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planemo upload for repository https://github.com/HMS-IDAC/UnMicst commit e14f76a8803cab0013c6dbe809bc81d7667f2ab9
author goeckslab
date Wed, 07 Sep 2022 23:10:14 +0000
parents 6bec4fef6b2e
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0:6bec4fef6b2e 1:74fe58ff55a5
1 <tool id="unmicst" name="UnMicst" version="@VERSION@.1" profile="17.09"> 1 <tool id="unmicst" name="UnMicst" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="19.01">
2 <description>UNet Model for Identifying Cells and Segmenting Tissue</description> 2 <description>Image segmentation - probability map generation</description>
3
3 <macros> 4 <macros>
4 <import>macros.xml</import> 5 <import>macros.xml</import>
5 </macros> 6 </macros>
6 7
7 <expand macro="requirements"/> 8 <expand macro="requirements"/>
8 @VERSION_CMD@ 9 <expand macro="version_cmd"/>
9 10
10 <command detect_errors="exit_code"><![CDATA[ 11 <command detect_errors="exit_code"><![CDATA[
11 #set $typeCorrected = str($image.name).replace('.ome.tiff','').replace('.ome.tif','').replace('.tiff','').replace('.tif','')+'.ome.tif' 12 #set $ext = str($image.file_ext)
13 #if $ext == 'tiff'
14 #set $ext = 'tif'
15 #end if
16 #set $input = 'image.' + str($ext)
12 17
13 ln -s $image '$typeCorrected'; 18 ln -s '$image' '$input' &&
19
20 @CMD_BEGIN@ '$input'
14 21
15 @CMD_BEGIN@ '$typeCorrected' 22 --tool $tool
16 23
17 #if $stackoutput 24 #if $stackoutput
18 --stackOutput 25 --stackOutput
19 #end if 26 #end if
20 27
21 --outputPath `pwd` 28 --outputPath '.'
22 --channel $channel 29 --channel $channel
23 --model $model 30 --model $model
24 --mean $mean 31 --mean $mean
25 --std $stdev 32 --std $stdev
26 --scalingFactor $scalingfactor; 33 --scalingFactor $scalingfactor
34 --outlier $outlier &&
27 35
28 ## Move files to different files for from_work_dir differentiation 36 ## Move files to different files for from_work_dir differentiation
29 #if $stackoutput 37 #if $stackoutput
30 mv *Probabilities*.tif Probabilities.tif; 38 mv *Probabilities*.tif Probabilities.tif &&
31 mv *Preview*.tif Preview.tif 39 mv qc/*Preview*.tif Preview.tif
32 #else 40 #else
33 mv *ContoursPM*.tif ContoursPM.tif; 41 mv *ContoursPM*.tif ContoursPM.tif &&
34 mv *NucleiPM*.tif NucleiPM.tif 42 mv *NucleiPM*.tif NucleiPM.tif
35 #end if 43 #end if
36 ]]></command> 44 ]]></command>
37 45
38 <inputs> 46 <inputs>
47 <param name="tool" type="select" label="UnMicst Tool">
48 <option selected="true" value="unmicst-solo">unmicst-solo</option>
49 <option value="unmicst-duo">unmicst-duo</option>
50 <option value="unmicst-legacy">unmicst-legacy</option>
51 <option value="UnMicstCyto2">UnMicstCyto2</option>
52 </param>
39 <param name="image" type="data" format="tiff" label="Registered TIFF"/> 53 <param name="image" type="data" format="tiff" label="Registered TIFF"/>
40 <param name="model" type="select" label="Model"> 54 <param name="model" type="select" label="Model">
41 <option value="nucleiDAPI">nucleiDAPI</option> 55 <option value="nucleiDAPI">nucleiDAPI</option>
56 <option value="CytoplasmIncell">CytoplasmIncell</option>
57 <option value="CytoplasmIncell2">CytoplasmIncell2</option>
58 <option value="CytoplasmZeissNikon">CytoplasmZeissNikon</option>
42 <option value="mousenucleiDAPI">mousenucleiDAPI</option> 59 <option value="mousenucleiDAPI">mousenucleiDAPI</option>
43 <option value="CytoplasmIncell">CytoplasmIncell</option> 60 <option value="nucleiDAPI1-5">nucleiDAPI1-5</option>
44 <option value="CytoplasmZeissNikon">CytoplasmZeissNikon</option> 61 <option value="nucleiDAPILAMIN">nucleiDAPILAMIN</option>
45 </param> 62 </param>
46 <param name="mean" type="float" value="-1" label="Mean (-1 for model default)"/> 63 <param name="mean" type="float" value="-1" label="Mean (-1 for model default)"/>
47 <param name="stdev" type="float" value="-1" label="Standard Deviation (-1 for model default)"/> 64 <param name="stdev" type="float" value="-1" label="Standard Deviation (-1 for model default)"/>
48 <param name="channel" type="integer" value="0" label="Channel to perform inference on"/> 65 <param name="channel" type="integer" value="0" label="Channel to perform inference on"/>
49 <param name="stackoutput" type="boolean" label="Stack probability map outputs"/> 66 <param name="stackoutput" type="boolean" label="Stack probability map outputs"/>
50 <param name="scalingfactor" type="float" value="1.0" label="Factor to scale by"/> 67 <param name="scalingfactor" type="float" value="1.0" label="Factor to scale by"/>
68 <param name="outlier" type="float" value="-1.0" label="Map percentile intensity to max when rescaling intensity values"/>
51 </inputs> 69 </inputs>
52 70
53 <outputs> 71 <outputs>
54 <data format="tiff" name="previews" from_work_dir="Preview.tif" label="${tool.name} on ${on_string}: Preview"> 72 <data format="tiff" name="previews" from_work_dir="Preview.tif" label="${tool.name} on ${on_string}: Preview">
55 <filter>stackoutput</filter> 73 <filter>stackoutput</filter>
62 </data> 80 </data>
63 <data format="tiff" name="nuclei" from_work_dir="NucleiPM.tif" label="${tool.name} on ${on_string}: NucleiPM"> 81 <data format="tiff" name="nuclei" from_work_dir="NucleiPM.tif" label="${tool.name} on ${on_string}: NucleiPM">
64 <filter>not stackoutput</filter> 82 <filter>not stackoutput</filter>
65 </data> 83 </data>
66 </outputs> 84 </outputs>
85 <tests>
86 <test expect_num_outputs="2">
87 <param name="image" value="105.tif" ftype="tiff" />
88 <param name="model" value="nucleiDAPI" />
89 <param name="tool" value="unmicst-legacy"/>
90 <param name="channel" value="1"/>
91 <output name="nuclei" file="105_NucleiPM_1.tif" compare="sim_size" delta="10" />
92 <output name="contours" file="105_ContoursPM_1.tif" compare="sim_size" delta="10" />
93 </test>
94 </tests>
67 <help><![CDATA[ 95 <help><![CDATA[
68 UnMicst - UNet Model for Identifying Cells and Segmenting Tissue 96 -------
69 Image Preprocessing 97 UNMICST
70 Images can be preprocessed by inferring nuclei contours via a pretrained UNet model. The model is trained on 3 classes : background, nuclei contours and nuclei centers. The resulting probability maps can then be loaded into any modular segmentation pipeline that may use (but not limited to) a marker controlled watershed algorithm. 98 -------
71 99
72 The only input file is: an .ome.tif or .tif (preferably flat field corrected, minimal saturated pixels, and in focus. The model is trained on images acquired at 20x with binning 2x2 or a pixel size of 0.65 microns/px. If your settings differ, you can upsample/downsample to some extent. 100 **UnMICST** uses a convolutional neural network to annotate each pixel with the probability that it belongs to a given subcellular component (nucleus, cytoplasm, cell boundary). Check the UnMICST website for the most up-to-date documentation.
73 101 **Input**
74 Running as a Docker container 102 An .ome.tif, preferably flat field corrected. The model is trained on images acquired at a pixelsize of 0.65 microns/px. If your settings differ, you can upsample/downsample to some extent.
75 103 **Output**
76 The docker image is distributed through Dockerhub and includes UnMicst with all of its dependencies. Parallel images with and without gpu support are available. 104 1. a .tif stack where the different probability maps for each class are concatenated in the Z-axis in the order: nuclei foreground, nuclei contours, and background.
77 105 2. a QC image with the DNA image concatenated with the nuclei contour probability map with suffix Preview
78 docker pull labsyspharm/unmicst:latest 106 More infortion available at: https://labsyspharm.github.io/UnMICST-info/.
79 docker pull labsyspharm/unmicst:latest-gpu
80 Instatiate a container and mount the input directory containing your image.
81
82 docker run -it --runtime=nvidia -v /path/to/data:/data labsyspharm/unmicst:latest-gpu bash
83 When using the CPU-only image, --runtime=nvidia can be omitted:
84
85 docker run -it -v /path/to/data:/data labsyspharm/unmicst:latest bash
86 UnMicst resides in the /app directory inside the container:
87
88 root@0ea0cdc46c8f:/# python app/UnMicst.py /data/input/my.tif --outputPath /data/results
89 Running in a Conda environment
90
91 If Docker is not available on your system, you can run the tool locally by creating a Conda environment. Ensure conda is installed on your system, then clone the repo and use conda.yml to create the environment.
92
93 git clone https://github.com/HMS-IDAC/UnMicst.git
94 cd UnMicst
95 conda env create -f conda.yml
96 conda activate unmicst
97 python UnMicst.py /path/to/input.tif --outputPath /path/to/results/directory
98 References:
99 S Saka, Y Wang, J Kishi, A Zhu, Y Zeng, W Xie, K Kirli, C Yapp, M Cicconet, BJ Beliveau, SW Lapan, S Yin, M Lin, E Boyde, PS Kaeser, G Pihan, GM Church, P Yin, Highly multiplexed in situ protein imaging with signal amplification by Immuno-SABER, Nat Biotechnology (accepted)
100
101 OHSU Wrapper Repo: https://github.com/ohsu-comp-bio/UnMicst
102 ]]></help> 107 ]]></help>
103 <expand macro="citations" /> 108 <expand macro="citations" />
104 </tool> 109 </tool>