Package: infer-subc
infer_subcAbout The Projectinfer_subcaims to create a simple, extensible, and reproducible workflow to measure (or infer) the shape, position, size, and interaction of several sub-cellular components. These data can then be applied later to better understand the spatial coordination of these structures and the interactome during key biological processes.is part of a larger collaboration between the CZI Neurodegeneration Challenge Network(NDCN)Data Science Concierge program and the Cohen lab at UNC(website,github)to migrate a multispectral imaging dataset of iPSCs which identifies sub-cellular components to a scalable cloud-based pipeline.infer_subcWorkflowThe staring point of this workflow is a set of multichannel images, where each channel labels a different sub-cellular component. The workflow can then be completed in asuggestedseries of steps, outlined in the notebooks below.Identify a single cell of interestInfer cellmask(🚨 Steps 2-9 depend on establishing a good solution here)Infer nucleiInfer cytoplasmSegment each of the organellesInfer lysosomesInfer mitochondriaInfer golgi complexInfer peroxisomesInfer endoplasmic reticulumInfer lipid bodiesBuilt WithA quick note on tools and resources used.napari-allencell-segmenter-- We are leveraging the framework of thenapari-allencell-segmenterplugin, which enables powerful 3D image segmentation while taking advantage of thenaparigraphical user interface.aicssegmentation-- We call theaicssegmentationpackage directly.napari-- Used as the visualization framework, a fast, interactive, multi-domensional image viewer for Python.scipy-- Image analysisscikit-image-- Image analysisitk-- Image analysisnumpy-- Under the hood computationAlzheimer's Disease AD Workbench-- We initially wanted to use the ADDI's ADWB as a method of data sharing and to serve as a computational resource.Getting StartedPrerequisitesInstallationinfer_subcis  available onPyPIvia:pip install infer_subcIf there are issues more details can be fouund in thedocumentationUsage - quick startIts recommended that you use this repo along with theorganelle-segmenter-pluginas inOption Abelow.  Alternatively using the module functions directly as inOption Bwould work just fine.Option A: Napariorganelle-segmenter-pluginOpen a file in napari by dragging multi-channel .czi file onto napari which will import a multi-channel, multi-Z 'layer'. (Using the menu's defaults toaicsIMAGEIOreader which automatically splits mutliple channels into individual layers.  The plugin is able to support multi-dimensional data in .tiff, .tif. ome.tif, .ome.tiff, .czi)Start the plugin (open napari, go to "Plugins" --> "organelle-segmenter-plugin" --> "workflow editor")Select the image and channel to work onSelect a workflow based on the example image and target segmentation based on user's data. Ideally, it is recommend to start with the example with very similar morphology as user's data.Click "Run All" to execute the whole workflow on the sample data.Adjust the parameters of steps, based on the intermediate results.  A complete list of all functions can be foundhere🚧 WIP 🚧Click "Run All" again after adjusting the parameters and repeat step 6 and 7 until the result is satisfactory.Save the workflowClose the plugin and open thebatch processingpart by (go to "Plugins" --> "organelle-segmenter-plugin" --> "batch processing")Load the customized workflow saved aboveLoad the folder with all the images to processClick "Run"Follow theexamplesin theinfer_subcrepofor postprocessing of the saved segmentations and generating the statistics.Option B: python script or notebookA variety of examplenotebooksdemonstrating how to use the are available in the repo.  Additional information can be found athttps://ndcn.github.io/infer-subc/nbs/overview/.DevelopmentRead theCONTRIBUTING.mdfile.LicenseDistributed under the terms of the [BSD-3] license,
"organelle-segmenter-plugin" is free and open source softwareIssuesIf you encounter any problems, please file an issue with a detailed description.ADWB hintsGiven that the github repos are not yet whitelisted, the source directory needs to be zipped and uploaded in order to make an "editable" pip install.uploading guideuploading files via the workspace article.Using BLOB storage