klkeys/cellranger-count

Nota bene: this workflow is mostly a relic and is listed here for archival purposes. The bulk of the code and repository structure is cribbed from qbic-pipelines/cellranger. The functionality is subsumed ( and probably better maintained ) in nf-core/scrnaseq as of version 2.0.0. Consider using the aforementioned sources before trying this one.

Introduction

A cloud implementation of 10X Genomics CellRanger workflow.

Pipeline summary

Read QC (FastQC)
Count reads (cellranger count)
Aggregate read counts (cellranger aggr)
Present QC for raw read, alignment, and sample similarity (MultiQC, R)

Quick Start

Install Nextflow (>=21.04.0)
Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs). Note: This pipeline does not currently support running with Conda on macOS if the --remove_ribo_rna parameter is used because the latest version of the SortMeRNA package is not available for this platform.
Download the pipeline and test it on a minimal dataset with a single command:
```
nextflow run klkeys/cellranger-count -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>
```
- 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 then the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the --singularity_pull_docker_container parameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use the nf-core download command to pre-download all of the required containers before running the pipeline and to set the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options to be able 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.

Start running your own analysis!

nextflow run klkeys/cellranger-count \
    --input samplesheet.csv \
    --transcriptome_reference s3://my-bucket/transcriptome_reference.tar.gz \
    -profile <docker/singularity/podman/conda/institute>

Documentation

The klkeys/cellranger-count pipeline comes with documentation about the pipeline usage, parameters and output.

Credits

These scripts were originally written for use at the Quantitative Biology Center(QBIC), in Tuebingen, Germany, by Gisela Gabernet (@ggabernet).

The pipeline was re-written in Nextflow DSL2 and is primarily maintained by Kevin L. Keys (@klkeys) formerly at Ambys Medicines, USA.

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 #rnaseq channel (you can join with this invite).

Citations

If you use klkeys/cellranger-count for your analysis, please cite it using the following doi: 10.5281/zenodo.1400710

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

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

klkeys / cellranger-count