eisca
A single-cell RNA-seq pipeline aimed at covering all analysis stages from quantification and clustering to tertiary analyses.
Science Score: 65.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 10 DOI reference(s) in README -
○Academic publication links
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○Academic email domains
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✓Institutional organization owner
Organization tgac has institutional domain (www.earlham.ac.uk) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (9.4%) to scientific vocabulary
Repository
A single-cell RNA-seq pipeline aimed at covering all analysis stages from quantification and clustering to tertiary analyses.
Basic Info
- Host: GitHub
- Owner: TGAC
- License: mit
- Language: Nextflow
- Default Branch: master
- Size: 18.6 MB
Statistics
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 2
- Releases: 0
Metadata Files
README.md
Introduction
TGAC/eisca is a bioinformatics pipeline that perform analysis for single-cell RNA-seq data. The pipeline is built using Nextflow and processes (implemented and to be implemented) are as follows:
- Primary analysis
- FastQC - Raw read QC
- TrimGalore - Adapter and quality trimming to FastQ files
- Kallisto & Bustools - Mapping & quantification by Kallisto & Bustools
- Salmon Alevin - Mapping & quantification by Salmon Alevin
- STARsolo - Mapping & quantification by STAR
- Secondary analysis
- QC & cell filtering - cell filtering and QC on raw data and filtered data
- Clustering analysis - single-cell clustering analysis
- Merging/integration of samples
- Tertiary analysis
- Cell type annotation
- Differential expression analysis
- Cell-cell communication analysis
- Trajectory & pseudotime analysis (to be implemented)
- Other downstream analyses (to be implemented)
- Pipeline reporting
- Analysis report - Single-ell Analysis Report.
- MultiQC - Aggregate report describing results and QC for tools registered in nf-core
- Pipeline information - Report metrics generated during the workflow execution
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 testbefore 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
pbmc8k,pbmc8k_S1_L007_R1_001.fastq.gz,pbmc8k_S1_L007_R2_001.fastq.gz
pbmc8k,pbmc8k_S1_L008_R1_001.fastq.gz,pbmc8k_S1_L008_R2_001.fastq.gz
pbmc5k,pbmc5k_S1_L003_R1_001.fastq.gz,pbmc5k_S1_L003_R2_001.fastq.gz
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 TGAC/eisca \
-profile <docker/singularity/.../institute> \
--input samplesheet.csv \
--genome_fasta GRCm38.p6.genome.chr19.fa \
--gtf gencode.vM19.annotation.chr19.gtf \
--protocol 10XV2 \
--aligner <alevin/kallisto/star/cellranger/universc> \
--outdir <OUTDIR>
[!WARNING] Please provide pipeline parameters via the CLI or Nextflow
-params-fileoption. Custom config files including those provided by the-cNextflow 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. <!-- (https://nf-co.re/eisca/usage). -->
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. <!-- (https://nf-co.re/eisca/output). -->
Credits
nf-core/eisca was originally written by Huihai Wu.
We thank the following people for their extensive assistance in the development of this pipeline:
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 #eisca channel (you can join with this invite).
Citations
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.
Owner
- Name: Earlham Institute
- Login: TGAC
- Kind: organization
- Location: Norwich, UK
- Website: https://www.earlham.ac.uk
- Twitter: EarlhamInst
- Repositories: 118
- Profile: https://github.com/TGAC
BBSRC-supported life science research institute in Norwich, UK
Citation (CITATIONS.md)
# nf-core/eisca: 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. ## 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
- Issues event: 2
- Push event: 16
- Fork event: 1
- Create event: 2
Last Year
- Issues event: 2
- Push event: 16
- Fork event: 1
- Create event: 2
Dependencies
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