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annotate unmicst.xml @ 0:6bec4fef6b2e draft

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"planemo upload for repository https://github.com/ohsu-comp-bio/unmicst commit 73e4cae15f2d7cdc86719e77470eb00af4b6ebb7-dirty"
author perssond
date Fri, 12 Mar 2021 00:17:29 +0000
parents
children 74fe58ff55a5
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1 <tool id="unmicst" name="UnMicst" version="@VERSION@.1" profile="17.09">
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2 <description>UNet Model for Identifying Cells and Segmenting Tissue</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6
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7 <expand macro="requirements"/>
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8 @VERSION_CMD@
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10 <command detect_errors="exit_code"><![CDATA[
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11 #set $typeCorrected = str($image.name).replace('.ome.tiff','').replace('.ome.tif','').replace('.tiff','').replace('.tif','')+'.ome.tif'
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12
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13 ln -s $image '$typeCorrected';
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14
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15 @CMD_BEGIN@ '$typeCorrected'
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16
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17 #if $stackoutput
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18 --stackOutput
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19 #end if
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20
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21 --outputPath `pwd`
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22 --channel $channel
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23 --model $model
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24 --mean $mean
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25 --std $stdev
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26 --scalingFactor $scalingfactor;
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27
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28 ## Move files to different files for from_work_dir differentiation
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29 #if $stackoutput
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30 mv *Probabilities*.tif Probabilities.tif;
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31 mv *Preview*.tif Preview.tif
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32 #else
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33 mv *ContoursPM*.tif ContoursPM.tif;
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34 mv *NucleiPM*.tif NucleiPM.tif
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35 #end if
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36 ]]></command>
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37
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38 <inputs>
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39 <param name="image" type="data" format="tiff" label="Registered TIFF"/>
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40 <param name="model" type="select" label="Model">
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41 <option value="nucleiDAPI">nucleiDAPI</option>
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42 <option value="mousenucleiDAPI">mousenucleiDAPI</option>
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43 <option value="CytoplasmIncell">CytoplasmIncell</option>
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44 <option value="CytoplasmZeissNikon">CytoplasmZeissNikon</option>
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45 </param>
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46 <param name="mean" type="float" value="-1" label="Mean (-1 for model default)"/>
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47 <param name="stdev" type="float" value="-1" label="Standard Deviation (-1 for model default)"/>
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48 <param name="channel" type="integer" value="0" label="Channel to perform inference on"/>
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49 <param name="stackoutput" type="boolean" label="Stack probability map outputs"/>
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50 <param name="scalingfactor" type="float" value="1.0" label="Factor to scale by"/>
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51 </inputs>
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52
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53 <outputs>
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54 <data format="tiff" name="previews" from_work_dir="Preview.tif" label="${tool.name} on ${on_string}: Preview">
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55 <filter>stackoutput</filter>
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56 </data>
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57 <data format="tiff" name="probabilities" from_work_dir="Probabilities.tif" label="${tool.name} on ${on_string}: Probabilities">
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58 <filter>stackoutput</filter>
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59 </data>
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60 <data format="tiff" name="contours" from_work_dir="ContoursPM.tif" label="${tool.name} on ${on_string}: ContoursPM">
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61 <filter>not stackoutput</filter>
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62 </data>
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63 <data format="tiff" name="nuclei" from_work_dir="NucleiPM.tif" label="${tool.name} on ${on_string}: NucleiPM">
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64 <filter>not stackoutput</filter>
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65 </data>
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66 </outputs>
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67 <help><![CDATA[
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68 UnMicst - UNet Model for Identifying Cells and Segmenting Tissue
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69 Image Preprocessing
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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.
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71
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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.
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73
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74 Running as a Docker container
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75
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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.
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77
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78 docker pull labsyspharm/unmicst:latest
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79 docker pull labsyspharm/unmicst:latest-gpu
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80 Instatiate a container and mount the input directory containing your image.
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81
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82 docker run -it --runtime=nvidia -v /path/to/data:/data labsyspharm/unmicst:latest-gpu bash
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83 When using the CPU-only image, --runtime=nvidia can be omitted:
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84
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85 docker run -it -v /path/to/data:/data labsyspharm/unmicst:latest bash
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86 UnMicst resides in the /app directory inside the container:
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87
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88 root@0ea0cdc46c8f:/# python app/UnMicst.py /data/input/my.tif --outputPath /data/results
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89 Running in a Conda environment
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90
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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.
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92
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93 git clone https://github.com/HMS-IDAC/UnMicst.git
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94 cd UnMicst
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95 conda env create -f conda.yml
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96 conda activate unmicst
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97 python UnMicst.py /path/to/input.tif --outputPath /path/to/results/directory
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98 References:
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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)
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100
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101 OHSU Wrapper Repo: https://github.com/ohsu-comp-bio/UnMicst
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102 ]]></help>
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103 <expand macro="citations" />
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104 </tool>