Mercurial > repos > galaxyp > mass_spectrometry_imaging_segmentations
diff segmentation_tool.xml @ 5:25e1e84cfe1b draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_segmentation commit a7be47698f53eb4f00961192327d93e8989276a7
| author | galaxyp |
|---|---|
| date | Mon, 11 Jun 2018 17:32:53 -0400 |
| parents | 38afc994e3cc |
| children | b3c8c831e51a |
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--- a/segmentation_tool.xml Mon May 28 12:35:11 2018 -0400 +++ b/segmentation_tool.xml Mon Jun 11 17:32:53 2018 -0400 @@ -1,5 +1,5 @@ -<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.0"> - <description>tool for spatial clustering</description> +<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.1"> + <description>mass spectrometry imaging spatial clustering</description> <requirements> <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement> <requirement type="package" version="2.2.1">r-gridextra</requirement> @@ -28,8 +28,7 @@ <configfile name="MSI_segmentation"><![CDATA[ -################################# load libraries and read file ######################### - +################################# load libraries and read file ################# library(Cardinal) library(gridExtra) @@ -47,9 +46,9 @@ ###################################### file properties in numbers ############## -## Number of features (mz) +## Number of features (m/z) maxfeatures = length(features(msidata)) -## Range mz +## Range m/z minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) ## Number of spectra (pixels) @@ -66,7 +65,7 @@ medint = round(median(spectra(msidata)[]), digits=2) ## Number of intensities > 0 npeaks= sum(spectra(msidata)[]>0) -## Spectra multiplied with mz (potential number of peaks) +## 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) @@ -74,12 +73,11 @@ TICs = colSums(spectra(msidata)[]) NumemptyTIC = sum(TICs == 0) - ## Processing informations processinginfo = processingData(msidata) centroidedinfo = processinginfo@centroided # TRUE or FALSE -## if TRUE write processinginfo if no write FALSE +## if TRUE write processinginfo if FALSE write FALSE ## normalization if (length(processinginfo@normalization) == 0) { @@ -106,10 +104,8 @@ peakpickinginfo=processinginfo@peakPicking } -############################################################################# - -properties = c("Number of mz features", - "Range of mz values [Da]", +properties = c("Number of m/z features", + "Range of m/z values [Da]", "Number of pixels", "Range of x coordinates", "Range of y coordinates", @@ -167,6 +163,21 @@ #set $color_string = ','.join(['"%s"' % $color.feature_color for $color in $colours]) colourvector = c($color_string) + ### preparation for images and plots: + #if str($image_cond.image_type) == "standard_image": + print("standard image") + + strip_input = TRUE + lattice_input = FALSE + + #elif str($image_cond.image_type) == "lattice_image": + print("lattice image") + + strip_input = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)) + lattice_input = TRUE + + #end if + #if str( $segm_cond.segmentationtool ) == 'pca': print('pca') @@ -178,25 +189,31 @@ pca = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE, method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1)) - print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col=colourvector)) - print(plot(pca, main="PCA plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)))) + print(image(pca, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector)) + print(plot(pca, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input)) - - pcaloadings = (pca@resultData\$ncomp\$loadings) ### loading for each mz value + pcaloadings = (pca@resultData\$ncomp\$loadings) ### loading for each m/z value pcascores = (pca@resultData\$ncomp\$scores) ### scores for each pixel write.table(pcaloadings, file="$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") + + #end if + #elif str( $segm_cond.segmentationtool ) == 'kmeans': print('kmeans') ##k-means skm = spatialKMeans(msidata, r=c($segm_cond.kmeans_r), k=c($segm_cond.kmeans_k), method="$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)), col= colourvector, layout=c(1,1))) + print(image(skm, key=TRUE, main="K-means clustering", lattice=lattice_input, strip=strip_input, col= colourvector, layout=c(1,1))) - 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($segm_cond.kmeans_layout))) - + print(plot(skm, main="K-means plot", lattice=lattice_input, col= colourvector, strip=strip_input, layout=c($segm_cond.kmeans_layout))) skm_clusters = data.frame(matrix(NA, nrow = pixelcount, ncol = 0)) for (iteration in 1:length(skm@resultData)){ @@ -209,14 +226,21 @@ write.table(skm_toplabels, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") write.table(skm_clusters, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") + ## optional output as .RData + #if $output_rdata: + + ## save as (.RData) + save(skm, file="$segmentation_rdata") + + #end if #elif str( $segm_cond.segmentationtool ) == 'centroids': print('centroids') ##centroids ssc = spatialShrunkenCentroids(msidata, r=c($segm_cond.centroids_r), k=c($segm_cond.centroids_k), s=c($segm_cond.centroids_s), method="$segm_cond.centroids_method") - print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col= colourvector,layout=c(1,1))) - print(plot(ssc, main="Spatial shrunken centroids plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)),layout=c($segm_cond.centroids_layout))) + 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($segm_cond.centroids_layout))) ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0)) for (iteration in 1:length(ssc@resultData)){ @@ -229,6 +253,13 @@ write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") write.table(ssc_classes, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") + ## optional output as .RData + #if $output_rdata: + + ## save as (.RData) + save(ssc, file="$segmentation_rdata") + + #end if #end if @@ -277,7 +308,7 @@ <option value="adaptive" selected="True">adaptive</option> </param> <param name="kmeans_toplabels" type="integer" value="500" - label="Number of toplabels (masses) which should be written in tabular output"/> + label="Number of toplabels (m/z) which should be written in tabular output"/> <param name="kmeans_layout" type="text" value="1,1" 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"/> </when> @@ -289,17 +320,25 @@ label="The initial number of clusters (k)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/> <param name="centroids_s" type="text" value="2" label="The sparsity thresholding parameter by which to shrink the t-statistics (s)" - 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)"/> + 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> </param> <param name="centroids_toplabels" type="integer" value="500" - label="Number of toplabels (masses) which should be written in tabular output"/> + label="Number of toplabels (m/z) which should be written in tabular output"/> <param name="centroids_layout" type="text" value="1,1" 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"/> </when> </conditional> + <conditional name="image_cond"> + <param name="image_type" type="select" label="Select the image type"> + <option value="standard_image" selected="True">standard</option> + <option value="lattice_image">lattice</option> + </param> + <when value="standard_image"/> + <when value="lattice_image"/> + </conditional> <repeat name="colours" title="Colours for the plots" min="1" max="50"> <param name="feature_color" type="color" label="Colours" value="#ff00ff" help="Numbers of columns should be the same as number of components"> <sanitizer> @@ -309,11 +348,15 @@ </sanitizer> </param> </repeat> + <param name="output_rdata" type="boolean" display="radio" label="Results as .RData output"/> </inputs> <outputs> - <data format="pdf" name="segmentationimages" from_work_dir="segmentationpdf.pdf" label = "${tool.name} ${on_string}"/> - <data format="tabular" name="mzfeatures" label="Mz features ${on_string}"/> - <data format="tabular" name="pixeloutput" label="Pixels ${on_string}"/> + <data format="pdf" name="segmentationimages" from_work_dir="segmentationpdf.pdf" label = "$infile.display_name segmentation"/> + <data format="tabular" name="mzfeatures" label="$infile.display_name m/z features"/> + <data format="tabular" name="pixeloutput" label="$infile.display_name pixels"/> + <data format="rdata" name="segmentation_rdata" label="$infile.display_name segmentation"> + <filter>output_rdata</filter> + </data> </outputs> <tests> <test> @@ -322,6 +365,7 @@ <composite_data value="Example_Continuous.ibd"/> </param> <param name="segmentationtool" value="pca"/> + <param name="image_type" value="lattice_image"/> <repeat name="colours"> <param name="feature_color" value="#ff00ff"/> </repeat> @@ -351,9 +395,12 @@ <repeat name="colours"> <param name="feature_color" value="#00C957"/> </repeat> + <param name="output_rdata" value="True"/> <output name="segmentationimages" file="kmeans_analyze.pdf" compare="sim_size" delta="20000"/> <output name="mzfeatures" file="toplabels_skm.tabular" compare="sim_size"/> <output name="pixeloutput" file="cluster_skm.tabular" compare="sim_size"/> + <output name="pixeloutput" file="cluster_skm.tabular" compare="sim_size"/> + <output name="segmentation_rdata" file="cluster_skm.RData" compare="sim_size"/> </test> <test> <param name="infile" value="preprocessed.RData" ftype="rdata"/> @@ -386,7 +433,7 @@ Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_ -This tool provides three different Cardinal functions for unsupervised clustering/spatial segmentation of mass-spectrometry imaging data. +This tool provides three different Cardinal functions for unsupervised clustering/spatial segmentation of mass spectrometry imaging data. Input data: 3 types of input data can be used: @@ -403,7 +450,8 @@ Output: - Pdf with the heatmaps and plots for the segmentation -- Tabular file with information on masses and pixels: loadings/scores (PCA), toplabels/clusters (k-means), toplabels/classes (spatial shrunken centroids) +- Tabular file with information on m/z and pixels: loadings/scores (PCA), toplabels/clusters (k-means), toplabels/classes (spatial shrunken centroids) +- Optional .RData file which contains the segmentation results and can be used for further exploration in R ]]> </help>