nf-core/scrnaseq

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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. 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:

sample,fastq_1,fastq_2
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz
CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz

Full samplesheet

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

An example samplesheet has been provided with the pipeline.

Expected cells

This parameter is currently supported by

• Alevin-fry
• STARsolo
• Cellranger

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

Aligning options

By default (i.e. --aligner alevin), the pipeline uses Salmon to perform pseudo-alignment of reads to the reference genome and Alevin-fry 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
• Cellranger Multi to perform the alignment and downstream analysis of samples with multiple libraries at the same time using Feature Barcode technology that enables simultaneous profiling of the V(D)J repertoire, cell surface protein, antigen, and gene expression (GEX) data.
• 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

• this issue, discussing various mechanisms to deal with non-conformant filenames
• the README of the cellranger/count module which demonstrates that renaming files does not affect the results.
• the code for renaming files in the cellranger/count module
• the code for renaming files in UniverSC

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), 3’ v3 (10XV3), and 3’ v4 (10XV4) 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

Kallisto/bustools

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

Simpleaf has the ability to pass custom chemistries to Alevin-fry, in a slighly different format, e.g. 1{b[16]u[12]x:}2{r:}.

For more details, see Simpleaf’s paper, He et al. 2023.

UniverSC

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:

sample,fastq_1,fastq_2,fastq_barcode,sample_type
test_scARC,path/test_scARC_atac_rep1_S1_L001_R1_001.fastq.gz,path/test_scARC_atac_rep1_S1_L001_R2_001.fastq.gz,path/test_scARC_atac_rep1_S1_L001_I2_001.fastq.gz,atac
test_scARC,path/test_scARC_atac_rep2_S2_L001_R1_001.fastq.gz,path/test_scARC_atac_rep2_S2_L001_R2_001.fastq.gz,path/test_scARC_atac_rep2_S2_L001_I2_001.fastq.gz,atac
test_scARC,path/test_scARC_gex_S1_L001_R1_001.fastq.gz,path/test_scARC_gex_S1_L001_R2_001.fastq.gz,,gex
test_scARC,path/test_scARC_gex_S1_L002_R1_001.fastq.gz,path/test_scARC_gex_S1_L002_R2_001.fastq.gz,,gex

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>.

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.

If using cellranger-multi

Automatic file name detection

The pipeline is able to automatically rename input FASTQ files to follow the naming convention by 10x:

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

If your data already follows the expected naming convention, you can deactivate this behavior with skip_cellranger_renaming.

Sample sheet definition

If you are using cellranger-multi you have to add the column feature_type to indicate which of the Feature Barcode Technology your data corresponds to:

More information on the Feature Barcode Technologies can be found here: https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/running-pipelines/cr-3p-multi

Beware of the following points:

• It is important that you give the same sample name for the different feature barcode technologies data that correspond to the same and should be analysed together.
• The pipeline will automatically generate the cellranger multi config file based on the given data.
• When working with multiplexed data (FFPE or CMO), you’ll need a second samplesheet relating the multiplexed samples to the corresponding “physical” sample (details below). The sample column in the main samplesheet refers to the “physical” sample that may contain multiple multiplexed samples.

An example samplesheet could look like this:

sample,fastq_1,fastq_2,feature_type,expected_cells
PBMC_10K,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/10k_pbmc/fastqs/5gex/5gex/subsampled_sc5p_v2_hs_PBMC_10k_5gex_S1_L001_R1_001.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/10k_pbmc/fastqs/5gex/5gex/subsampled_sc5p_v2_hs_PBMC_10k_5gex_S1_L001_R2_001.fastq.gz,gex,1000
PBMC_10K,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/10k_pbmc/fastqs/bcell/subsampled_sc5p_v2_hs_PBMC_10k_b_S1_L001_R1_001.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/10k_pbmc/fastqs/bcell/subsampled_sc5p_v2_hs_PBMC_10k_b_S1_L001_R2_001.fastq.gz,vdj,1000
PBMC_10K,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/10k_pbmc/fastqs/5gex/5fb/subsampled_sc5p_v2_hs_PBMC_10k_5fb_S1_L001_R1_001.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/10k_pbmc/fastqs/5gex/5fb/subsampled_sc5p_v2_hs_PBMC_10k_5fb_S1_L001_R2_001.fastq.gz,ab,1000
PBMC_10K_CMO,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/10k_pbmc_cmo/fastqs/gex_1/subsampled_SC3_v3_NextGem_DI_CellPlex_Human_PBMC_10K_1_gex_S2_L001_R1_001.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/10k_pbmc_cmo/fastqs/gex_1/subsampled_SC3_v3_NextGem_DI_CellPlex_Human_PBMC_10K_1_gex_S2_L001_R2_001.fastq.gz,gex,1000
PBMC_10K_CMO,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/10k_pbmc_cmo/fastqs/cmo/subsampled_SC3_v3_NextGem_DI_CellPlex_Human_PBMC_10K_1_multiplexing_capture_S1_L001_R1_001.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/10k_pbmc_cmo/fastqs/cmo/subsampled_SC3_v3_NextGem_DI_CellPlex_Human_PBMC_10K_1_multiplexing_capture_S1_L001_R2_001.fastq.gz,cmo,1000
PBMC_10K_CMV,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/5k_cmvpos_tcells/fastqs/gex_1/subsampled_5k_human_antiCMV_T_TBNK_connect_GEX_1_S1_L001_R1_001.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/5k_cmvpos_tcells/fastqs/gex_1/subsampled_5k_human_antiCMV_T_TBNK_connect_GEX_1_S1_L001_R2_001.fastq.gz,gex,1000
PBMC_10K_CMV,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/5k_cmvpos_tcells/fastqs/ab/subsampled_5k_human_antiCMV_T_TBNK_connect_AB_S2_L004_R1_001.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/5k_cmvpos_tcells/fastqs/ab/subsampled_5k_human_antiCMV_T_TBNK_connect_AB_S2_L004_R2_001.fastq.gz,ab,1000
PBMC_10K_CMV,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/5k_cmvpos_tcells/fastqs/vdj/subsampled_5k_human_antiCMV_T_TBNK_connect_VDJ_S1_L001_R1_001.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/5k_cmvpos_tcells/fastqs/vdj/subsampled_5k_human_antiCMV_T_TBNK_connect_VDJ_S1_L001_R2_001.fastq.gz,vdj,1000
4PLEX_HUMAN,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/4plex_scFFPE/4plex_human_liver_colorectal_ovarian_panc_scFFPE_multiplex_S1_L001_R1_001.subsampled.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/4plex_scFFPE/4plex_human_liver_colorectal_ovarian_panc_scFFPE_multiplex_S1_L001_R2_001.subsampled.fastq.gz,gex,
4PLEX_HUMAN,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/4plex_scFFPE/4plex_human_liver_colorectal_ovarian_panc_scFFPE_multiplex_S1_L002_R1_001.subsampled.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/4plex_scFFPE/4plex_human_liver_colorectal_ovarian_panc_scFFPE_multiplex_S1_L002_R2_001.subsampled.fastq.gz,gex,
4PLEX_HUMAN,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/4plex_scFFPE/4plex_human_liver_colorectal_ovarian_panc_scFFPE_multiplex_S1_L003_R1_001.subsampled.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/4plex_scFFPE/4plex_human_liver_colorectal_ovarian_panc_scFFPE_multiplex_S1_L003_R2_001.subsampled.fastq.gz,gex,
4PLEX_HUMAN,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/4plex_scFFPE/4plex_human_liver_colorectal_ovarian_panc_scFFPE_multiplex_S1_L004_R1_001.subsampled.fastq.gz,https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/genomics/homo_sapiens/10xgenomics/cellranger/4plex_scFFPE/4plex_human_liver_colorectal_ovarian_panc_scFFPE_multiplex_S1_L004_R2_001.subsampled.fastq.gz,gex,

Additional samplesheet for multiplexed samples

You must provide those via a CSV with the --cellranger_multi_barcodes parameter. The file should look like this:

sample,multiplexed_sample_id,probe_barcode_ids,cmo_ids,description
PBMC_10K_CMO,PBMC_10K_CMO_PBMCs_human_1,,CMO301,PBMCs_human_1
PBMC_10K_CMO,PBMC_10K_CMO_PBMCs_human_2,,CMO302,PBMCs_human_2
4PLEX_HUMAN,Liver_BC1,BC001,,Healthy liver dissociated using the Miltenyi FFPE Tissue Dissociation Kit
4PLEX_HUMAN,Ovarian_BC2,BC002,,Ovarian cancer dissociated using the Miltenyi FFPE Dissociation Kit
4PLEX_HUMAN,Colorectal_BC3,BC003,,Colorectal cancer dissociated using the Miltenyi FFPE Dissociation Kit
4PLEX_HUMAN,Pancreas_BC4,BC004,,Healthy pancreas dissociated using the Miltenyi FFPE Tissue Dissociation Kit

The sample column must match the corresponding entry in the main samplesheet.

Additional reference data

• Cellranger multi needs a reference for GEX and VDJ analysis. They are calculated on the fly given the reference files (--fasta and --gtf) provided, but users can also provide their own with: --cellranger_index and --cellranger_vdj_index, for GEX and VDJ, respectively.

  When running cellranger multi, without any VDJ data, users can also skip VDJ automated ref building with: --skip_cellrangermulti_vdjref.

• When working with FFPE data, a prob set needs to be specified via --gex_frna_probe_set. This file is typically provided by 10x.

• When working with Cell Multiplexing Oligos (CMOs), a reference file needs to be provided via --gex_cmo_set. The default reference file, as well as a description how to write a custom one, are available from the 10x documentation

• When working with Feature barcoding (antibody capture), a reference file needs to be provided via --fb_reference. It relates each “feature” to the corresponding barcode sequence. The structure of this file is described in the cellranger documentation

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>.

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

nextflow run nf-core/scrnaseq -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.

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

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/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.

Core Nextflow arguments

-profile

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.

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'