diff segmentation_tool.xml @ 4:aec189b0c64d draft

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_segmentation commit 1c808d60243bb1eeda0cd26cb4b0a17ab05de2c0

--- a/segmentation_tool.xml	Mon Apr 23 17:19:53 2018 -0400
+++ b/segmentation_tool.xml	Mon May 28 12:39:28 2018 -0400
@@ -1,21 +1,20 @@
-<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.7.0.3">
+<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.0">
     <description>tool for spatial clustering</description>
     <requirements>
-        <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement>
+        <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>
         <requirement type="package" version="2.2.1">r-gridextra</requirement>
-        <requirement type="package" version="2.23-15">r-kernsmooth</requirement>
         <requirement type="package" version="0.20-35">r-lattice</requirement>
     </requirements>
     <command detect_errors="exit_code">
     <![CDATA[
 
         #if $infile.ext == 'imzml'
-            cp '${infile.extra_files_path}/imzml' infile.imzML &&
-            cp '${infile.extra_files_path}/ibd' infile.ibd &&
+            ln -s '${infile.extra_files_path}/imzml' infile.imzML &&
+            ln -s '${infile.extra_files_path}/ibd' infile.ibd &&
         #elif $infile.ext == 'analyze75'
-            cp '${infile.extra_files_path}/hdr' infile.hdr &&
-            cp '${infile.extra_files_path}/img' infile.img &&
-            cp '${infile.extra_files_path}/t2m' infile.t2m &&
+            ln -s '${infile.extra_files_path}/hdr' infile.hdr &&
+            ln -s '${infile.extra_files_path}/img' infile.img &&
+            ln -s '${infile.extra_files_path}/t2m' infile.t2m &&
         #else
             ln -s $infile infile.RData &&
         #end if
@@ -34,20 +33,19 @@
 
 library(Cardinal)
 library(gridExtra)
-library(KernSmooth)
 library(lattice)
 
 ## Read MALDI Imaging dataset
 
 #if $infile.ext == 'imzml'
-    msidata = readMSIData('infile.imzML')
+    msidata = readImzML('infile')
 #elif $infile.ext == 'analyze75'
-    msidata = readMSIData('infile.hdr')
+    msidata = readAnalyze('infile')
 #else
     load('infile.RData')
 #end if
 
-###################################### file properties in numbers ######################
+###################################### file properties in numbers ##############
 
 ## Number of features (mz)
 maxfeatures = length(features(msidata))
@@ -145,9 +143,9 @@
 property_df = data.frame(properties, values)
 
 
-######################################## PDF #############################################
-##########################################################################################
-##########################################################################################
+######################################## PDF ###################################
+################################################################################
+################################################################################
 
 
 pdf("segmentationpdf.pdf", fonts = "Times", pointsize = 12)
@@ -180,7 +178,7 @@
             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, ylim=c(maximumy+2, 0)))
+            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))))
 
 
@@ -195,7 +193,7 @@
             ##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, ylim=c(maximumy+2, 0), layout=c(1,1)))
+            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(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)))
 
@@ -217,7 +215,7 @@
             ##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,  ylim=c(maximumy+2, 0),layout=c(1,1)))
+            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)))
 
             ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
@@ -248,33 +246,33 @@
                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)"/>
             <conditional name="segm_cond">
-                <param name="segmentationtool" type="select" label="Select the tool for spatial clustering.">
+                <param name="segmentationtool" type="select" label="Select the tool for spatial clustering">
                     <option value="pca" selected="True">pca</option>
                     <option value="kmeans">k-means</option>
-                    <option value="centroids">shrunken centroids</option>
+                    <option value="centroids">spatial shrunken centroids</option>
                 </param>
                 <when value="pca">
                     <param name="pca_ncomp" type="integer" value="2"
-                           label="The number of principal components to calculate."/>
+                           label="The number of principal components to calculate"/>
                     <param name="pca_method" type="select" 
-                           label="The function used to calculate the singular value decomposition.">
+                           label="The function used to calculate the singular value decomposition">
                         <option value="irlba" selected="True">irlba</option>
                         <option value="svd">svd</option>
                     </param>
                     <param name="pca_scale" type="select" display="radio" optional="False"
-                           label="Shoud the data be scaled first?">
+                           label="Scaling of data before analysis">
                         <option value="TRUE">yes</option>
                         <option value="FALSE" selected="True">no</option>
                 </param>
-                </when> 
+                </when>
 
                 <when value="kmeans">
                     <param name="kmeans_r" type="text" value="2"
-                           label="The spatial neighborhood radius of nearby pixels to consider (r)." help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>
+                           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="kmeans_k" type="text" value="3"
-                           label="The number of clusters (k)." help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>
+                           label="The number of clusters (k)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>
                     <param name="kmeans_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) clustering, and 'adaptive' refers to spatially-aware structurally-adaptive (SASA) clustering.">
+                           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>
@@ -286,13 +284,13 @@
 
                 <when value="centroids">
                     <param name="centroids_r" type="text" value="2"
-                           label="The spatial neighborhood radius of nearby pixels to consider (r)." help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>
+                           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)"/>
+                           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)."/>
-                    <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.">
+                           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)"/>
+                    <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>
@@ -343,6 +341,7 @@
             <param name="segmentationtool" value="kmeans"/>
             <param name="kmeans_r" value="1:3"/>
             <param name="kmeans_k" value="2,3"/>
+            <param name="kmeans_toplabels" value="20"/>
             <repeat name="colours">
                 <param name="feature_color" value="#ff00ff"/>
             </repeat>
@@ -352,16 +351,16 @@
             <repeat name="colours">
                 <param name="feature_color" value="#00C957"/>
             </repeat>
-            <output name="segmentationimages" file="kmeans_imzml.pdf" compare="sim_size" delta="20000"/>
+            <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"/>
         </test>
         <test>
-            <param name="infile" value="preprocessing_results1.RData" ftype="rdata"/>
+            <param name="infile" value="preprocessed.RData" ftype="rdata"/>
             <param name="segmentationtool" value="centroids"/>
-            <param name="centroids_r" ftype="text" value="1,2"/>
-            <param name="centroids_k" ftype="text" value="5"/>
-            <param name="centroids_toplabels" ftype="integer" value="100"/>
+            <param name="centroids_r" value="1,2"/>
+            <param name="centroids_k" value="5"/>
+            <param name="centroids_toplabels" value="50"/>
             <repeat name="colours">
                 <param name="feature_color" value="#0000FF"/>
             </repeat>
@@ -377,7 +376,7 @@
             <repeat name="colours">
                 <param name="feature_color" value="#848484"/>
             </repeat>
-            <output name="segmentationimages" file="centroids_imzml.pdf" compare="sim_size" delta="20000"/>
+            <output name="segmentationimages" file="centroids_rdata.pdf" compare="sim_size" delta="20000"/>
             <output name="mzfeatures" file="toplabels_ssc.tabular" compare="sim_size"/>
             <output name="pixeloutput" file="classes_ssc.tabular" compare="sim_size"/>
         </test>
@@ -385,16 +384,27 @@
     <help>
         <![CDATA[
 
-Spatially aware segmentation of mass-spectrometry imaging data by unsupervised clustering algorithms. Underlying structures can be identified with the following tools: pca, k-means clustering and spatial shrunken centroids. The spatialShrunkenCentroids method allows the number of segments to decrease according to the data. This allows automatic selection of the number
-of clusters.
+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.
 
 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 <http://ms-imaging.org/wp/introduction/>`_
+- 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)
 - Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData)
 
-The output of this tool contains a pdf with plots from the segmentation tools. 
+Options: 
+
+- PCA: principal component analysis
+- k-means: patially-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: 
+
+- 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)
+
         ]]>
     </help>
     <citations>