diff coreograph.xml @ 2:224e0cf4aaeb draft

planemo upload for repository https://github.com/ohsu-comp-bio/UNetCoreograph commit cb09eb9d2fa0feae993ae994b6beae05972c644b

--- a/coreograph.xml	Fri Mar 11 23:40:51 2022 +0000
+++ b/coreograph.xml	Thu Sep 01 22:43:42 2022 +0000
@@ -1,51 +1,39 @@
-<tool id="unet_coreograph" name="UNetCoreograph" version="@VERSION@.3" profile="17.09">
-    <description>Coreograph uses UNet, a deep learning model, to identify complete/incomplete tissue cores on a tissue microarray. It has been trained on 9 TMA slides of different sizes and tissue types.</description>
+<tool id="unet_coreograph" name="UNetCoreograph" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="19.01">
+    <description>TMA core detection and dearraying</description>
     <macros>
         <import>macros.xml</import>
     </macros>
  
     <expand macro="requirements"/>
-    @VERSION_CMD@
+    <expand macro="version_cmd"/>
 
     <command detect_errors="exit_code"><![CDATA[
-        #set $type_corrected = str($source_image)[:-3]+'ome.tif'
-        ln -s $source_image `basename $type_corrected`;
-
+        #set $type_corrected = 'image.' + str($source_image.file_ext)
+        ln -s '$source_image' '$type_corrected' &&
+        
         @CMD_BEGIN@
 
         python \$UNET_PATH
-        --imagePath `basename $type_corrected`
+        --imagePath '$type_corrected'
         --downsampleFactor $downsamplefactor
         --channel $channel
         --buffer $buffer
         --sensitivity $sensitivity
-
-        ##if $usegrid
-        ##--useGrid
-        ##end if
-
-        #if $cluster
-        --cluster
-        #end if
-
-        #if $tissue
-        --tissue
-        #end if
-
-        --outputPath .;
+        $cluster
+        $tissue
+        --outputPath '.'
         
     ]]></command>
 
 
     <inputs>
-        <param name="source_image" type="data" format="tiff" label="Registered TIFF"/>
+        <param name="source_image" type="data" format="tiff,ome.tiff" label="Registered TIFF"/>
         <param name="downsamplefactor" type="integer" value="5" label="Down Sample Factor"/>
         <param name="channel" type="integer" value="0" label="Channel"/>
         <param name="buffer" type="float" value="2.0" label="Buffer"/>
         <param name="sensitivity" type="float" value="0.3" label="Sensitivity"/>
-        <!--<param name="usegrid" type="boolean" label="Use Grid"/>-->
-        <param name="cluster" type="boolean" checked="false" label="Cluster"/>
-        <param name="tissue" type="boolean" checked="false" label="Tissue"/>
+        <param name="cluster" type="boolean" truevalue="--cluster" falsevalue="" checked="false" label="Cluster"/>
+        <param name="tissue" type="boolean" truevalue="--tissue" falsevalue="" checked="false" label="Tissue"/>
     </inputs>
 
     <outputs>
@@ -57,7 +45,56 @@
         </collection>
         <data name="TMA_MAP" format="tiff" label="${tool.name} on ${on_string}: TMA Map" from_work_dir="TMA_MAP.tif"/>
     </outputs>
+    <tests>
+        <test>
+            <param name="source_image" value="coreograph_test.tiff" />
+            <output_collection name="tma_sections" type="list">
+                <element name="1" ftype="tiff">
+                    <assert_contents>
+                        <has_size value="18000" delta="1000" />
+                    </assert_contents>
+                </element>
+                <element name="2" ftype="tiff">
+                    <assert_contents>
+                        <has_size value="18000" delta="1000" />
+                    </assert_contents>
+                </element>
+            </output_collection>
+            <output_collection name="masks" type="list">
+                <element name="1" ftype="tiff">
+                    <assert_contents>
+                        <has_size value="345" delta="100" />
+                    </assert_contents>
+                </element>
+                <element name="2" ftype="tiff">
+                    <assert_contents>
+                        <has_size value="345" delta="100" />
+                    </assert_contents>
+                </element>
+            </output_collection>
+            <output name="TMA_MAP" ftype="tiff">
+                <assert_contents>
+                    <has_size value="530" delta="100" />
+                </assert_contents>
+            </output>
+        </test>
+    </tests>
     <help><![CDATA[
+-------------------        
+UNet Coreograph
+-------------------
+**Coreograph** uses UNet, a deep learning model, to identify complete/incomplete tissue cores on a tissue microarray. It has been trained on 9 TMA slides of different sizes and tissue types
+
+Training sets were acquired at 0.2micron/pixel resolution and downsampled 1/32 times to speed up performance. Once the center of each core has been identifed, active contours is used to generate a tissue mask of each core that can aid downstream single cell segmentation. A GPU is not required but will reduce computation time.
+
+**Inputs**
+A tif or ome.tiff image multiple tissues, such as a tissue microarray.
+
+**Outputs**
+Coreograph exports these files:
+1. individual cores as tiff stacks with user-selectable channel ranges
+2. binary tissue masks (saved in the 'mask' subfolder)
+3. a TMA map showing the labels and outlines of each core for quality control purposes
     ]]></help>
     <expand macro="citations" />
 </tool>