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scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

View ORCID ProfileCombiz Khozoie, Nurun Fancy, Mahdi M. Marjaneh, View ORCID ProfileAlan E. Murphy, View ORCID ProfilePaul M. Matthews, Nathan Skene
doi: https://doi.org/10.1101/2021.08.16.456499
Combiz Khozoie
1UK Dementia Research Institute, Imperial College London
2Department of Brain Sciences, Imperial College London, United Kingdom
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  • ORCID record for Combiz Khozoie
  • For correspondence: c.khozoie@imperial.ac.uk
Nurun Fancy
1UK Dementia Research Institute, Imperial College London
2Department of Brain Sciences, Imperial College London, United Kingdom
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Mahdi M. Marjaneh
1UK Dementia Research Institute, Imperial College London
2Department of Brain Sciences, Imperial College London, United Kingdom
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Alan E. Murphy
1UK Dementia Research Institute, Imperial College London
2Department of Brain Sciences, Imperial College London, United Kingdom
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Paul M. Matthews
1UK Dementia Research Institute, Imperial College London
2Department of Brain Sciences, Imperial College London, United Kingdom
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Nathan Skene
1UK Dementia Research Institute, Imperial College London
2Department of Brain Sciences, Imperial College London, United Kingdom
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Abstract

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput: datasets with over a million cells are becoming commonplace. The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods, led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data. The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities. Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users. We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.

Competing Interest Statement

PMM has received consultancy fees from Roche, Adelphi Communications, Celgene, Neurodiem and Medscape. He has received honoraria or speakers' fees from Novartis and Biogen and has received research or educational funds from Biogen, Novartis and GlaxoSmithKline

Footnotes

  • Updated author affiliations, placement of figures, and supplementary files.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 19, 2021.
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scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data
Combiz Khozoie, Nurun Fancy, Mahdi M. Marjaneh, Alan E. Murphy, Paul M. Matthews, Nathan Skene
bioRxiv 2021.08.16.456499; doi: https://doi.org/10.1101/2021.08.16.456499
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scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data
Combiz Khozoie, Nurun Fancy, Mahdi M. Marjaneh, Alan E. Murphy, Paul M. Matthews, Nathan Skene
bioRxiv 2021.08.16.456499; doi: https://doi.org/10.1101/2021.08.16.456499

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