diff segmentation_tool.xml @ 8:4a62874c21a3 draft default tip

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_segmentation commit 5feaf3d0e0da8cef1241fecc1f4d6f81324594e6

--- a/segmentation_tool.xml	Fri Jul 06 14:14:27 2018 -0400
+++ b/segmentation_tool.xml	Wed Aug 22 13:44:28 2018 -0400
@@ -1,4 +1,4 @@
-<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.3">
+<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.10.0.4">
     <description>mass spectrometry imaging spatial clustering</description>
     <requirements>
         <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>
@@ -45,7 +45,11 @@
 #elif $infile.ext == 'analyze75'
     msidata = readAnalyze('infile')
 #else
-    load('infile.RData')
+    loadRData <- function(fileName){
+    load(fileName)
+    get(ls()[ls() != "fileName"])
+    }
+    msidata = loadRData('infile.RData')
 #end if
 
 
@@ -186,6 +190,8 @@
 
         #end if
 
+        ## set seed to make analysis reproducible
+        set.seed($setseed)
 
         #if str( $segm_cond.segmentationtool ) == 'pca':
             print('pca')
@@ -198,9 +204,9 @@
             method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1))
 
             ### images in pdf file
-            print(image(pca_result, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector))
+            print(image(pca_result, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector, ylim=c(maximumy+2, minimumy-2)))
             for (PCs in 1:$segm_cond.pca_ncomp){
-                print(image(pca_result, column = c(paste0("PC",PCs)), lattice=lattice_input, superpose = FALSE, col.regions = risk.colors(100)))}
+                print(image(pca_result, column = c(paste0("PC",PCs)), lattice=lattice_input, superpose = FALSE, col.regions = risk.colors(100), ylim=c(maximumy+2, minimumy-2)))}
             ### plots in pdf file
             print(plot(pca_result, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input))
             for (PCs in 1:$segm_cond.pca_ncomp){
@@ -208,10 +214,14 @@
 
             ### values in tabular files
             pcaloadings = (pca_result@resultData\$ncomp\$loadings) ### loading for each m/z value
+            pcaloadings2 = cbind(rownames(pcaloadings), pcaloadings)
+            colnames(pcaloadings2) = c("mz", colnames(pcaloadings))
             pcascores = (pca_result@resultData\$ncomp\$scores) ### scores for each pixel
+            pcascores2 = cbind(rownames(pcascores), pcascores)
+            colnames(pcascores2) = c("pixel names", colnames(pcascores))
 
-            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")
+            write.table(pcaloadings2, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
+            write.table(pcascores2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
 
             ## optional output as .RData
             #if $output_rdata:
@@ -225,7 +235,7 @@
             ##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=lattice_input, strip=strip_input, 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), ylim=c(maximumy+2, minimumy-2)))
 
             print(plot(skm, main="K-means plot", lattice=lattice_input, col= colourvector, strip=strip_input, layout=c($segm_cond.kmeans_layout)))
 
@@ -233,12 +243,13 @@
             for (iteration in 1:length(skm@resultData)){
                         skm_cluster = ((skm@resultData)[[iteration]]\$cluster)
             skm_clusters = cbind(skm_clusters, skm_cluster) }
-            colnames(skm_clusters) = names((skm@resultData)) 
+            skm_clusters2 = cbind(rownames(skm_clusters), skm_clusters)
+            colnames(skm_clusters2) = c("pixel names", names(skm@resultData))
 
             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="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
+
+            write.table(skm_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
+            write.table(skm_clusters2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
 
             ## optional output as .RData
             #if $output_rdata:
@@ -253,7 +264,7 @@
             ##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=lattice_input, strip = strip_input, 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), ylim=c(maximumy+2, minimumy-2)))
             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))
             plot(summary(ssc), main = "Number of segments")
@@ -262,12 +273,13 @@
             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))
+            ssc_classes2 = cbind(rownames(ssc_classes), ssc_classes)
+            colnames(ssc_classes2) = c("pixel names", names(ssc@resultData))
 
             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")
+            write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
+            write.table(ssc_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
 
             ## optional output as .RData
             #if $output_rdata:
@@ -379,6 +391,7 @@
                 </param>
             </repeat>
         <param name="output_rdata" type="boolean" display="radio" label="Results as .RData output"/>
+        <param name="setseed" type="integer" value="1" label="set seed" help="use same value to reproduce previous results"/>
     </inputs>
     <outputs>
         <data format="pdf" name="segmentationimages" from_work_dir="segmentationpdf.pdf" label = "$infile.display_name segmentation"/>