scrnaseq

Single-cell RNA-Seq pipeline for barcode-based protocols such as 10x, DropSeq or SmartSeq, offering a variety of aligners and empty-droplet detection

https://github.com/nf-core/scrnaseq

Science Score: 77.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
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DOI references
Found 7 DOI reference(s) in README
✓
Academic publication links
Links to: biorxiv.org
✓
Committers with academic emails
6 of 41 committers (14.6%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (11.0%) to scientific vocabulary

Keywords

10x-genomics 10xgenomics alevin bustools cellranger kallisto nextflow nf-core pipeline rna-seq single-cell star-solo workflow

Keywords from Contributors

workflows metagenomics pipelines bioinformatics nf-test dsl2 illumina taxonomic-classification taxonomic-profiling airr

Last synced: 6 months ago · JSON representation ·

Repository

Single-cell RNA-Seq pipeline for barcode-based protocols such as 10x, DropSeq or SmartSeq, offering a variety of aligners and empty-droplet detection

Basic Info

Host: GitHub
Owner: nf-core
License: mit
Language: Nextflow
Default Branch: master
Homepage: https://nf-co.re/scrnaseq
Size: 43.7 MB

Statistics

Stars: 285
Watchers: 152
Forks: 194
Open Issues: 72
Releases: 17

Topics

10x-genomics 10xgenomics alevin bustools cellranger kallisto nextflow nf-core pipeline rna-seq single-cell star-solo workflow

Created almost 7 years ago · Last pushed 7 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation

Introduction

nf-core/scrnaseq is a bioinformatics best-practice analysis pipeline for processing 10x Genomics single-cell RNA-seq data.

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

SimpleAF(Alevin-Fry) + AlevinQC
STARSolo
Kallisto + BUStools
Cellranger

[!IMPORTANT] Cellranger is a commercial tool from 10X Genomics Inc. and falls under the EULA from 10X Genomics Inc. The container provided for the CellRanger functionality in this pipeline has been built by the nf-core community and is therefore not supported by 10X genomics directly. We are in discussions with 10X on how to improve the user experience and licence situation for both us as a community as well as 10X and end users and will update this statement here accordingly.

Documentation

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

scrnaseq workflow

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

csv sample,fastq_1,fastq_2,expected_cells pbmc8k,pbmc8k_S1_L007_R1_001.fastq.gz,pbmc8k_S1_L007_R2_001.fastq.gz,10000 pbmc8k,pbmc8k_S1_L008_R1_001.fastq.gz,pbmc8k_S1_L008_R2_001.fastq.gz,10000

Each row represents a fastq file (single-end) or a pair of fastq files (paired end).

Now, you can run the pipeline using:

bash nextflow run nf-core/scrnaseq \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --fasta GRCm38.p6.genome.chr19.fa \ --gtf gencode.vM19.annotation.chr19.gtf \ --protocol 10XV2 \ --aligner <simpleaf/kallisto/star/cellranger> \ --outdir <OUTDIR>

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Decision Tree for users

The nf-core/scrnaseq pipeline features several paths to analyze your single cell data. Future additions will also be done soon, e.g. the addition of multi-ome analysis types. To aid users in analyzing their data, we have added a decision tree to help people decide on what type of analysis they want to run and how to choose appropriate parameters for that.

Options for the respective alignment method can be found here to choose between methods.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/scrnaseq was originally written by Bailey PJ, Botvinnik O, Marques de Almeida F, Gabernet G, Peltzer A, Sturm G.

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

@heylf
@KevinMenden
@FloWuenne
@rob-p
GHGA

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 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.

Owner

Name: nf-core
Login: nf-core
Kind: organization
Email: core@nf-co.re

Website: http://nf-co.re
Twitter: nf_core
Repositories: 84
Profile: https://github.com/nf-core

A community effort to collect a curated set of analysis pipelines built using Nextflow.

Citation (CITATIONS.md)

# nf-core/scrnaseq: Citations

## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.

## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

## Pipeline tools

- [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

> Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].

- [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/)

> Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.

- [Simpleaf](https://doi.org/10.1093/bioinformatics/btad614)

  > He, D., Patro, R. simpleaf: a simple, flexible, and scalable framework for single-cell data processing using alevin-fry, Bioinformatics 39, 10 (2023).

* [Alevin-fry](https://doi.org/10.1038/s41592-022-01408-3)

  > He, D., Zakeri, M., Sarkar, H. et al. Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data. Nat Methods 19, 316–322 (2022).

* [Alevin](https://doi.org/10.1186/s13059-019-1670-y)

  > Srivastava, A., Malik, L., Smith, T. et al. Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biol 20, 65 (2019).

* [Salmon](https://www.nature.com/articles/nmeth.4197)

  > Patro, R., Duggal, G., Love, M. et al. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14, 417–419 (2017).

* [Kallisto/Bustools](https://www.nature.com/articles/s41587-021-00870-2)

  > Melsted, P., Booeshaghi, A.S., Liu, L. et al. Modular, efficient and constant-memory single-cell RNA-seq preprocessing. Nat Biotechnol 39, 813–818 (2021).

* [StarSolo](https://www.biorxiv.org/content/10.1101/2021.05.05.442755v1)
  > Benjamin Kaminow, Dinar Yunusov, Alexander Dobin. STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data. BioRxiv 2021.05.05.442755 (2021).

## Software packaging/containerisation tools

- [Anaconda](https://anaconda.com)

  > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

- [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)

  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)

  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

- [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)

  > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.

- [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)

  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.

GitHub Events

Total

Create event: 35
Release event: 1
Issues event: 65
Watch event: 72
Delete event: 37
Member event: 1
Issue comment event: 313
Push event: 175
Pull request event: 97
Pull request review event: 161
Pull request review comment event: 143
Fork event: 38

Last Year

Create event: 35
Release event: 1
Issues event: 65
Watch event: 72
Delete event: 37
Member event: 1
Issue comment event: 313
Push event: 175
Pull request event: 97
Pull request review event: 161
Pull request review comment event: 143
Fork event: 38

Committers

Last synced: over 2 years ago

All Time

Total Commits: 785
Total Committers: 41
Avg Commits per committer: 19.146
Development Distribution Score (DDS): 0.763

Past Year

Commits: 212
Committers: 21
Avg Commits per committer: 10.095
Development Distribution Score (DDS): 0.802

Top Committers

Name	Email	Commits
Alexander Peltzer	a**r@g**m	186
Felipe Marques de Almeida	a**s@g**m	107
Olga Botvinnik	o**k@g**m	61
Gregor Sturm	m**l@g**e	37
Felipe Marques de Almeida	f**9@g**m	33
kevinmenden	k**n@t**e	31
nf-core-bot	c**e@n**e	31
Alexander Peltzer	a****r	26
Gregor Sturm	g**m@b**m	26
Alexander Peltzer	a**r@b**m	25
Sangram Keshari Sahu	s**5@g**m	22
Pol Alvarez	p**s@g**m	21
Rob Syme	r**e@g**m	19
Adam Talbot	a**t@s**o	18
ggabernet	g**t@q**e	16
Florian	f**o@g**m	15
Alison Meynert	a**2@e**k	15
TomKellyGenetics	t**s@g**m	14
Harshil Patel	d**h@g**m	12
maxulysse	m**a@g**m	10
PeterBailey	P**2@g**k	10
Alex Thiery	a**y@k**k	7
Harshil Patel	d****h	5
Azedine Zoufir	4****x	5
Hana Medova	5****a	5
RHReynolds	r**6@u**k	5
Khajidu	K****u	4
Adam Talbot	1****t	3
Marcel Ribeiro-Dantas	m**s@c**r	2
Gisela Gabernet	g**t@g**m	2
and 11 more...

Committer Domains (Top 20 + Academic)

seqera.io: 2 boehringer-ingelheim.com: 2 ip-172-31-11-186.ec2.internal: 1 lifebit.ai: 1 peters-mbp.home: 1 student.howest.be: 1 curie.fr: 1 ucl.ac.uk: 1 kcl.ac.uk: 1 glasgow.ac.uk: 1 ed.ac.uk: 1 qbic.uni-tuebingen.de: 1 nf-co.re: 1 t-online.de: 1 gregor-sturm.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 106
Total pull requests: 132
Average time to close issues: 5 months
Average time to close pull requests: 16 days
Total issue authors: 69
Total pull request authors: 36
Average comments per issue: 3.48
Average comments per pull request: 2.78
Merged pull requests: 99
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 25
Pull requests: 41
Average time to close issues: about 2 months
Average time to close pull requests: 5 days
Issue authors: 20
Pull request authors: 18
Average comments per issue: 1.28
Average comments per pull request: 1.83
Merged pull requests: 26
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

grst (21)
nick-youngblut (14)
fmalmeida (5)
tomsing1 (4)
wzheng0520 (4)
svigneau (3)
Vivian-chen16 (3)
rhodesch (3)
adamrtalbot (3)
alexblaessle (3)
apeltzer (3)
asp8200 (2)
heylf (2)
maggiebr0wn (2)
Khajidu (2)

Pull Request Authors

grst (50)
fmalmeida (29)
nf-core-bot (20)
apeltzer (10)
heylf (9)
adamrtalbot (8)
nictru (7)
kafkasl (5)
robsyme (5)
drpatelh (4)
DongzeHE (3)
wzheng0520 (2)
Vivian-chen16 (2)
kopichris (2)
nick-youngblut (2)

Top Labels

Issue Labels

bug (74) enhancement (68) quality & consistency (7) good first issue (2) help wanted (2) question (1) documentation (1) alevin (1) cellranger-arc (1) bug-confirmed (1)

Pull Request Labels

Ready for review (9) enhancement (8) bug (5) DO_NOT_MERGE (2)

Dependencies

.github/workflows/awsfulltest.yml actions

actions/upload-artifact v3 composite
nf-core/tower-action v3 composite

.github/workflows/awstest.yml actions

actions/upload-artifact v3 composite
nf-core/tower-action v3 composite

.github/workflows/branch.yml actions

mshick/add-pr-comment v1 composite

.github/workflows/ci.yml actions

actions/checkout v2 composite
nf-core/setup-nextflow v1 composite

.github/workflows/fix-linting.yml actions

actions/checkout v3 composite
actions/setup-node v2 composite

.github/workflows/linting.yml actions

actions/checkout v2 composite
actions/setup-node v2 composite
actions/setup-python v3 composite
actions/upload-artifact v2 composite
mshick/add-pr-comment v1 composite
nf-core/setup-nextflow v1 composite
psf/black stable composite

.github/workflows/linting_comment.yml actions

dawidd6/action-download-artifact v2 composite
marocchino/sticky-pull-request-comment v2 composite

.github/workflows/clean-up.yml actions

actions/stale v7 composite

modules/nf-core/cellranger/count/meta.yml cpan

modules/nf-core/cellranger/mkgtf/meta.yml cpan

modules/nf-core/cellranger/mkref/meta.yml cpan

modules/nf-core/custom/dumpsoftwareversions/meta.yml cpan

modules/nf-core/fastqc/meta.yml cpan

modules/nf-core/gffread/meta.yml cpan

modules/nf-core/gunzip/meta.yml cpan

modules/nf-core/kallistobustools/count/meta.yml cpan

modules/nf-core/kallistobustools/ref/meta.yml cpan

modules/nf-core/multiqc/meta.yml cpan

modules/nf-core/star/genomegenerate/meta.yml cpan

modules/nf-core/universc/meta.yml cpan

pyproject.toml pypi

scrnaseq

Science Score: 77.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Introduction

Documentation

Usage

Decision Tree for users

Pipeline output

Credits

Contributions and Support

Citations

Owner

Citation (CITATIONS.md)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Dependencies