mass_spectrometry_imaging_segmentations: segmentation

comparison 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

comparison

equal deleted inserted replaced

-:38afc994e3cc
+:25e1e84cfe1b
-<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.0">
+<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.1">
-<description>tool for spatial clustering</description>
+<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>
 <requirement type="package" version="0.20-35">r-lattice</requirement>
 </requirements>
 </command>
 <configfiles>
 <configfile name="MSI_segmentation"><![CDATA[
-################################# load libraries and read file #########################
+################################# load libraries and read file #################
 library(Cardinal)
 library(gridExtra)
 library(lattice)
 load('infile.RData')
 #end if
 ###################################### 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)
 pixelcount = length(pixels(msidata))
 ## Range x coordinates
 minint = round(min(spectra(msidata)[]), digits=2)
 maxint = round(max(spectra(msidata)[]), digits=2)
 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)
 ## Number of empty TICs
 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) {
 normalizationinfo='FALSE'
 } else {
 peakpickinginfo='FALSE'
 } else {
 peakpickinginfo=processinginfo@peakPicking
 }
-#############################################################################
+properties = c("Number of m/z features",
+"Range of m/z values [Da]",
-properties = c("Number of mz features",
-"Range of mz values [Da]",
 "Number of pixels",
 "Range of x coordinates",
 "Range of y coordinates",
 "Range of intensities",
 "Median of intensities",
 ######################## II) segmentation tools #############################
 #############################################################################
 #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( $segm_cond.segmentationtool ) == 'pca':
 print('pca')
 ##pca
 for (numberofcomponents in 1:$segm_cond.pca_ncomp)
 {component_vector[numberofcomponents]= paste0("PC", numberofcomponents)}
 pca = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE,
 method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1))
-print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col=colourvector))
+print(image(pca, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector))
-print(plot(pca, main="PCA plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))))
+print(plot(pca, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input))
+pcaloadings = (pca@resultData\$ncomp\$loadings) ### loading for each m/z value
-pcaloadings = (pca@resultData\$ncomp\$loadings) ### loading for each mz value
 pcascores = (pca@resultData\$ncomp\$scores) ### scores for each pixel
 write.table(pcaloadings, file="$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)){
 skm_cluster = ((skm@resultData)[[iteration]]\$cluster)
 skm_clusters = cbind(skm_clusters, skm_cluster) }
 skm_toplabels = topLabels(skm, n=$segm_cond.kmeans_toplabels)
 write.table(skm_toplabels, file="$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="$segm_cond.centroids_method")
-print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col= colourvector,layout=c(1,1)))
+print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=lattice_input, strip = strip_input, col= colourvector,layout=c(1,1)))
-print(plot(ssc, main="Spatial shrunken centroids plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)),layout=c($segm_cond.centroids_layout)))
+print(plot(ssc, main="Spatial shrunken centroids plot", lattice=lattice_input, col= colourvector, strip = strip_input,layout=c($segm_cond.centroids_layout)))
 ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
 for (iteration in 1:length(ssc@resultData)){
 ssc_class = ((ssc@resultData)[[iteration]]\$classes)
 ssc_classes = cbind(ssc_classes, ssc_class) }
 ssc_toplabels =  topLabels(ssc, n=$segm_cond.centroids_toplabels)
 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
 dev.off()
 label="The method to use to calculate the spatial smoothing kernels for the embedding. The 'gaussian' method refers to spatially-aware (SA) clustering, and 'adaptive' refers to spatially-aware structurally-adaptive (SASA) clustering">
 <option value="gaussian">gaussian</option>
 <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>
 <when value="centroids">
 label="The spatial neighborhood radius of nearby pixels to consider (r)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>
 <param name="centroids_k" type="text" value="5"
 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>
 <valid initial="string.letters,string.digits">
 <add value="#" />
 </valid>
 </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} ${on_string}"/>
+<data format="pdf" name="segmentationimages" from_work_dir="segmentationpdf.pdf" label = "$infile.display_name segmentation"/>
-<data format="tabular" name="mzfeatures" label="Mz features ${on_string}"/>
+<data format="tabular" name="mzfeatures" label="$infile.display_name m/z features"/>
-<data format="tabular" name="pixeloutput" label="Pixels ${on_string}"/>
+<data format="tabular" name="pixeloutput" label="$infile.display_name pixels"/>
+<data format="rdata" name="segmentation_rdata" label="$infile.display_name segmentation">
+<filter>output_rdata</filter>
+</data>
 </outputs>
 <tests>
 <test>
 <param name="infile" value="" ftype="imzml">
 <composite_data value="Example_Continuous.imzML"/>
 <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>
 <repeat name="colours">
 <param name="feature_color" value="#0000FF"/>
 <param name="feature_color" value="#0000FF"/>
 </repeat>
 <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"/>
 <param name="segmentationtool" value="centroids"/>
 <param name="centroids_r" value="1,2"/>
 <help>
 <![CDATA[
 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:
 - imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_
 - Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
 - spatial shrunken centroids: Allows the number of segments to decrease according to the data. This allows automatic selection of the number of clusters
 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>
 <citations>
 <citation type="doi">10.1093/bioinformatics/btv146</citation>

Mercurial > repos > galaxyp > mass_spectrometry_imaging_segmentations

comparison segmentation_tool.xml @ 5:25e1e84cfe1b draft