Metadata-Version: 2.1
Name: imcpipeline
Version: 0.0.3
Summary: A pipeline and utils for IMC data analysis.
Home-page: https://github.com/elementolab/imcpipeline
Author: Andre Rendeiro
Author-email: andre.rendeiro@pm.me
License: GPL3
Project-URL: Bug Tracker, https://github.com/elementolab/imcpipeline/issues
Project-URL: Documentation, https://github.com/elementolab/imcpipeline/README.md
Project-URL: Source Code, https://github.com/elementolab/imcpipeline
Description: ![imcpipeline logo](https://raw.githubusercontent.com/elementolab/imcpipeline/master/logo.png)
        
        # Imaging mass cytometry pipeline [![Build Status](https://travis-ci.org/ElementoLab/imcpipeline.svg?branch=master)](https://travis-ci.org/ElementoLab/imcpipeline)
        
        This is a pipeline for the processing of imaging mass cytometry (IMC) data.
        
        It is largely based on [Vito Zanotelli's pipeline](https://github.com/BodenmillerGroup/ImcSegmentationPipeline).
        It performs image preprocessing and filtering, uses
        [`ilastik`](https://www.ilastik.org/) for semi-supervised pixel classification,
        [`CellProfiler`](https://cellprofiler.org/) for image segmentation and
        quantification of single cells.
        
        The pipeline can be used in standalone mode or with `imcrunner` in order to
        process multiple samples in a distributed way and in parallel such as a local
        computer, on the cloud, or a high performance computing cluster (HPC).
        This is due to the use of the light-weight computing configuration manager
        [divvy](https://github.com/pepkit/divvy).
        
        ## Requirements and installation
        
        Requires:
        
        - Python >= 3.7
        - One of: `docker`, `singularity`, `conda` or `cellprofiler` in a local installation.
        
        Install with:
        
        ```bash
        pip install imcpipeline
        ```
        
        Make sure to have an updated PIP version.
        Development and testing is only done for Linux. If anyone is interested in
        maintaining this repository in MacOS/Windows fell free to submit a PR.
        
        ## Quick start
        
        ### Demo
        
        You can run a demo dataset using the ``--demo`` flag:
        
        ```
        imcpipeline --demo
        ```
        
        The pipeline will try to use a local `cellprofiler` installation, `docker` or
        `singularity` in that order if any is available.
        Output files are in a `imcpipeline_demo_data` directory.
        
        ### Running on your data
        
        To run the pipeline on real data, one simply needs to specify input and output
        directories. A trained `ilastik` model can be provided and if not, the user will
        be prompted to train it.
        
        ```
        imcpipeline \
            --container docker \
            --ilastik-model model.ilp \
            -i input_dir -o output_dir
        ```
        
        If `docker` or `singularity` is not available, one could for example use a
        `conda` environment or a `virtualenv` environment activated only for the
        `cellprofiler` command like this:
        
        ```
        imcpipeline \
            --cellprofiler-exec \
                "source ~/.miniconda2/bin/activate && conda activate cellprofiler && cellprofiler"
            --ilastik-model model.ilp \
            -i input_dir -o output_dir
        ```
        
        To run one step only for a single sample, use the `-s/--step` argument:
        ```
        imcpipeline \
            --step segmentation \
            -i input_dir -o output_dir
        ```
        
        To run the pipeline for various samples in a specific computing configuration
        ([more details in the documentation](docs.md)):
        
        ```
        imcrunner \
            --divvy-configuration slurm \
            metadata.csv \
                --container docker \
                --ilastik-model model.ilp \
                -i input_dir -o output_dir
        ```
        
        ## Documentation
        
        For additional details on the pipeline, [see the documentation](docs.md).
        
        ## Related software
        
         - [Vito Zanotelli's pipeline](https://github.com/BodenmillerGroup/ImcSegmentationPipeline);
         - A similar pipeline implemented in [Nextflow](https://github.com/nf-core/imcyto).
        
Keywords: computational biology,bioinformatics,imaging,mass cytometry,mass spectrometry
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Development Status :: 3 - Alpha
Classifier: Typing :: Typed
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Description-Content-Type: text/markdown
Provides-Extra: dev
