--- a/segmentation.xml	Thu Feb 28 09:23:26 2019 -0500
+++ b/segmentation.xml	Fri Mar 22 08:15:15 2019 -0400
@@ -1,4 +1,4 @@
-<tool id="cardinal_segmentations" name="MSI segmentation" version="@VERSION@.2">
+<tool id="cardinal_segmentations" name="MSI segmentation" version="@VERSION@.3">
     <description>mass spectrometry imaging spatial clustering</description>
     <macros>
         <import>macros.xml</import>
@@ -30,15 +30,10 @@
 
 @READING_MSIDATA_INRAM@
 
-## to make sure that processed files work as well: 
-iData(msidata) = iData(msidata)[]
 
 ## remove duplicated coordinates
-print(paste0(sum(duplicated(coord(msidata))), " duplicated coordinates were removed"))
 msidata <- msidata[,!duplicated(coord(msidata))]
 
-## count and print number of NAs, all methods are not compatible with NAs
-print(paste0("Number of NA in dataset: ", sum(is.na(spectra(msidata)[])), " - segmentation does not work with NA values"))
 
 @DATA_PROPERTIES_INRAM@
 
@@ -58,7 +53,7 @@
 #############################################################################
 grid.table(property_df, rows= NULL)
 
-if (npeaks > 0 && sum(is.na(spectra(msidata)[]))==0)
+if (npeaks > 0 && sum(is.na(spectra(msidata)))==0)
 {
 
 ######################## II) segmentation tools #############################
@@ -67,13 +62,13 @@
         colourvector = c($color_string)
 
         ### preparation for images and plots:
-        #if str($image_cond.image_type) == "standard_image":
+        #if str($image_type) == "standard_image":
             print("standard image")
 
-            strip_input = TRUE
+            strip_input = FALSE
             lattice_input = FALSE
 
-        #elif str($image_cond.image_type) == "lattice_image":
+        #elif str($image_type) == "lattice_image":
             print("lattice image")
 
             strip_input = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))
@@ -112,11 +107,11 @@
             ### images in pdf file
             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:$segm_cond.pca_ncomp){
-                print(image(pca_result, column = c(paste0("PC",PCs)), lattice=lattice_input, superpose = FALSE, col.regions = risk.colors(100), ylim=c(maximumy+2, minimumy-2)))}
+                print(image(pca_result, column = c(paste0("PC",PCs)), lattice=lattice_input,strip = strip_input, superpose = FALSE, main=paste0("PC", PCs), col.regions = risk.colors(100), ylim=c(maximumy+2, minimumy-2)))}
             ### plots in pdf file
             print(plot(pca_result, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input))
             for (PCs in 1:$segm_cond.pca_ncomp){
-                print(plot(pca_result, column = c(paste0("PC",PCs)),superpose = FALSE))}
+                print(plot(pca_result, column = c(paste0("PC",PCs)),main=paste0("PC", PCs),strip = FALSE,superpose = FALSE))}
 
             ### values in tabular files
             pcaloadings = formatC(pca_result@resultData\$ncomp\$loadings, format = "e", digits = 6)### loading for each m/z value
@@ -155,7 +150,6 @@
             gc()
 
             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(1,1)))
 
             skm_clusters = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
@@ -194,9 +188,10 @@
             ## remove msidata to clean up RAM space
             rm(msidata)
             gc()
-            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(1,1)))
+            print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=lattice_input, strip = TRUE, 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 = TRUE,layout=c(1,1)))
             print(plot(ssc, mode = "tstatistics",key = TRUE, lattice=lattice_input, layout = c(1,1), main="t-statistics", col=colourvector))
+
             plot(summary(ssc), main = "Number of segments")
 
             ssc_classes = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
@@ -231,6 +226,28 @@
 
     dev.off()
 
+        ## optional svg output with original coordinates
+        #if $svg_pixelimage:
+            print("svg image")
+            ## reverse y axis for svg output = correct order and nice svg image
+
+
+            svg(file="svg_pixel_output.svg", width=maximumx, height=maximumy)
+            par(mar=c(0,0,0,0))
+            #if str( $segm_cond.segmentationtool ) == 'pca':
+                coord(pca_result)\$y <- max(coord(pca_result)\$y) - coord(pca_result)\$y + 1
+                image(pca_result, strip = FALSE, colorkey=FALSE, axes=FALSE, xlab=NA, ylab=NA, col=colourvector)
+            #elif str( $segm_cond.segmentationtool ) == 'kmeans':
+                coord(skm)\$y <- max(coord(skm)\$y) - coord(skm)\$y + 1
+                image(skm, key=FALSE, strip=FALSE, col= colourvector)
+            #elif str( $segm_cond.segmentationtool ) == 'centroids':
+                coord(ssc)\$y <- max(coord(ssc)\$y) - coord(ssc)\$y + 1
+                image(ssc, key=FALSE, strip = FALSE, col= colourvector)
+            #end if
+            dev.off()
+        #end if
+
+
 }else{
     print("Inputfile has no intensities > 0")
     dev.off()
@@ -256,7 +273,6 @@
                     </param>
                     <param name="pca_scale" type="boolean" truevalue="TRUE" falsevalue="FALSE" label="Scaling of data before analysis"/>
                 </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)">
@@ -297,14 +313,9 @@
                        label="Number of toplabels (m/z) which should be written in tabular output"/>
                 </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>
+            <param name="image_type" type="boolean" checked="True" truevalue="standard_image" falsevalue="lattice_image" 
+            label="Standard image" help="No: lattice function is used to display image"/>
+            <param name="svg_pixelimage" type="boolean" label="Export first segmentation image as svg"/>
             <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 colours should be the same as number of components">
                   <sanitizer>
@@ -324,6 +335,9 @@
         <data format="rdata" name="segmentation_rdata" label="${tool.name} on ${on_string}: results.RData">
             <filter>output_rdata</filter>
         </data>
+        <data format="svg" name="svg_output" from_work_dir="svg_pixel_output.svg" label="${tool.name} on ${on_string}: image.svg">
+            <filter>svg_pixelimage</filter>
+        </data>
     </outputs>
     <tests>
         <test>
@@ -404,18 +418,22 @@
 This tool provides three different Cardinal functions for unsupervised clustering/spatial segmentation of mass spectrometry imaging data.
 
 @MSIDATA_INPUT_DESCRIPTION@
+            - NA intensities are not allowed
+            - duplicated coordinates will be removed
+
 
 **Options**
 
 - PCA: principal component analysis
-- k-means: spatially-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
+- k-means: spatially-aware k-means clustering (adopted from `Alexandrov and Kobarg <https://doi.org/10.1093/bioinformatics/btr246>`_)
+- spatial shrunken centroids: Allows the number of segments to decrease according to the data. This allows selection of the number of clusters (more details in `Bemis et al. <https://doi.org/10.1074/mcp.O115.053918>`_)
 
 **Output**
 
 - Pdf with the heatmaps and plots for the segmentation
 - 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 using the Cardinal package
+- Optional: svg file with the first segmentation image
 
         ]]>
     </help>