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comparison coreograph.xml @ 2:224e0cf4aaeb draft

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planemo upload for repository https://github.com/ohsu-comp-bio/UNetCoreograph commit cb09eb9d2fa0feae993ae994b6beae05972c644b
author goeckslab
date Thu, 01 Sep 2022 22:43:42 +0000
parents 57f1260ca94e
children ee92746d141a
comparison
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1:57f1260ca94e 2:224e0cf4aaeb
1 <tool id="unet_coreograph" name="UNetCoreograph" version="@VERSION@.3" profile="17.09"> 1 <tool id="unet_coreograph" name="UNetCoreograph" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="19.01">
2 <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> 2 <description>TMA core detection and dearraying</description>
3 <macros> 3 <macros>
4 <import>macros.xml</import> 4 <import>macros.xml</import>
5 </macros> 5 </macros>
6 6
7 <expand macro="requirements"/> 7 <expand macro="requirements"/>
8 @VERSION_CMD@ 8 <expand macro="version_cmd"/>
9 9
10 <command detect_errors="exit_code"><![CDATA[ 10 <command detect_errors="exit_code"><![CDATA[
11 #set $type_corrected = str($source_image)[:-3]+'ome.tif' 11 #set $type_corrected = 'image.' + str($source_image.file_ext)
12 ln -s $source_image `basename $type_corrected`; 12 ln -s '$source_image' '$type_corrected' &&
13 13
14 @CMD_BEGIN@ 14 @CMD_BEGIN@
15 15
16 python \$UNET_PATH 16 python \$UNET_PATH
17 --imagePath `basename $type_corrected` 17 --imagePath '$type_corrected'
18 --downsampleFactor $downsamplefactor 18 --downsampleFactor $downsamplefactor
19 --channel $channel 19 --channel $channel
20 --buffer $buffer 20 --buffer $buffer
21 --sensitivity $sensitivity 21 --sensitivity $sensitivity
22 22 $cluster
23 ##if $usegrid 23 $tissue
24 ##--useGrid 24 --outputPath '.'
25 ##end if
26
27 #if $cluster
28 --cluster
29 #end if
30
31 #if $tissue
32 --tissue
33 #end if
34
35 --outputPath .;
36 25
37 ]]></command> 26 ]]></command>
38 27
39 28
40 <inputs> 29 <inputs>
41 <param name="source_image" type="data" format="tiff" label="Registered TIFF"/> 30 <param name="source_image" type="data" format="tiff,ome.tiff" label="Registered TIFF"/>
42 <param name="downsamplefactor" type="integer" value="5" label="Down Sample Factor"/> 31 <param name="downsamplefactor" type="integer" value="5" label="Down Sample Factor"/>
43 <param name="channel" type="integer" value="0" label="Channel"/> 32 <param name="channel" type="integer" value="0" label="Channel"/>
44 <param name="buffer" type="float" value="2.0" label="Buffer"/> 33 <param name="buffer" type="float" value="2.0" label="Buffer"/>
45 <param name="sensitivity" type="float" value="0.3" label="Sensitivity"/> 34 <param name="sensitivity" type="float" value="0.3" label="Sensitivity"/>
46 <!--<param name="usegrid" type="boolean" label="Use Grid"/>--> 35 <param name="cluster" type="boolean" truevalue="--cluster" falsevalue="" checked="false" label="Cluster"/>
47 <param name="cluster" type="boolean" checked="false" label="Cluster"/> 36 <param name="tissue" type="boolean" truevalue="--tissue" falsevalue="" checked="false" label="Tissue"/>
48 <param name="tissue" type="boolean" checked="false" label="Tissue"/>
49 </inputs> 37 </inputs>
50 38
51 <outputs> 39 <outputs>
52 <collection name="tma_sections" type="list" label="${tool.name} on ${on_string}: Images"> 40 <collection name="tma_sections" type="list" label="${tool.name} on ${on_string}: Images">
53 <discover_datasets pattern="(?P&lt;designation&gt;[0-9]+)\.tif" format="tiff" visible="false"/> 41 <discover_datasets pattern="(?P&lt;designation&gt;[0-9]+)\.tif" format="tiff" visible="false"/>
55 <collection name="masks" type="list" label="${tool.name} on ${on_string}: Masks"> 43 <collection name="masks" type="list" label="${tool.name} on ${on_string}: Masks">
56 <discover_datasets pattern="(?P&lt;designation&gt;[0-9]+)_mask\.tif" directory="masks" format="tiff" visible="false"/> 44 <discover_datasets pattern="(?P&lt;designation&gt;[0-9]+)_mask\.tif" directory="masks" format="tiff" visible="false"/>
57 </collection> 45 </collection>
58 <data name="TMA_MAP" format="tiff" label="${tool.name} on ${on_string}: TMA Map" from_work_dir="TMA_MAP.tif"/> 46 <data name="TMA_MAP" format="tiff" label="${tool.name} on ${on_string}: TMA Map" from_work_dir="TMA_MAP.tif"/>
59 </outputs> 47 </outputs>
48 <tests>
49 <test>
50 <param name="source_image" value="coreograph_test.tiff" />
51 <output_collection name="tma_sections" type="list">
52 <element name="1" ftype="tiff">
53 <assert_contents>
54 <has_size value="18000" delta="1000" />
55 </assert_contents>
56 </element>
57 <element name="2" ftype="tiff">
58 <assert_contents>
59 <has_size value="18000" delta="1000" />
60 </assert_contents>
61 </element>
62 </output_collection>
63 <output_collection name="masks" type="list">
64 <element name="1" ftype="tiff">
65 <assert_contents>
66 <has_size value="345" delta="100" />
67 </assert_contents>
68 </element>
69 <element name="2" ftype="tiff">
70 <assert_contents>
71 <has_size value="345" delta="100" />
72 </assert_contents>
73 </element>
74 </output_collection>
75 <output name="TMA_MAP" ftype="tiff">
76 <assert_contents>
77 <has_size value="530" delta="100" />
78 </assert_contents>
79 </output>
80 </test>
81 </tests>
60 <help><![CDATA[ 82 <help><![CDATA[
83 -------------------
84 UNet Coreograph
85 -------------------
86 **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
87
88 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.
89
90 **Inputs**
91 A tif or ome.tiff image multiple tissues, such as a tissue microarray.
92
93 **Outputs**
94 Coreograph exports these files:
95 1. individual cores as tiff stacks with user-selectable channel ranges
96 2. binary tissue masks (saved in the 'mask' subfolder)
97 3. a TMA map showing the labels and outlines of each core for quality control purposes
61 ]]></help> 98 ]]></help>
62 <expand macro="citations" /> 99 <expand macro="citations" />
63 </tool> 100 </tool>