Introduction

The nf-core/scrnaseq is a bioinformatics best-practise analysis pipeline for transciptomic data of single-cell RNAs. The pipeline creates count matrices from FASTQ sequence reads. It is suitable for droplets-based sequencing technologies. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Pipeline summary

This is a community effort in building a pipeline capable to support:

• Alevin + AlevinQC
• STARSolo
• Kallisto + BUStools

Documentation

The nf-core/scrnaseq pipeline comes with documentation about the pipeline usage, parameters and output.

Quick Start

1. Install Nextflow (>=21.10.3)

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs)

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run nf-core/scrnaseq -profile test,YOURPROFILE

   Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

   • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
   • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
   • If you are using singularity and are persistently observing issues downloading Singularity images directly due to timeout or network issues, then you can use the --singularity_pull_docker_container parameter to pull and convert the Docker image instead. Alternatively, you can use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
   • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

4. Start running your own analysis!

   nextflow run nf-core/scrnaseq -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input samplesheet.csv --genome_fasta GRCm38.p6.genome.chr19.fa --gtf gencode.vM19.annotation.chr19.gtf --protocol 10XV2

Credits

The nf-core/scrnaseq was initiated by Peter J. Bailey (Salmon Alevin, AlevinQC) with major contributions from Olga Botvinnik (STARsolo, Testdata) and Alex Peltzer (Kallisto/BusTools workflow).

We thank the following people for their extensive assistance in the development of this pipeline:

• @KevinMenden
• @ggabernet
• @FloWuenne

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #scrnaseq channel (you can join with this invite).

Citations

If you use nf-core/scrnaseq for your analysis, please cite it using the following doi: 10.5281/zenodo.3568187

The basic benchmarks that were used as motivation for incorporating the three available modular workflows can be found in this publication.

We offer all three paths for the processing of scRNAseq data so it remains up to the user to decide which pipeline workflow is chosen for a particular analysis question.

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.