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 3 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Multiple runs of the same sample

The sample identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:


Full samplesheet

There is a strict requirement for the first 3 columns to match those defined in the table below.

sampleRequired. Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (_).
fastq_1Required. Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
fastq_2Required. Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
expected_cellsOptional. Number of cells expected for a sample. Must be an integer. If multiple rows are provided for the same sample, this must be the same number for all rows, i.e. the total number of expected cells for the sample.
seq_centerOptional. Sequencing center for the sample. If multiple rows are provided for the same sample, this must be the same string for all rows. Samples sequenced at different centers are considered different samples and must have different identifiers. Used for STARsolo BAM outputs only. Overrides params.seq_center.

An example samplesheet has been provided with the pipeline.

Expected cells

This parameter is currently supported by

Note that since cellranger v7, it is not recommended anymore to supply the --expected-cells parameter.

Aligning options

By default, the pipeline uses Salmon Alevin (i.e. —aligner alevin) to perform pseudo-alignment of reads to the reference genome and to perform the downstream BAM-level quantification. Then QC reports are generated with AlevinQC.

Other aligner options for running the pipeline are:

  • Kallisto & Bustools, where kallisto is used for alignment and bustools is used for downstream analysis
    • --aligner kallisto
  • STARsolo to perform both alignment and downstream analysis.
    • --aligner star
  • Cellranger to perform both alignment and downstream analysis.
    • --aligner cellranger
  • UniverSC to run an open-source version of Cell Ranger on any technology
    • ‘—aligner universc`

If using cellranger or universc

This pipeline automatically renames input FASTQ files to follow the naming convention by 10x:

[Sample Name]_S1_L00[Lane Number]_[Read Type]_001.fastq.gz

For more details, see

As a sanity check, we verify that filenames of a pair of FASTQ files only differ by R1/R2.

Support for different scRNA-seq protocols

The single-cell protocol used in the experiment can be specified using the --protocol flag. For cellranger, it is recommended to stick with the default value 'auto' for automatic detection of the protocol. For all other aligner, you need to specify the protocol manually.

The three 10x Genomics protocols 3’ v1 (10XV1), 3’ v2 (10XV2) and 3’ v3 (10XV3) are universally supported by all aligners in the pipeline and mapped to the correct options automatically. If the protocol is unknown to the nf-core pipeline, the value specified to --protocol is passed to the aligner in verbatim to support additional protocols.

Here are some hints on running the various aligners with different protocols


The command kb --list shows all supported, preconfigured protocols. Additionally, a custom technology string such as 0,0,16:0,16,26:1,0,0 can be speficied:

Additionally kallisto bus will accept a string specifying a new technology in the format of bc:umi

where each of bc,umi and seq are a triplet of integers separated by a comma, denoting the file index, start and stop of the sequence used. For example to specify the 10xV2 technology we would use 0,0,16:0,16,26:1,0,0

For more details, please refer to the Kallisto/bustools documentation.


Alevin/fry also supports custom chemistries in a slighly different format, e.g. 1{b[16]u[12]x:}2{r:}.

For more details, see the simpleaf documentation


See the UniverSC GitHub page for all supported protocols.

Currently only 3’ scRNA-Seq parameters are supported in nextflow, although chemistry parameters for 5’ scRNA-Seq and full-length scRNA-Seq libraries are supported by teh container.

If using cellranger-arc

Automatic file name detection

This pipeline currently does not automatically renames input FASTQ files to follow the naming convention by 10x:

[Sample Name]_S1_L00[Lane Number]_[Read Type]_001.fastq.gz

Thus please make sure your files follow this naming convention.

Sample sheet definition

If you are using cellranger-arc you have to add the column sample_type (atac for scATAC or gex for scRNA) and fastq_barcode (part of the scATAC data) to your samplesheet as an input.

Beware of the following points:

  • It is important that you give your scRNA and scATAC different [Sample Name]s.
  • Check first which file is your barcode fastq file for your scATAC data (see).
  • If you have more than one sequencing run then you have to give them another suffix (e.g., rep*) to your [Sample Name] (see).

An example samplesheet for a dataset called test_scARC that has two sequencing runs for the scATAC and one seqeuncing run from two lanes for the scRNA could look like this:


Config file and index

Cellranger-arc needs a reference index directory that you can provide with --cellranger_index. Be aware, you can use for cellranger-arc the same index you use for cellranger (see). Yet, a cellranger-arc index might include additional data (e.g., TF binding motifs). Therefore, please first check if you have to create a new cellranger-arc index (see here for more information)

If you decide to create a cellranger-arc index, then you need to create a config file to generate the index. The pipeline can do this autmatically for you if you provide a --fasta, --gtf, and an optional --motif file. However, you can also decide to provide your own config file with --cellrangerarc_config, then you also have to specify with --cellrangerarc_reference the reference genome name that you have used and stated as genome: in your config file.

Running the pipeline

The minimum typical command for running the pipeline is as follows:

nextflow run nf-core/scrnaseq --input ./samplesheet.csv --outdir ./results --genome GRCh38 -profile docker

This will launch the pipeline with the docker configuration profile and default --type and --barcode_whitelist. See below for more information about profiles and these options.

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>.


Do not use -c <file> to specify parameters as this will result in errors. Custom config files specified with -c must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).

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

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

with params.yaml containing:

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

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/scrnaseq


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/scrnaseq releases page and find the latest version number - numeric only (eg. 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. 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.


If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.

Core Nextflow arguments


These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).


Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments. Note that multiple profiles can be loaded, for example: -profile docker - the order of arguments is important!

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.


We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from 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
  • 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.


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.


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.


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'