nf-core/mcmicro

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Introduction

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. We currently accept 2 formats for the input samplesheets. One format is one row per sample and the other is one row per sample per cycle. Use the parameter input_sample for one row per sample or the parameter input_cycle for one row per sample per cycle, to specify its location. It has to be a comma-separated file with a header row and either two (input_sample) or four (input_cycle) columns as shown in the examples below.

--input_cycle '[path to one row per sample per cycle samplesheet file]'

OR

--input_sample '[path to one row per sample samplesheet file]'

Samplesheet with one row per sample per cycle

The sample identifier must be the same for multiple cycles of the same sample. All the files from the same sample will be run in a single run of ashlar in the cycle order that they appear in the samplesheet. If illumination correction is requested using basicpy, each cycle will be corrected separately.

sample,cycle_number,channel_count,image_tiles
TEST1,1,10,/path/to/image/cycif-tonsil-cycle1.ome.tif
TEST1,2,10,/path/to/image/cycif-tonsil-cycle2.ome.tif
TEST1,3,10,/path/to/image/cycif-tonsil-cycle3.ome.tif

An example one row per sample per cycle samplesheet has been provided with the pipeline.

Samplesheet with one row per sample

This is similar to the above case except each row just contains a column for each sample name and a columnn containing a directory where all the files for a given sample are located. All per-cycle image files in the image_directory for a given sample will be run in a single run of ashlar. If illumination correction is requested using basicpy, each cycle will be corrected separately.

sample,image_directory
TEST1,/path/to/image/directory

An example one row per sample samplesheet has been provided with the pipeline.

Markersheet input

Each row of the markersheet represents a single channel in the associated sample image. The columns channel_number, cycle_number and marker_name are required.

channel_number,cycle_number,marker_name
1,1,DNA 1
2,1,Na/K ATPase
3,1,CD3
4,1,CD45RO

optional markersheet columns

Running the pipeline

One row per sample per cycle

The typical command for running the one row per sample per cycle pipeline is as follows:

nextflow run nf-core/mcmicro --input_cycle ./samplesheet_cycle.csv --outdir ./results --marker_sheet markers.csv -profile docker

One row per sample

The typical command for running the one row per sample pipeline is as follows:

nextflow run nf-core/mcmicro --input_sample ./samplesheet_sample.csv --outdir ./results --marker_sheet markers.csv -profile docker

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/mcmicro -profile docker -params-file params.yaml

with:

input_cycle: "samplesheet_cycle.csv"
outdir: "./output"
marker_sheet: "markers.csv"

You can also generate such YAML/JSON files via nf-core/launch.

Pipeline stages and associated input parameters

Illumination Correction

Illumination correction can optionally be performed before registration. It is triggered by the --illumination flag which can currently only be followed by the option basicpy. We plan on supporting other modules for illumination correction in the future. When basicpy is selected the nf-core module basicpy is run on the input image(s). Basicpy is a python package for background and shading correction of optical microscopy images. More information about it can be found on the basicpy nf-core module website.

Registration

Registration is a required step of the pipeline and the only module currently supported is ashlar. Ashlar is a software package that combines multi-tile microscopy images into a high-dimensional mosaic image. More information about ashlar can be found on the ashlar website. We plan to support other modules for registration in the future.

Background Subtraction

This is an optional step that occurs immediately following registration. It is triggered by the --backsub flag. When this flag is selected, the module backsub is run on the output from the registration step. The backsub module performs pixel-by-pixel channel subtraction scaled by exposure times of pre-stitched tif images. More information about it can be found on the backsub nf-core module website.

TMA Core Separation

This is an optional step that occurs immediately following background subtration if that optional step was run or after registration if is was not. It is triggered by the --tma_dearray flag. When this flag is selected, the coreograph module is run on the output from either the background subtraction step or the registration step if background subtration was not performed. Coreograph separates the input image into a set of images for each of the cores. It uses UNet, a deep learning model, to identify complete/incomplete tissue cores on a tissue microarray. It has been trained on 9 TMA slides of different sizes and tissue types. More information about it can be found on the coreograph nf-core module website

Segmentation

This is a required step that follows the TMA Core Separation step. The workflow will run the deepcell_mesmer module by default, but other options are available by using the --segmentation flag. The flag should be followed by a single segmentation module name or a comma separated list of names to run multiple segmentation modules in parallel. The available options currently supported are mesmer and cellpose. More information about each of these modules can be found on their respective nf-core module websites: deepcell_mesmer cellpose

When cellpose is selected as a segmentation method you may also provide a pretrained model to the cellpose module by using the --cellpose_model flag followed by a full path or URL to the model file.

Quantification

This is a required step that follows segmentation. The workflow currently runs the mcquant module by default. Other quantification modules will be added as options in the future. Mcquant extracts single-cell data given a multi-channel image and a segmentation mask. More information about mcquant can be found on the mcquant nf-core module website.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you’re running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull nf-core/mcmicro

Reproducibility

It is a good idea to specify the pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you’ll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the nf-core/mcmicro releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you’ll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in reproducibility, you can use share and reuse parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Core Nextflow arguments

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to check if your system is supported, please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended, since it can lead to different results on different machines dependent on the computer environment.

• test
  • A profile with a complete configuration for automated testing
  • Includes links to test data so needs no other parameters
• docker
  • A generic configuration profile to be used with Docker
• singularity
  • A generic configuration profile to be used with Singularity
• podman
  • A generic configuration profile to be used with Podman
• shifter
  • A generic configuration profile to be used with Shifter
• charliecloud
  • A generic configuration profile to be used with Charliecloud
• apptainer
  • A generic configuration profile to be used with Apptainer
• wave
  • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
• conda
  • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

-resume

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps for a particular tool. By default, nf-core pipelines use containers and software from the biocontainers or bioconda projects. However, in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'