Metadata-Version: 2.1
Name: actk
Version: 0.2.0
Summary: Automated Cell Toolkit
Home-page: https://github.com/AllenCellModeling/actk
Author: Jackson Maxfield Brown
Author-email: jacksonb@alleninstitute.org
License: Allen Institute Software License
Description: # actk
        
        [![Build Status](https://github.com/AllenCellModeling/actk/workflows/Build%20Master/badge.svg)](https://github.com/AllenCellModeling/actk/actions)
        [![Documentation](https://github.com/AllenCellModeling/actk/workflows/Documentation/badge.svg)](https://AllenCellModeling.github.io/actk)
        [![Code Coverage](https://codecov.io/gh/AllenCellModeling/actk/branch/master/graph/badge.svg)](https://codecov.io/gh/AllenCellModeling/actk)
        [![Published Data](https://img.shields.io/badge/Data-Published-Success)](https://open.quiltdata.com/b/allencell/tree/aics/actk/)
        
        Automated Cell Toolkit
        
        A pipeline to process field-of-view (FOV) microscopy images and generate data and
        render-ready products for the cells in each field. Of note, the data produced by this
        pipeline is used for the [Cell Feature Explorer](https://cfe.allencell.org/).
        
        ![workflow as an image](./images/header.png)
        
        ---
        
        ## Features
        All steps and functionality in this package can be run as single steps or all together
        by using the command line.
        
        In general, all commands for this package will follow the format:
        `actk {step} {command}`
        
        * `step` is the name of the step, such as "StandardizeFOVArray" or "SingleCellFeatures"
        * `command` is what you want that step to do, such as "run" or "push"
        
        Each step will check that the dataset provided contains the required fields prior to
        processing. For details and definitions on each field, see our
        [dataset fields documentation](https://AllenCellModeling.github.io/actk/dataset_fields.html).
        
        An example dataset can be seen [here](https://open.quiltdata.com/b/aics-modeling-packages-test-resources/tree/actk/test_data/data/example_dataset.csv).
        
        ### Pipeline
        To run the entire pipeline from start to finish you can simply run:
        
        ```bash
        actk all run --dataset {path to dataset}
        ```
        
        Step specific parameters can additionally be passed by simply appending them.
        For example: the step `SingleCellFeatures` has a parameter for
        `cell_ceiling_adjustment` and this can be set on both the individual step run level and
        also for the entire pipeline with:
        
        ```bash
        actk all run --dataset {path to dataset} --cell_ceiling_adjustment {integer}
        ```
        
        See the [steps module in our documentation](https://AllenCellModeling.github.io/actk/actk.steps.html)
        for a full list of parameters for each step
        
        #### Pipeline Config
        
        A configuration file can be provided to the underlying `datastep` library that manages
        the data storage and upload of the steps in this workflow.
        
        The config file should simply be called `workflow_config.json` and be available from
        whichever directory you run `actk` from. If this config is not found in the current
        working directory, defaults are selected by the `datastep` package.
        
        Here is an example of our production config:
        
        ```json
        {
            "quilt_storage_bucket": "s3://allencell",
            "project_local_staging_dir": "/allen/aics/modeling/jacksonb/results/actk"
        }
        ```
        
        You can even additionally attach step-specific configuration in this file by using the
        name of the step like so:
        
        ```json
        {
            "quilt_storage_bucket": "s3://example_config_7",
            "project_local_staging_dir": "example/config/7",
            "example": {
                "step_local_staging_dir": "example/step/local/staging/"
            }
        }
        ```
        
        #### AICS Distributed Computing
        
        For members of the AICS team, to run in distributed mode across the SLURM cluster add
        the `--distributed` flag to the pipeline call.
        
        To set distributed cluster and worker parameters you can additionally add the flags:
        * `--n_workers {int}` (i.e. `--n_workers 100`)
        * `--worker_cpu {int}` (i.e. `--worker_cpu 2`)
        * `--worker_mem {str}` (i.e. `--worker_mem 100GB`)
        
        ### Individual Steps
        * `actk standardizefovarray run --dataset {path to dataset}`, Generate standardized,
        ordered, and normalized FOV images as OME-Tiffs.
        * `actk singlecellfeatures run --dataset {path to dataset}`, Generate a features JSON
        file for each cell in the dataset.
        * `actk singlecellimages run --dataset {path to dataset}`, Generate bounded 3D images
        and 2D projections for each cell in the dataset.
        * `actk diagnosticsheets run --dataset {path to dataset}`, Generate diagnostic sheets
        for single cell images. Useful for quality control.
        
        ## Installation
        **Install Requires:** The python package, `numpy`, must be installed prior to the
        installation of this package: `pip install numpy`
        
        **Stable Release:** `pip install actk`<br>
        **Development Head:** `pip install git+https://github.com/AllenCellModeling/actk.git`
        
        ## Documentation
        For full package documentation please visit
        [allencellmodeling.github.io/actk](https://allencellmodeling.github.io/actk/index.html).
        
        ## Published Data
        
        For a large-scale example of what this library is capable of, please see the data
        produced by this pipeline after running our largest cell dataset through it. The data
        from the Allen Institute for Cell Science created from this pipeline can be found
        [here](https://open.quiltdata.com/b/allencell/tree/aics/actk/).
        
        This package contains the source microscopy images, segmentation files, pre-processed
        single cell images and features, and diagnostic sheets.
        
        Our source images are of endogenously-tagged hiPSC, grown for 4 days on Matrigel-coated
        96-well, glass bottom imaging plates. Each field of view (FOV) includes 4 channels (BF,
        EGFP, DNA, Cell membrane) collected either interwoven with one camera (workflow
        Pipeline 4.0 - 4.2) or simultaneously with two cameras (Workflow Pipeline 4.4). You can
        use the file metadata of each image to target the specific channel you are interested
        in. FOVs were either selected randomly (mode A), enriched for mitotic events (mode B)
        or sampling 3 different areas of a colony (edge, ridge, center) using a photo
        protective cocktail (mode C). The images cataloged in this dataset come in several
        flavors:
        
        * Field of view (FOV) images with channels* :
          * Brightfield
          * EGFP
          * DNA
          * Cell Membrane
        * Segmentation files with channels:
          * Nucleus Segmentation
          * Nucleus Contour
          * Membrane Segmentation
          * Membrane Contour
        
        _* Some FOV images contain seven channels rather than four. The extra three channels
        are "dummy" channels added during acquisition that can be ignored._
        
        The full details of the Allen Institute cell workflow are available on our website
        [here](https://www.allencell.org/methods-for-cells-in-the-lab.html).<br>
        The full details of the Allen Institute microscopy workflow are available on our
        website [here](https://www.allencell.org/methods-for-microscopy.html).
        
        The following is provided for each cell:
        * Cell Id
        * Cell Index (from within the FOV's segmentation)
        * Metadata (Cell line, Labeled protein name, segmented region index, gene, etc.)
        * 3D cell and nuclear segmentation, and, DNA, membrane, and structure channels
        * 2D max projects for dimension pairs (XY, ZX, and ZY) of the above 3D images
        * A whole bunch of features for each cell
        
        For the 3D single cell images the channel ordering is:
        * Segmented DNA
        * Segmented Membrane
        * DNA (Hoechst)
        * Membrane (CellMask)
        * Labeled Structure (GFP)
        * Transmitted Light
        
        To interact with this dataset please see the
        [Quilt Documentation](https://docs.quiltdata.com/).
        
        ## Development
        See
        [CONTRIBUTING.md](https://github.com/AllenCellModeling/actk/blob/master/CONTRIBUTING.md)
        for information related to developing the code.
        
        For more details on how this pipeline is constructed please see
        [cookiecutter-stepworkflow](https://github.com/AllenCellModeling/cookiecutter-stepworkflow)
        and [datastep](https://github.com/AllenCellModeling/datastep).
        
        To add new steps to this pipeline, run `make_new_step` and follow the instructions in
        [CONTRIBUTING.md](https://github.com/AllenCellModeling/actk/blob/master/CONTRIBUTING.md)
        
        ### Developer Installation
        The following two commands will install the package with dev dependencies in editable
        mode and download all resources required for testing.
        
        ```bash
        pip install -e .[dev]
        python scripts/download_test_data.py
        ```
        
        ### AICS Developer Instructions
        If you want to run this pipeline with the Pipeline Integrated Cell dataset
        (`pipeline 4.*`) run the following commands:
        
        ```bash
        pip install -e .[all]
        python scripts/download_aics_dataset.py
        ```
        
        Options for this script are available and can be viewed with:
        `python scripts/download_aics_dataset.py --help`
        
        ## Acknowledgments
        
        A previous iteration of this pipeline was created and managed by
        [Gregory Johnson](https://github.com/gregjohnso) for work with
        [PyTorch Integrated Cell](https://github.com/AllenCellModeling/pytorch_integrated_cell).
        
        This version of this pipeline is more generalized and while still used for the
        Integrated Cell model, can be used to pre-process a variety of microscopy image
        datasets.
        
        The previous version of this pipeline produced the
        [pipeline_integrated_single_cell dataset](https://open.quiltdata.com/b/allencell/tree/aics/pipeline_integrated_single_cell/).
        
        ***Free software: Allen Institute Software License***
        
Keywords: actk,computational biology,workflow,cell,microscopy
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: Free for non-commercial use
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: setup
Provides-Extra: test
Provides-Extra: dev
Provides-Extra: aics
Provides-Extra: all
