Updating workflows/imaging/tissue-microarray-analysis from 0.1 to 0.2

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

@@ -1,5 +1,10 @@
 # Changelog
 
+## [0.2] - 2025-04-14
+
+### Automatic update
+- `toolshed.g2.bx.psu.edu/repos/perssond/coreograph/unet_coreograph/2.2.8+galaxy1` was updated to `toolshed.g2.bx.psu.edu/repos/perssond/coreograph/unet_coreograph/2.2.8.3`
+
 ## [0.1] - 2024-04-12
 
 - Initial release of Tissue Microarray Analysis Workflow

workflows/imaging/tissue-microarray-analysis/tissue-micro-array-analysis.ga

@@ -12,8 +12,6 @@
     "format-version": "0.1",
     "license": "MIT",
     "name": "End-to-End Tissue Microarray Analysis",
-    "readme": "# End-to-End Tissue Microarray Image Analysis with Galaxy-ME\n\n## Input datasets\n\n- Collection of raw cycle images (TIFF/OME-TIFF): Ensure that the list is ordered in cycle order (ex: cycle_1.tiff, cycle_2.tiff, etc.)\n- Markers file (CSV): A comma-separated file with `marker_names` in the third column\n\n    - Example markers file:\n\n```\nround,channel,marker_name\n0,0,DAPI_1\n0,1,CD3\n0,2,CD45\n0,3,CD8\n1,4,DAPI_2\n1,5,PANCK\n1,6,SMA\n1,7,ECAD\n...\n```\n\n\n- Phenotype file (CSV): A comma-separated Scimap phenotyping file that maps hierarchical cell phenotypes to markers\n\n    - For an example phenotype workflow, 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/).\n\n\n## Input values\n\nAll 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: \n\n- Channel used as a reference for registration (ASHLAR): `0`\n- Channel used for nuclear segmentation (Mesmer): `0`\n- Image resolution (microns per pixel): `0.65`\n\nThe workflow should be imported and edited if these values are not suitable for your datasets. \n\n## Processing\n\nFor 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)\n\n- Tile-to-tile illumination differences are corrected in the unstitched input raw cycle images using **Basic Illumination**\n- A whole-slide OME-TIFF image is generated via stitching and registration with **ASHLAR**. Channel names are assigned at this step using the input markers file\n- TMA cores are segmented and cropped into individual images, producing a collection of TIFFs using **UNetCoreograph**. All subsequent steps are run as batch processing across the collection of core datasets\n- The output of **UNetCoreograph** is a generic TIFF, and must be converted back to OME-TIFF using the **Convert Image** tool, and channels can be renamed using the **Rename OME-TIFF channels** utility\n- Nuclear segmentation is performed using **Mesmer**, producing a nuclear mask in TIFF format for each core image\n- 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)\n- The quantification table is converted to anndata format (h5ad), a common datatype used by most single-cell and spatial analysis packages\n- Automated cell phenotyping is performed using **Scimap** (see *Warning* section about GMM-based phenotyping)\n- Finally, **Vitessce** dashboards combine interactive image viewing with linked single-cell analysis components to allow for integrated initial data exploration\n\n## Warning\n\nIn this workflow, we perform automated GMM-based cell phenotyping using Scimap. The Scimap tool also accepts manual gates, which can be determined using the **GateFinder** tool. This method is highly recommended, as **most** markers are not well suited for GMM-based thresholding. The automated GMM-based thresholding can work well for highly abundant markers that show a strong bimodal distribution; otherwise, it should be used primarily as a means of generating an initial starting point for gating and cell phenotyping. \n\nFor more warnings and context, see our tutorial linked above. \n\n\n## Tool developers' documentation\n\n- [MCMICRO](https://mcmicro.org/)\n    - Basic Illumination\n    - ASHLAR\n    - UNetCoreograph\n    - MCQuant\n- [Mesmer](https://deepcell.readthedocs.io/en/master/)\n- [Scimap](https://scimap-doc.readthedocs.io/en/latest/)\n- [Vitessce](https://vitessce.io/)\n\n\n## Tool references\n\n- Peng, T., K. Thorn, T. Schroeder, L. Wang, F. J. Theis et al., 2017 A BaSiC tool for background and shading correction of optical microscopy images. Nature Communications 8: 10.1038/ncomms14836\n- Wolf, F. A., P. Angerer, and F. J. Theis, 2018 SCANPY: large-scale single-cell gene expression data analysis. Genome Biology 19: 10.1186/s13059-017-1382-0\n- Stringer, C., T. Wang, M. Michaelos, and M. Pachitariu, 2020 Cellpose: a generalist algorithm for cellular segmentation. Nature Methods 18: 100\u2013106. 10.1038/s41592-020-01018-x\n- 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\u2013565. 10.1038/s41587-021-01094-0\n- 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\u2013315. 10.1038/s41592-021-01308-y\nVirshup, I., S. Rybakov, F. J. Theis, P. Angerer, and F. A. Wolf, 2021 anndata: Annotated data. 10.1101/2021.12.16.473007\n- Muhlich, J. L., Y.-A. Chen, C. Yapp, D. Russell, S. Santagata et al., 2022 Stitching and registering highly multiplexed whole-slide images of tissues and tumors using ASHLAR (A. Valencia, Ed.). Bioinformatics 38: 4613\u20134621. 10.1093/bioinformatics/btac544\n- 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\u2013178. 10.1038/s41592-021-01358-2\n- Yapp, C., E. Novikov, W.-D. Jang, T. Vallius, Y.-A. Chen et al., 2022 UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues. Communications Biology 5: 10.1038/s42003-022-04076-3\n- Zhang, W., I. Li, N. E. Reticker-Flynn, Z. Good, S. Chang et al., 2022 Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA. Nature Methods 19: 759\u2013769. 10.1038/s41592-022-01498-z\n- 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",
-    "release": "0.1",
     "report": {
         "markdown": "# Workflow Execution Report\n\n## Workflow Inputs\n\n```galaxy\ninvocation_inputs()\n```\n\n## Workflow Outputs\n\n```galaxy\ninvocation_outputs()\n```\n\n## Workflow\n\n```galaxy\nworkflow_display()\n```\n\n```visualization\n{\n    \"dataset_label\": {\n        \"invocation_id\": \"\",\n        \"output\": \"Vitessce Dashboard Config\"\n    },\n    \"visualization_name\": \"vitessce\",\n    \"visualization_title\": \"Vitessce\"\n}\n```\n\n"
     },
@@ -661,6 +659,7 @@
     "tags": [
         "imaging"
     ],
-    "uuid": "c880e149-6237-46b6-aac6-7e1e3bb58ede",
-    "version": 7
-}
+    "uuid": "02f4cd57-3e49-4100-bad7-7af18867b68f",
+    "version": 1,
+    "release": "0.2"
+}