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Introduction

nfcore/imcyto is a bioinformatics analysis pipeline used for Image Mass Cytometry analysis.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.

Pipeline summary

  1. Split mcd file by ROI, and save full and ilastik stacks separately based on specification in metadata.csv (imctools)
  2. Apply preprocessing filters to full stack tiff files (CellProfiler; --full_stack_cppipe parameter)
  3. Merge images from ilastik stack to obtain RGB image of cell nuclei and membranes to generate a composite tiff (CellProfiler; --ilastik_stack_cppipe parameter)
  4. Use composite tiff to classify pixels as membrane, nuclei or background, and save probabilities map as tiff (Ilastik; --ilastik_training_ilp parameter; optional)
  5. Use probability tiffs and preprocessed full stack tiffs for single cell segmentation to generate a cell mask as tiff and then overlay cell mask onto full stack tiff images to extract single cell information generating a csv file (CellProfiler; --segmentation_cppipe parameter)

Quick Start

i. Install nextflow

ii. Install one of docker or singularity

iii. Download the pipeline and test it on a minimal dataset with a single command

nextflow run nf-core/imcyto -profile test,<docker/singularity>

iv. Start running your own analysis!

nextflow run nf-core/imcyto \
    --input "./mcd/*.mcd" \
    --metadata 'metadata.csv' \
    --full_stack_cppipe './plugins/full_stack_preprocessing.cppipe' \
    --ilastik_stack_cppipe './plugins/ilastik_stack_preprocessing.cppipe' \
    --segmentation_cppipe './plugins/segmentation.cppipe' \
    --ilastik_training_ilp './plugins/ilastik_training_params.ilp' \
    -profile <docker/singularity>

See usage docs for all of the available options when running the pipeline.

Documentation

The nf-core/imcyto pipeline comes with documentation about the pipeline, found in the docs/ directory:

  1. Installation
  2. Pipeline configuration
  3. Running the pipeline
  4. Output and how to interpret the results
  5. Troubleshooting

Credits

The pipeline was originally written by The Bioinformatics & Biostatistics Group for use at The Francis Crick Institute, London.

The pipeline was developed by Harshil Patel and Nourdine Bah in collaboration with Karishma Valand, Febe van Maldegem, Emma Colliver and Mihaela Angelova.

Many thanks to others who contributed as a result of the Crick Data Challenge (Jan 2019) - Gavin Kelly, Becky Saunders, Katey Enfield, Alix Lemarois, Nuria Folguera Blasco, Andre Altmann.

It would not have been possible to develop this pipeline without the guidelines, scripts and plugins provided by the Bodenmiller Lab. Thank you too!

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on Slack (you can join with this invite).

Citation

You can cite the nf-core pre-print as follows:

Ewels PA, Peltzer A, Fillinger S, Alneberg JA, Patel H, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. nf-core: Community curated bioinformatics pipelines. bioRxiv. 2019. p. 610741. doi: 10.1101/610741.