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Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data

Nature Methods volume 19, pages 316–322 (2022)Cite this article

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Abstract

The rapid growth of high-throughput single-cell and single-nucleus RNA-sequencing (scRNA-seq and snRNA-seq) technologies has produced a wealth of data over the past few years. The size, volume and distinctive characteristics of these data necessitate the development of new computational methods to accurately and efficiently quantify sc/snRNA-seq data into count matrices that constitute the input to downstream analyses. We introduce the alevin-fry framework for quantifying sc/snRNA-seq data. In addition to being faster and more memory frugal than other accurate quantification approaches, alevin-fry ameliorates the memory scalability and false-positive expression issues that are exhibited by other lightweight tools. We demonstrate how alevin-fry can be effectively used to quantify sc/snRNA-seq data, and also how the spliced and unspliced molecule quantification required as input for RNA velocity analyses can be seamlessly extracted from the same preprocessed data used to generate normal gene expression count matrices.

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Fig. 1: Overview of the alevin-fry pipeline (operating in USA quantification mode).
Fig. 2: Comprehensive analysis of the performance pf alevin-fry on real and simulated datasets.

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Data availability

All data analyzed in this paper are publicly available. The mouse pancreas dataset, the mouse placenta dataset and the zebrafish pineal dataset analyzed during the current study are available on NCBI Gene Expression Omnibus, under accession number GSM3852755, GSM4609872 and GSM3511193, respectively. The PBMC5k and PBMC10k dataset are available from 10x Genomics at https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.2/5k_pbmc_v3 and https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc_10k_v3, respectively. The description of the references that were used for mapping the sequencing reads from each dataset can be found in Supplementary Table 1.

Code availability

Alevin-fry is written in Rust (https://www.rust-lang.org), and is available under the BSD 3-Clause license, as a free and open-source tool at https://github.com/COMBINE-lab/alevin-fry. The specific version used in this work40 has been uploaded to zenodo at https://doi.org/10.5281/zenodo.5799568. The generation of RAD files is implemented as part of the alevin command of the salmon tool, available at https://github.com/COMBINE-lab/salmon. Both tools are also available through bioconda41,42. The roe R package has been developed for the construction of splici reference sequences, and it is available at https://github.com/COMBINE-lab/roe as free and open-source software under the BSD 3-Clause license. Useful scripts and functions for simplifying reference preparation and quantification as well as utilities for reading alevin-fry output in Python and R are available at https://github.com/COMBINE-lab/usefulaf. Support for reading alevin-fry output (including USA mode output) has been integrated into the fishpond package available at https://github.com/mikelove/fishpond as well as through Bioconductor43. The scripts used to perform the analyses in this paper are available at https://github.com/COMBINE-lab/alevin-fry-paper-scripts.

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Acknowledgements

This work is supported by the National Institute of Health under grant award numbers R01HG009937 to R.P. and K99CA267677 to A.S., and the National Science Foundation under grant award numbers CCF-1750472 to R.P. and CNS-1763680 to R.P. Also, this project has been made possible in part by grant number CZIF2020-004893 from the Chan Zuckerberg Initiative Foundation to R.P. The funders had no role in the design of the method, data analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Cell Biology and Molecular Genetics and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA

    Dongze He

  2. Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA

    Mohsen Zakeri & Rob Patro

  3. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Hirak Sarkar

  4. Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland

    Charlotte Soneson

  5. SIB Swiss Institute of Bioinformatics, Basel, Switzerland

    Charlotte Soneson

  6. New York Genome Center, New York City, NY, USA

    Avi Srivastava

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Contributions

All authors conceptualized the method. D.H., A.S., R.P., M.Z. and H.S. implemented the software. M.Z. and R.P. benchmarked the tools. D.H., R.P. and C.S. analyzed the results. All authors wrote and approved the manuscript.

Corresponding author

Correspondence to Rob Patro.

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Competing interests

R.P. is a cofounder of Ocean Genomics, Inc. The other authors declare no competing interests.

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Nature Methods thanks Davis McCarthy, David van Dijk and Matthew Ritchie for their contribution to the peer review of this work. Lin Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.

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Supplementary information

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Supplementary Table 1

Details of the datasets used in this work.

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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). https://doi.org/10.1038/s41592-022-01408-3

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