The nf-core/scflow pipeline is designed to orchestrate a reproducible case/control analysis of scRNA-seq data with best-practices, at scale, from quality-control through to insight discovery.

A pipeline run with nf-core/scflow requires three inputs: (1) a two-column manifest file with paths to gene-cell matrices and a unique sample key; (2) a sample sheet with sample information for each input matrix in the manifest file; and, (3) a parameters configuration file (documentation for each parameter is available at []).

A complete, automated, scalable, and reproducible case-control analysis can then be performed with a single line of code: -

nextflow run nf-core/scflow \
--manifest Manifest.tsv \
--input Samplesheet.tsv \
-c scflow_params.config \
-profile local
nextflow run nf-core/scflow \
--manifest Manifest.tsv \
--input Samplesheet.tsv \
-c scflow_params.config \
-profile local

Pipeline inputs

Manifest file

You will need to create a manifest file with paths to the sparse matrices you would like to analyse. Use the manifest parameter to specify its location: -

--manifest '[path to manifest file]'

This is a tab-separated-variable file with two columns: key and filepath. This file specifies the locations of input files for an analysis and a unique key. This unique key identifies a unique sample (single row) inside the sample sheet (discussed below). The manifest file may contain fewer samples than the sample sheet and can be revised based on QC etc. without changing the sample sheet.

An example manifest file: -


In this example, the key column is a single word proquint (pronouncable-quintuplet) identifier generated using the ids package in R. Any unique identifier is valid (avoid spaces and special characters).

The filepath column of the manifest file should point to folders containing matrix.mtx.gz, features.tsv.gz, barcodes.tsv.gz for individual samples. These are the raw or filtered gene-cell matrices output by CellRanger (additional inputs will be supported in the future).

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use the input parameter to specify its location: -

--input '[path to samplesheet file]'
--input '[path to samplesheet file]'

An example samplesheet file: -


As sample sheet values may be used in figures and figure legends generated by the pipeline, relatively brief values in PascalCase are recommended. For example, Low is preferable to low, and MS is preferable to MultipleSclerosis. Spaces are not supported (e.g. Multiple Sclerosis).

Parameters file

The parameter configuration for an analysis is defined in the scflow_analysis.config file in the conf directory. This file defines over 130 tunable analysis parameters. Defaults are recommended for most parameters, while those defining experimental design should be configured before a run. The pipeline supports parameter tuning with cache-based workflow resume using the -resume NextFlow feature. This feature is highly recommended for optimizing parameters which are highly sensitive to dataset size/quality differences (e.g. clustering, dimensionality reduction).

The documentation for parameters are available in human-readable format here or for version-specific parameters, here:{version}/parameters.

For your first analysis, we recommend default parameters; however, parameters containing sample sheet specific variables should be updated before starting. These include: -

  • qc_factor_vars — this parameter will explicitly set the variable type of named sample sheet variables to factors, to override the assumption by R that table columns which contain only numbers are numeric or integers. In the sample sheet example above, the capdate, prepdate, and seqdate should all be explicitly overridden with qc_factor_vars='capdate,prepdate,seqdate'.
  • integ_categorical_covariates — add sample sheet variables which may be sources of sample-to-sample variance here in order to examine integration performance, e.g. integ_categorical_covariates='manifest,diagnosis,sex,group,seqdate'.
  • merge_facet_vars— optionally add categorical sample sheet variables which may be sources of batch effects here for assessment in the post-merge QC report, e.g. merge_facet_vars='seqdate,capdate'.
  • cta_facet_vars — optionally add categorical sample sheet variables here to evaluate cell-type metrics across classes in the cell-type metrics report, e.g. cta_facet_vars='manifest,diagnosis,sex'.
  • For differential gene expression (dge_ prefix parameters) and Dirichlet differential cell-type composition (dirich_ prefix parameters), set the dependent_var as the sample sheet variable of interest (e.g. diagnosis), and specify the reference class (ref_class) of the variable (e.g. control) if the variable is categorical. Typically the dge_confounding_vars would include both cellular and sample level co-variates (e.g. dge_confounding_vars='cngeneson,pc_mito,seqdate').

For more details of experimental parameters, see the parameters documentation or the scFlow Manual.

General Nextflow


The profile parameter can provide configuration presets for different compute environments. Switching from a local workstation analysis to a Cloud based analysis can be achieved simply by changing the profile parameter. For example, a Google Cloud analysis with automated staging of input matrices from Cloud storage (e.g. a Google Storage Bucket) can be achieved using -profile gcp. Additionally, pre-configured institutional profiles for a range of university and research institution HPC systems are readily available via nf-core [] and will be loaded dynamically at runtime. To check if your system is available in these configs, please see the documentation [].

Additionally, the test profile includes a complete configuration to test the pipeline (including a dynamically loaded minimal dataset, and associated inputs: a sample sheet, manifest file, and parameters). This may run with: -

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

Updating the pipeline

When you run a nextflow run 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/scflow


It’s 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/scflow releases page and find the latest version number - numeric only (e.g. 1.0.0). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.0.0.

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. Each release will also be associated with a citable DOI.

Additional options


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.

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.


Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information. We highly recommend utilizing Nextflow Tower for pipeline monitoring; your token can be passed with a custom config file.

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.

For example, if the nf-core/scflow pipeline is failing after multiple re-submissions of the PERFORM_DE process due to an exit code of 137 this would indicate that there is an out of memory issue.

To bypass this error you would need to find exactly which resources are set by the SCFLOW_DGE process. The quickest way is to search for process SCFLOW_DGE in the nf-core/scflow Github repo. We have standardised the structure of Nextflow DSL2 pipelines such that all module files will be present in the modules/ directory and so based on the search results the file we want is modules/local/process/scflow/ If you click on the link to that file you will notice that there is a label directive at the top of the module that is set to label process_high. The Nextflow label directive allows us to organise workflow processes in separate groups which can be referenced in a configuration file to select and configure subset of processes having similar computing requirements. The default values for the process_high label are set in the pipeline’s base.config which in this case is defined as 72GB. Providing you haven’t set any other standard nf-core parameters to cap the maximum resources used by the pipeline then we can try and bypass the SCFLOW_DGE process failure by creating a custom config file that sets at least 72GB of memory, in this case increased to 100GB. The custom config below can then be provided to the pipeline via the -c parameter as highlighted in previous sections.

process {
    withName: SCFLOW_DGE {
        memory = 100.GB
process {
    withName: SCFLOW_DGE {
        memory = 100.GB

NB: We specify just the process name i.e. SCFLOW_DGE in the config file and not the full task name string that is printed to screen in the error message or on the terminal whilst the pipeline is running i.e. SCFLOW:SCFLOW_DGE. You may get a warning suggesting that the process selector isn’t recognised but you can ignore that if the process name has been specified correctly. This is something that needs to be fixed upstream in core Nextflow.


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'
NXF_OPTS='-Xms1g -Xmx4g'