Mercurial > repos > galaxyp > mass_spectrometry_imaging_segmentations
diff segmentation_tool.xml @ 1:d4158c9955ea draft
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/msi_segmentation commit edbf2a6cb50fb04d0db56a7557a64e3bb7a0806a
| author | galaxyp |
|---|---|
| date | Thu, 01 Mar 2018 08:26:19 -0500 |
| parents | 0c1a9b68f436 |
| children | f66c5789deac |
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--- a/segmentation_tool.xml Sat Feb 24 13:51:32 2018 -0500 +++ b/segmentation_tool.xml Thu Mar 01 08:26:19 2018 -0500 @@ -1,4 +1,4 @@ -<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.7.0"> +<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.7.0.1"> <description>tool for spatial clustering</description> <requirements> <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement> @@ -40,9 +40,9 @@ ## Read MALDI Imaging dataset #if $infile.ext == 'imzml' - msidata <- readMSIData('infile.imzML') + msidata = readMSIData('infile.imzML') #elif $infile.ext == 'analyze75' - msidata <- readMSIData('infile.hdr') + msidata = readMSIData('infile.hdr') #else load('infile.RData') #end if @@ -177,10 +177,10 @@ component_vector = character() for (numberofcomponents in 1:$segm_cond.pca_ncomp) {component_vector[numberofcomponents]= paste0("PC", numberofcomponents)} - pca <- PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE, + pca = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE, method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1)) - print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.pca_imagecontrast", smooth.image = "$segm_cond.pca_imagesmoothing", col=colourvector)) + print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.pca_imagecontrast", smooth.image = "$segm_cond.pca_imagesmoothing", col=colourvector, ylim=c(maximumy+2, 0))) print(plot(pca, main="PCA plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)))) @@ -194,8 +194,8 @@ print('kmeans') ##k-means - skm <- spatialKMeans(msidata, r=$segm_cond.kmeans_r, k=$segm_cond.kmeans_k, method="$segm_cond.kmeans_method") - 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")) + skm = spatialKMeans(msidata, r=$segm_cond.kmeans_r, k=$segm_cond.kmeans_k, method="$segm_cond.kmeans_method") + print(image(skm, key=TRUE, main="K-means clustering", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.kmeans_imagecontrast", col= colourvector, smooth.image = "$segm_cond.kmeans_imagesmoothing", ylim=c(maximumy+2, 0))) print(plot(skm, main="K-means plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)))) @@ -210,8 +210,8 @@ print('centroids') ##centroids - ssc <- spatialShrunkenCentroids(msidata, r=$segm_cond.centroids_r, k=$segm_cond.centroids_k, s=$segm_cond.centroids_s, method="$segm_cond.centroids_method") - print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.centroids_imagecontrast", col= colourvector, smooth.image = "$segm_cond.centroids_imagesmoothing")) + ssc = spatialShrunkenCentroids(msidata, r=$segm_cond.centroids_r, k=$segm_cond.centroids_k, s=$segm_cond.centroids_s, method="$segm_cond.centroids_method") + print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.centroids_imagecontrast", col= colourvector, smooth.image = "$segm_cond.centroids_imagesmoothing", ylim=c(maximumy+2, 0))) 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)))) ssc_classes = (ssc@resultData\$r\$classes) @@ -342,8 +342,8 @@ <param name="feature_color" value="#0000FF"/> </repeat> <output name="segmentationimages" file="pca_imzml.pdf" compare="sim_size" delta="20000"/> - <output name="mzfeatures" file="pcaloadings_results1.txt" compare="sim_size"/> - <output name="pixeloutput" file="pcascores_results1.txt" compare="sim_size"/> + <output name="mzfeatures" file="loadings_pca.tabular" compare="sim_size"/> + <output name="pixeloutput" file="scores_pca.tabular" compare="sim_size"/> </test> <test> <param name="infile" value="" ftype="analyze75"> @@ -362,8 +362,8 @@ <param name="feature_color" value="#00C957"/> </repeat> <output name="segmentationimages" file="kmeans_imzml.pdf" compare="sim_size" delta="20000"/> - <output name="mzfeatures" file="toplabels_results1.txt" compare="sim_size"/> - <output name="pixeloutput" file="cluster_results1.txt" compare="sim_size"/> + <output name="mzfeatures" file="toplabels_skm.tabular" compare="sim_size"/> + <output name="pixeloutput" file="cluster_skm.tabular" compare="sim_size"/> </test> <test> <param name="infile" value="preprocessing_results1.RData" ftype="rdata"/> @@ -384,8 +384,8 @@ <param name="feature_color" value="#848484"/> </repeat> <output name="segmentationimages" file="centroids_imzml.pdf" compare="sim_size" delta="20000"/> - <output name="mzfeatures" file="toplabels_results1.txt" compare="sim_size"/> - <output name="pixeloutput" file="classes_results1.txt" compare="sim_size"/> + <output name="mzfeatures" file="toplabels_ssc.tabular" compare="sim_size"/> + <output name="pixeloutput" file="classes_ssc.tabular" compare="sim_size"/> </test> </tests> <help>