nf-core/spatialvi

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Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. It has to be a comma-separated file as described in the examples below and depends on the input data type. Use this parameter to specify its location.

--input '[path to samplesheet file]'

There are two types of samplesheets that the pipeline can handle: those specifying raw data (to be analysed by Space Ranger) and processed data (i.e. already analysed by Space Ranger). The workflow will automatically detect the samplesheet type and run the appropriate analysis steps. The two types of samplesheet are described in the following sections.

Raw spatial data

This section describes samplesheets for processing raw spatial data yet to be analysed with Space Ranger.

Here is an example of a typical samplesheet for analysing FFPE or fresh frozen (FF) data with bright field microscopy imagery:

sample,fastq_dir,image,slide,area
SAMPLE_1,fastqs_1/,hires_1.png,V11J26,B1
SAMPLE_2,fastqs_2/,hires_2.png,V11J26,B1

You may also supply a compressed tarball containing the FASTQ files in lieu of a directory path:

sample,fastq_dir,image,slide,area
SAMPLE_1,fastqs_1.tar.gz,hires_1.png,V11J26,B1
SAMPLE_2,fastqs_2.tar.gz,hires_2.png,V11J26,B1

For Cytassist samples, the image column gets replaced with the cytaimage column:

sample,fastq_dir,cytaimage,slide,area
SAMPLE_1,fastqs_1/,cytassist_1.tif,V11J26,B1
SAMPLE_2,fastqs_2/,cytassist_2.tif,V11J26,B1

Depending on the experimental setup, (additional) colour composite fluorescence images or dark background fluorescence images can be supplied using the colorizedimage or darkimage columns, respectively.

Please refer to the following table for an overview of all supported columns:

[!NOTE]

• You need to specify at least one of image, cytaimage, darkimage, colorizedimage. Most commonly, you’ll specify image for bright field microscopy data, or cytaimage for tissue scans generated with the 10x Cyatassist device. Please refer to the Space Ranger documentation, how multiple image types can be combined.
• The manual_alignment column is only required for samples for which a manual alignment file is needed and can be ignored if you’re using automatic alignment.

If you are unsure, please see the Visium documentation for details regarding the different variants of FASTQ directory structures and slide parameters appropriate for your samples.

Processed data

If your data has already been processed by Space Ranger and you are only interested in running downstream steps, the samplesheet looks as follows:

sample,spaceranger_dir
SAMPLE_1,results/SAMPLE_1/outs
SAMPLE_2,results/SAMPLE_2/outs

You may alternatively supply a compressed tarball containing the Space Ranger output:

sample,spaceranger_dir
SAMPLE_1,outs.tar.gz
SAMPLE_2,outs.tar.gz

The Space Ranger output directory is typically called outs and contains both gene expression matrices as well as spatial information.

Space Ranger

The pipeline exposes several of Space Ranger’s parameters when executing with raw spatial data. Space Ranger requires a lot of memory (64 GB) and several threads (8) to be able to run. You can find the Space Ranger documentation at the 10X website.

You are only able to run Space Ranger on the officially supported organisms: human and mouse. If you have already downloaded a reference you may supply the path to its directory (or another link from the 10X website above) using the --spaceranger_reference parameter, otherwise the pipeline will download the default human reference for you automatically.

appropriate probeset using the --spaceranger_probeset parameter Please refer to the Space Ranger Downloads page to obtain the correct probeset.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run \
    nf-core/spatialvi \
    --input <SAMPLESHEET> \
    --outdir <OUTDIR> \
    -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/spatialvi -profile docker -params-file params.yaml

with params.yaml containing:

input: '<SAMPLESHEET>'
outdir: '<OUTDIR>'
<...>

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

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/spatialvi

Reproducibility

It is a good idea to specify a 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/spatialvi 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.

To further assist in reproducibility, you can use share and re-use 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.

[!INFO] We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is partially supported. Please note that Conda is not at all supported for Space Ranger processing, and only supported on non-ARM64 architectures for analyses downstream of Space Ranger.

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 see if your system is available in these configs 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
• 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 steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (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 which container or conda environment a step of the pipeline uses 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.

Azure Resource Requests

To be used with the azurebatch profile by specifying the -profile azurebatch. We recommend providing a compute params.vm_type of Standard_D16_v3 VMs by default but these options can be changed if required.

Note that the choice of VM size depends on your quota and the overall workload during the analysis. For a thorough list, please refer the Azure Sizes for virtual machines in Azure.

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'