nf-core/scdownstream

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Filtered and unfiltered matrices

Throughout this documentation, you will find references to filtered and unfiltered matrices. The unfiltered matrices are matrices which still contain empty droplets, whereas the filtered matrices have been filtered for empty droplets. A more technical definition can be found here. CellRanger provides you with both matrices, whereas other quantification tools only provide you with the unfiltered matrix. The pipeline can handle the following cases:

1. You have both filtered and unfiltered matrices: Provide both matrices in the samplesheet and the pipeline will use the unfiltered matrix for ambient RNA removal and the filtered matrix for all other steps.
2. You only have the filtered matrix: Provide the filtered matrix in the samplesheet and the pipeline will use it for all steps. In this case, only decontX can be used for ambient RNA removal, as all other methods require the unfiltered matrix.
3. You only have the unfiltered matrix: Provide the unfiltered matrix in the samplesheet and the pipeline will automatically create a filtered matrix by identifying empty droplets using CellBender.

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with at least 2 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Minimal samplesheet

The samplesheet needs to contain at least two columns: sample and at least one out of filtered and unfiltered:

sample,unfiltered
sample1,/absolute/path/to/sample1.h5ad
sample2,relative/path/to/sample2.rds
sample3,/absolute/path/to/sample3.csv

Full samplesheet

There are a couple of optional columns that can be used for more advanced features:

sample,filtered,unfiltered,batch_col,label_col,unknown_label,min_genes,min_cells,min_counts_cell,min_counts_gene
sample1,/absolute/path/to/sample1_filtered.h5ad,/absolute/path/to/sample1.h5ad,batch,cell_type,unknown,1,2,3,4
sample2,relative/path/to/sample2_filtered.rds,relative/path/to/sample2.rds,batch_id,annotation,unannotated,5,6,7,8
sample3,/absolute/path/to/sample3_filtered.csv,/absolute/path/to/sample3.csv,,,,9,10,11,12

For CSV input files, specifying the batch_col, label_col, and unknown_label columns will not have any effect, as no additional metadata is available in the CSV file.

An example samplesheet has been provided with the pipeline.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/scdownstream --input ./samplesheet.csv --outdir ./results --genome GRCh37 -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/scdownstream -profile docker -params-file params.yaml

with:

input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'
<...>

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

Cell type annotation

Automated cell type annotation using Celltypist is supported. You can specify the models to use with the celltypist_model parameter. If no models are specified, no cell type annotation will be performed.

Reference mapping

The pipeline supports mapping new samples into the latent space of an existing scVI/scANVI model. If it is an scANVI model, this approach allows transferring cell type annotations to new samples. If the scVI/scANVI model was built during a previous run of the pipeline, you can also use the previous output AnnData file as a base, and the pipeline will aggregate the new samples onto the base AnnData.

The following scenarious can be distinguished:

• You have a reference scVI model from an arbitrary source (e.g. from a publication) and you want to map new data into the latent space described by the model. In this case, you need to provide the path to the reference model via the reference_model parameter and set the reference_model_type parameter to scvi. Only scvi and scanvi may be used in the integration_methods parameter in this case. scanvi will only work if he input data in the samplesheet contains at least some cell type annotations. Using scanvi in addition to scvi as an integration method will extend the model so that it can be used for label transfer in future.
• You have a reference scANVI model from an arbitrary source (e.g. from a publication) and you want to map new data into the latent space described by the model and transfer cell type annotations to the new data. In this case, you need to provide the path to the reference model via the reference_model parameter and set the reference_model_type parameter to scanvi. Only scanvi may be used in the integration_methods parameter in this case.
• You have a reference scVI/scANVI model as well as an output AnnData file from a previous run of the pipeline and you want to add more samples to the existing AnnData file. In this case, you need to provide the path to the reference model via the reference_model parameter and set the reference_model_type parameter to either scvi or scanvi, depending on the type of the reference model. If an scANVI model is used, existing cell type annotations will be transferred to the new samples. The existing AnnData file should be provided via the base_adata parameter.

The pipeline will perform the preprocessing steps on the new samples as usual. During the integration step, the new samples will be mapped onto the latent space of the reference model. If base_adata is provided, the new samples will then be aggregated onto the base file. The clustering, dimensionality reduction etc. will then be performed on the integrated object.

Skipping integration

If you want to run tasks after the integration step without performing integration, you can provide a previous result of the pipeline as the base_adata parameter. You do not need to provide a samplesheet via the input parameter in this case. In order to let the pipeline know which integration embeddings should be used, you need to provide the base_embeddings parameter. If you stored the labels (e.g. cell type annotations) in a column other than label, you can provide the column name via the base_label_col parameter.

The pipeline will then re-execute the tasks after the integration step without performing integration again. Most interestingly, the pipeline will generate cell type specific UMAPs, clusterings, and PAGA graphs, if the clustering_per_label parameter is set to true.

GPU acceleration

Tools with implemented support for GPU acceleration are:

• cellbender
• scvi-tools
  • scVI/scANVI
  • scAR
  • solo
• rapids-singlecell
  • scrublet
  • harmony
  • HVG identification
  • Neighborhood graph calculation, UMAP and Leiden clustering
  • Identification of characteristic genes (rank_genes_groups)

To utilize GPU acceleration, you need to specify the gpu profile. This will make the tool steps use cuda-enabled environments and it will tell the tools to use the GPU. All processes which support GPU acceleration are marked with the process_gpu label.

You also need to make sure that the tasks are run on a machine with a GPU. If all tasks are run on a machine with a GPU, no further action is needed. If you are running the pipeline on a slurm cluster, where there is dedicated queue for GPU jobs, you need additional configuration that might look like this:

process {
  withLabel:process_gpu {
    queue = '<gpu-queue>'
    clusterOptions = '--gpus 1'
  }
}

For different executors, the configuration might look different. Once a wider range of users have tested the GPU support, we will provide more detailed instructions for different executors.

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/scdownstream

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/scdownstream 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 reproducbility, 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.

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 enviroment.

• 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 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.

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'