################# ############################################################################# #set $color_string = ','.join(['"%s"' % $color.feature_color for $color in $colours]) @@ -186,21 +187,28 @@ component_vector = character() for (numberofcomponents in 1:$segm_cond.pca_ncomp) {component_vector[numberofcomponents]= paste0("PC", numberofcomponents)} - pca = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE, + pca_result = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE, method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1)) - print(image(pca, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector)) - print(plot(pca, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input)) + ### images in pdf file + print(image(pca_result, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector)) + for (PCs in 1:$segm_cond.pca_ncomp){ + print(image(pca_result, column = c(paste0("PC",PCs)), superpose = FALSE, col.regions = risk.colors(100)))} + ### plots in pdf file + print(plot(pca_result, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input)) + for (PCs in 1:$segm_cond.pca_ncomp){ + print(plot(pca_result, column = c(paste0("PC",PCs)), superpose = FALSE))} - pcaloadings = (pca@resultData\$ncomp\$loadings) ### loading for each m/z value - pcascores = (pca@resultData\$ncomp\$scores) ### scores for each pixel + + ### values in tabular files + pcaloadings = (pca_result@resultData\$ncomp\$loadings) ### loading for each m/z value + pcascores = (pca_result@resultData\$ncomp\$scores) ### scores for each pixel write.table(pcaloadings, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") write.table(pcascores, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") ## optional output as .RData #if $output_rdata: - ## save as (.RData) save(pca, file="$segmentation_rdata") @@ -241,12 +249,14 @@ 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") 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)) ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0)) for (iteration in 1:length(ssc@resultData)){ ssc_class = ((ssc@resultData)[[iteration]]\$classes) ssc_classes = cbind(ssc_classes, ssc_class) } - colnames(ssc_classes) = names((ssc@resultData)) + colnames(ssc_classes) = names((ssc@resultData)) ssc_toplabels = topLabels(ssc, n=$segm_cond.centroids_toplabels) @@ -273,9 +283,14 @@ ]]></configfile> </configfiles> <inputs> - <param name="infile" type="data" format="imzml, rdata, analyze75" + <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="segm_cond"> <param name="segmentationtool" type="select" label="Select the tool for spatial clustering"> <option value="pca" selected="True">pca</option> @@ -322,8 +337,8 @@ label="The sparsity thresholding parameter by which to shrink the t-statistics (s)" 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)"/> <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"> - <option value="gaussian" selected="True">gaussian</option> - <option value="adaptive">adaptive</option> + <option value="gaussian">gaussian</option> + <option value="adaptive" selected="True">adaptive</option> </param> <param name="centroids_toplabels" type="integer" value="500" label="Number of toplabels (m/z) which should be written in tabular output"/> @@ -406,7 +421,7 @@ <param name="infile" value="preprocessed.RData" ftype="rdata"/> <param name="segmentationtool" value="centroids"/> <param name="centroids_r" value="1,2"/> - <param name="centroids_k" value="5"/> + <param name="centroids_k" value="3"/> <param name="centroids_toplabels" value="50"/> <repeat name="colours"> <param name="feature_color" value="#0000FF"/> @@ -444,7 +459,7 @@ Options: - PCA: principal component analysis -- k-means: patially-aware k-means clustering +- k-means: spatially-aware k-means clustering - spatial shrunken centroids: Allows the number of segments to decrease according to the data. This allows automatic selection of the number of clusters Output: