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‎workflows/imaging/tissue-microarray-analysis/multiplex-tissue-microarray-analysis/.dockstore.yml‎

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version: 1.2
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workflows:
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- name: main
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subclass: Galaxy
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publish: true
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primaryDescriptorPath: /multiplex-tma.ga
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testParameterFiles:
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- /multiplex-tma-tests.yml
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authors:
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- name: Cameron Watson
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orcid: 0000-0002-6942-2469

‎workflows/imaging/tissue-microarray-analysis/multiplex-tissue-microarray-analysis/CHANGELOG.md‎

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# Changelog
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## [0.1] - 2025-06-21
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- Initial release of Multiplex Tissue Microarray Analysis Workflow

‎workflows/imaging/tissue-microarray-analysis/multiplex-tissue-microarray-analysis/README.md‎

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# Multiplex tissue image processing and analysis with Galaxy-ME
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Use this workflow when you have pre-registered OME-TIFF images that are ready for analysis (no preprocessing needed).
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## Overview
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This workflow processing and analyzes multiple multiplex tissue imaging datasets by
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1. Performing **background subtraction, nuclear segmentation, and feature extraction**
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2. Performing hierarchical, GMM-based **cell phenotyping**
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3. Analyzing **multi-sample cell type composition**
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4. Quantifying the **spatial arrangment of cell types** in the tissues
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5. Exploring the original images with all downstream associated data using **interactive dashboards**
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## Input datasets
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- Collection of registered OME-TIFF images
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- Markers file (CSV) for background subtraction
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- Example markers file:
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```
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marker_name,background,exposure,remove
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DNA_1,,,
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Control_A488,,,TRUE
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Control_A555,,,TRUE
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Control_A647,,,TRUE
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DNA_2,,,TRUE
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RNA_Pol_II_CTD,Control_A488,,
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pERK,Control_A555,,
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p53,Control_A647,,
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...
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```
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- Phenotype workflow and manual gate files (CSV): A comma-separated Scimap phenotyping file that maps hierarchical cell phenotypes to markers, and a manual gates file that maps markers to manually-determined thresholds
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- For examples, see our [tutorial](https://training.galaxyproject.org/training-material/topics/imaging/tutorials/multiplex-tissue-imaging-TMA/tutorial.html) and the [Scimap documentation](https://scimap-doc.readthedocs.io/en/latest/tutorials/scimap-tutorial-cell-phenotyping/).
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## Inputs values
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All input values have been preset in the workflow and are optimized for cyclic immunofluorescence images captured using a Rarecyte slide scanner. Some important assumptions are made:
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- Channel used for nuclear segmentation (Mesmer): `0`
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- Image resolution (microns per pixel): `0.65`
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The workflow should be imported and edited if these values are not suitable for your datasets.
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## Processing
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For more detailed information, see our [tutorial on the Galaxy Training Network](https://training.galaxyproject.org/training-material/topics/imaging/tutorials/multiplex-tissue-imaging-TMA/tutorial.html)
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- Nuclear segmentation is performed using **Mesmer**, producing a nuclear mask in TIFF format for each core image
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- Cell/nuclear features (mean marker intensities, spatial coordinates, and morphological measurements) are quantified using **MCQUANT**, producing a CSV table of cells (rows) x features (columns)
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- The quantification table is converted to anndata format (h5ad), a common datatype used by most single-cell and spatial analysis packages
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- Automated cell phenotyping is performed using **Scimap** (see *Warning* section about GMM-based phenotyping)
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- **Scimap** and **Squidpy** are used for spatial analysis
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- Finally, **Vitessce** dashboards combine interactive image viewing with linked single-cell analysis components to allow for integrated initial data exploration
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## Tool developers' documentation
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- [MCMICRO](https://mcmicro.org/)
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- Basic Illumination
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- ASHLAR
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- UNetCoreograph
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- MCQuant
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- [Mesmer](https://deepcell.readthedocs.io/en/master/)
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- [Scimap](https://scimap-doc.readthedocs.io/en/latest/)
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- [Vitessce](https://vitessce.io/)
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## Tool references
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- Greenwald, N. F., G. Miller, E. Moen, A. Kong, A. Kagel et al., 2021 Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nature Biotechnology 40: 555–565. 10.1038/s41587-021-01094-0
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- Schapiro, D., A. Sokolov, C. Yapp, Y.-A. Chen, J. L. Muhlich et al., 2021 MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nature Methods 19: 311–315. 10.1038/s41592-021-01308-y
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- Virshup, I., S. Rybakov, F. J. Theis, P. Angerer, and F. A. Wolf, 2021 anndata: Annotated data. 10.1101/2021.12.16.473007
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- Palla, G., H. Spitzer, M. Klein, D. Fischer, A. C. Schaar et al., 2022 Squidpy: a scalable framework for spatial omics analysis. Nature Methods 19: 171–178. 10.1038/s41592-021-01358-2
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- Nirmal, A. J., and P. K. Sorger, 2024 SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data. Journal of Open Source Software 9: 6604. 10.21105/joss.06604

‎workflows/imaging/tissue-microarray-analysis/multiplex-tissue-microarray-analysis/multiplex-tma-tests.yml‎

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- doc: Test outline for GBCC-TMA-L2-workflow
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job:
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Markers File:
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class: File
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path: test-data/Markers File.csv
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filetype: csv
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Manual Gates:
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class: File
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path: test-data/Manual Gates.csv
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filetype: csv
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Phenotype workflow:
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class: File
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path: test-data/Phenotype workflow.csv
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filetype: csv
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Registered Images:
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class: Collection
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collection_type: list
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elements:
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- class: File
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identifier: image
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location: https://zenodo.org/records/15710828/files/Registered%20Images_registered%20image.ome.tiff?download=1
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filetype: ome.tiff
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outputs:
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Spatial Scatterplot Montage:
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asserts:
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- has_size:
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size: 181K
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delta: 50K
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Spatial Interaction Montage:
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asserts:
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- has_size:
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size: 39K
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delta: 20K
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Merged anndata:
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asserts:
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- has_h5_keys:
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keys: obs/Area,obs/CellID,obs/Eccentricity,obs/Extent,obs/MajorAxisLength,obs/MinorAxisLength,obs/Orientation,obs/Solidity,obs/X_centroid,obs/Y_centroid,obs/imageid,obs/imageid/categories,obs/imageid/codes
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Multisample barplot:
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asserts:
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- has_size:
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size: 60K
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delta: 30K
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Background subtracted images:
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element_tests:
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image:
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asserts:
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- has_image_channels:
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channels: 7
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Background subtracted markers:
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element_tests:
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image:
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asserts:
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- has_line:
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line: marker_name,background,exposure
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- has_line:
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line: DNA_7,,
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Primary Mask Quantification:
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element_tests:
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image:
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asserts:
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- has_line:
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line: CellID,DNA_7,CD11B,SMA,CD16,ECAD,FOXP3,NCAM,X_centroid,Y_centroid,Area,MajorAxisLength,MinorAxisLength,Eccentricity,Solidity,Extent,Orientation
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Segmented Multiplexed Mask:
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element_tests:
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image:
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asserts:
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- has_image_channels:
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channels: 1
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phenotyped adata:
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element_tests:
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image:
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asserts:
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- has_h5_keys:
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keys: obs/Area,obs/CellID,obs/Eccentricity,obs/Extent,obs/MajorAxisLength,obs/MinorAxisLength,obs/Orientation,obs/Solidity,obs/X_centroid,obs/Y_centroid,obs/imageid,obs/imageid/categories,obs/imageid/codes
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Interaction Matrix Plot:
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element_tests:
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image:
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asserts:
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- has_size:
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size: 43K
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delta: 20K
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Interaction Matrix Anndata:
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element_tests:
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image:
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asserts:
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- has_h5_keys:
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keys: obs/Area,obs/CellID,obs/Eccentricity,obs/Extent,obs/MajorAxisLength,obs/MinorAxisLength,obs/Orientation,obs/Solidity,obs/X_centroid,obs/Y_centroid,obs/imageid,obs/imageid/categories,obs/imageid/codes
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Squidpy Spatial Scatterplots:
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element_tests:
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image:
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asserts:
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- has_size:
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size: 181K
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delta: 50K
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Vitessce Dashboard:
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element_tests:
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image:
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asserts:
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- has_json_property_with_text:
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property: version
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text: "1.0.17"

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