--- a/classification.xml	Thu Oct 25 07:27:07 2018 -0400
+++ b/classification.xml	Fri Feb 15 10:18:51 2019 -0500
@@ -1,4 +1,4 @@
-<tool id="cardinal_classification" name="MSI classification" version="@VERSION@.1">
+<tool id="cardinal_classification" name="MSI classification" version="@VERSION@.2">
     <description>spatial classification of mass spectrometry imaging data</description>
     <macros>
         <import>macros.xml</import>
@@ -28,12 +28,13 @@
 library(lattice)
 library(ggplot2)
 
-@READING_MSIDATA@
+@READING_MSIDATA_INRAM@
 
-## create full matrix to make processed imzML files compatible with classification
-iData(msidata) <- iData(msidata)[] 
+## to make sure that processed files work as well: 
+iData(msidata) = iData(msidata)[]
 
-@DATA_PROPERTIES@
+@DATA_PROPERTIES_INRAM@
+
 
 ######################################## PDF ###################################
 ################################################################################
@@ -60,7 +61,7 @@
 grid.table(property_df, rows= NULL)
 
 
-if (npeaks > 0 && sum(is.na(spectra(msidata)))==0){
+if (npeaks > 0 && sum(is.na(spectra(msidata)[]))==0){
 
     opar <- par()
 
@@ -141,6 +142,10 @@
                 ## PLS-cvApply:
                 msidata.cv.pls <- cvApply(msidata, .y = y_vector, .fold = fold_vector, .fun = "PLS", ncomp = components)
 
+                ## remove msidata to clean up RAM space
+                rm(msidata)
+                gc()
+
                 ## create table with summary
                 count = 1
                 summary_plscv = list()
@@ -231,9 +236,13 @@
                 x_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,2]
                 y_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,3]
 
+                ## remove msidata to clean up RAM space
+                rm(msidata)
+                gc()
                 pls_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, pls_classes)
                 colnames(pls_classes2) = c("pixel names", "x", "y","predicted condition")
                 pls_toplabels = topLabels(msidata.pls, n=$type_cond.method_cond.analysis_cond.pls_toplabels)
+                pls_toplabels[,4:6] <-round(pls_toplabels[,4:6],6)
                 write.table(pls_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
                 write.table(pls_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
 
@@ -275,6 +284,10 @@
                 ## OPLS-cvApply:
                 msidata.cv.opls <- cvApply(msidata, .y = y_vector, .fold = fold_vector, .fun = "OPLS", ncomp = components, keep.Xnew = $type_cond.method_cond.opls_analysis_cond.xnew_cv)
 
+                ## remove msidata to clean up RAM space
+                rm(msidata)
+                gc()
+
                 ## create table with summary
                 count = 1
                 summary_oplscv = list()
@@ -369,7 +382,12 @@
                 opls_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, opls_classes)
                 colnames(opls_classes2) = c("pixel names", "x", "y","predicted condition")
 
+                ## remove msidata to clean up RAM space
+                rm(msidata)
+                gc()
+
                 opls_toplabels = topLabels(msidata.opls, n=$type_cond.method_cond.opls_analysis_cond.opls_toplabels)
+                opls_toplabels[,4:6] <-round(opls_toplabels[,4:6],6)
                 write.table(opls_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
                 write.table(opls_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
 
@@ -410,6 +428,10 @@
                 ## SSC-cvApply:
                 msidata.cv.ssc <- cvApply(msidata, .y = y_vector,.fold = fold_vector,.fun = "spatialShrunkenCentroids", r = c($type_cond.method_cond.ssc_r), s = c($type_cond.method_cond.ssc_s), method = "$type_cond.method_cond.ssc_kernel_method")
 
+                ## remove msidata to clean up RAM space
+                rm(msidata)
+                gc()
+
                 ## create table with summary
                 count = 1
                 summary_ssccv = list()
@@ -502,9 +524,15 @@
                 pixel_names = gsub(" = ", "y_", pixel_names)
                 x_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,2]
                 y_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,3]
+
+                ## remove msidata to clean up RAM space
+                rm(msidata)
+                gc()
+
                 ssc_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, ssc_classes)
                 colnames(ssc_classes2) = c("pixel names", "x", "y","predicted condition")
                 ssc_toplabels = topLabels(msidata.ssc, n=$type_cond.method_cond.ssc_analysis_cond.ssc_toplabels)
+                ssc_toplabels[,6:9] <-round(ssc_toplabels[,6:9],6)
                 write.table(ssc_toplabels, file="$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")
 
@@ -571,6 +599,11 @@
         predicted_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, predicted_classes)
         colnames(predicted_classes2) = c("pixel names", "x", "y","predicted condition")
         predicted_toplabels = topLabels(prediction, n=$type_cond.predicted_toplabels)
+        if (colnames(predicted_toplabels)[4] == "coefficients"){
+            predicted_toplabels[,4:6] <-round(predicted_toplabels[,4:6],5)
+
+        }else{
+            predicted_toplabels[,6:9] <-round(predicted_toplabels[,6:9],5)}
         write.table(predicted_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
         write.table(predicted_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
 
@@ -745,7 +778,7 @@
         <param name="output_rdata" type="boolean" label="Results as .RData output" help="Can be used to generate a classification prediction on new data"/>
     </inputs>
     <outputs>
-        <data format="pdf" name="classification_images" from_work_dir="classificationpdf.pdf" label = "${tool.name} on ${on_string}"/>
+        <data format="pdf" name="classification_images" from_work_dir="classificationpdf.pdf" label = "${tool.name} on ${on_string}: results"/>
         <data format="tabular" name="mzfeatures" label="${tool.name} on ${on_string}: features"/>
         <data format="tabular" name="pixeloutput" label="${tool.name} on ${on_string}: pixels"/>
         <data format="rdata" name="classification_rdata" label="${tool.name} on ${on_string}: results.RData">