nfcore/imcyto is a bioinformatics analysis pipeline used for image segmentation and extraction of single cell expression data. This pipeline was generated for Imaging Mass Cytometry data, however, it is flexible enough to be applicable to other types of imaging data e.g. immunofluorescence/immunohistochemistry data.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Pipeline summary

  1. Split image acquisition output files (mcd, ome.tiff or txt) by ROI and convert to individual tiff files for channels with names matching those defined in user-provided metadata.csv file. Full and ilastik stacks will be generated separately for all channels being analysed in single cell expression analysis, and for channels being used to generate the cell mask, respectively (imctools).

  2. Apply pre-processing filters to full stack tiff files (CellProfiler).

  3. Use selected tiff files in ilastik stack to generate a composite RGB image representative of the plasma membranes and nuclei of all cells (CellProfiler).

  4. Use composite cell map to apply pixel classification for membranes, nuclei or background, and save probabilities map as tiff (Ilastik). If CellProfiler modules alone are deemed sufficient to achieve a reliable segmentation mask this step can be bypassed using the --skip_ilastik parameter in which case the composite tiff generated in step 3 will be used in subsequent steps instead.

  5. Use probability/composite tiff and pre-processed full stack tiff for segmentation to generate a single cell mask as tiff, and subsequently overlay cell mask onto full stack tiff to generate single cell expression data in csv file (CellProfiler).

Quick Start

  1. Install Nextflow (>=21.10.3)

  2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

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

    nextflow run nf-core/imcyto -profile test,YOURPROFILE --outdir <OUTDIR>

    Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

    • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
    • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
    • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
    • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.
  4. Start running your own analysis!

    nextflow run nf-core/imcyto \
       --input "./inputs/*.mcd" \
       --outdir <OUTDIR> \
       --metadata './inputs/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' \
       --plugins_dir './plugins/cp_plugins/' \
       -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>


The nf-core/imcyto pipeline comes with documentation about the pipeline usage, parameters and output.


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 the Slack #imcyto channel (you can join with this invite).


If you use nf-core/imcyto for your analysis, please cite it using the following doi: 10.5281/zenodo.3865430

An extensive list of references for the tools used by the pipeline can be found in the file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.