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Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS

Nature Protocols volume 18, pages 3690–3731 (2023)Cite this article

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Abstract

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user’s desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

Key points

  • This protocol describes procedures for learning cellular and molecular processes from single-cell RNA-sequencing data using the non-negative matrix factorization algorithm Coordinated Gene Activity across Pattern Subsets. This is implemented and demonstrated in Python and R, with additional vignettes covering how to run Coordinated Gene Activity across Pattern Subsets via Docker deployment and GenePattern Notebook.

  • This protocol presents an end-to-end, optimized workflow that is usable, flexible, totally optimized for contemporary single-cell data formats, accessible and intuitive for computational biologists.

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Fig. 1: NMF learns signal in input data.
Fig. 2: A generalized workflow for performing NMF on single-cell data.
Fig. 3: Distributed CoGAPS finds robust patterns across randomized gene or sample subsets.
Fig. 4: Decision tree for selecting the most appropriate PyCoGAPS or CoGAPS procedure to follow.
Fig. 5: Graphical comparison of the procedures.
Fig. 6: Comparison of runtimes of R CoGAPS versus PyCoGAPS.
Fig. 7: UMAP of patterns learned by PyCoGAPS.
Fig. 8: Comparing statistical overlap between a biologist’s single-cell annotations and the learned CoGAPS to associate patterns with biological processes.
Fig. 9: Python hallmark GSEA.
Fig. 10: UMAP of patterns learned by CoGAPS.
Fig. 11: Pattern amplitude by cell group.
Fig. 12: R CoGAPS hallmark GSEA.

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Data availability

The data analyzed in these examples is freely available under accession code GSA: CRA001160, and from the Genome Sequence Archive, where it has the ID: PRJCA001063.

Code availability

All code and example data objects are accessible via our lab’s GitHub repositories, and/or available for download from Zenodo50. The CoGAPS core library and R interface are available at https://github.com/FertigLab/CoGAPS/ and the PyCoGAPS (Python interface) can be obtained from https://github.com/FertigLab/pycogaps.

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Acknowledgements

This work was supported by U24CA248457/US Department of Health & Human Services, National Institutes of Health and National Institutes of Health U24 CA220341 (J.P.M.), the Chan-Zuckerberg Initiative DAF (2018-183445 to L.A.G. and 2018-183444 to E.J.F.); the Johns Hopkins University Catalyst (E.J.F. and L.A.G.); an Allegheny Health Network grant (to E.J.F.), U01CA212007 (to E.J.F.), U01CA253403 (to E.J.F.), P01CA247886 (to E.J.F. and E.M.J.); a Pilot Award from P50CA062924 (to E.J.F.) from the National Cancer Institute; the JHU School of Medicine Synergy Award (to E.J.F. and L.A.G.); 640183 from the Emerson Collective (to E.J.F. and E.M.J.); a Kavli Neurodiscovery Institute Distinguished Postdoctoral fellowship (G.L.S.-O.): a Johns Hopkins Provost Award (G.L.S.-O.); and K99NS122085 from the BRAIN Initiative in partnership with the National Institute of Neurological Disorders (G.L.S.-O.)

Author information

Author notes

  1. These authors contributed equally: Jeanette A.I. Johnson, Ashley P. Tsang.

Authors and Affiliations

  1. Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA

    Jeanette A. I. Johnson, Julia Bowden, Thomas Sherman, Melanie Loth, Jacquelyn W. Zimmerman, Elizabeth M. Jaffee, Elana J. Fertig & Genevieve L. Stein-O’Brien

  2. Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA

    Jeanette A. I. Johnson, Jacob T. Mitchell, Julia Bowden, Emily Davis-Marcisak, Melanie Loth, Jacquelyn W. Zimmerman, Elizabeth M. Jaffee, Elana J. Fertig & Genevieve L. Stein-O’Brien

  3. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Ashley P. Tsang, Loyal A. Goff, Elana J. Fertig & Genevieve L. Stein-O’Brien

  4. Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA

    Jacob T. Mitchell & Emily Davis-Marcisak

  5. Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA

    David L. Zhou, Loyal A. Goff & Genevieve L. Stein-O’Brien

  6. Department of Medicine, Moores Cancer Center, University of California San Diego, San Diego, CA, USA

    Ted Liefeld, Pablo Tamayo, Jill P. Mesirov & Michael Reich

  7. Kavli Neurodiscovery Institute, Johns Hopkins University, Baltimore, MD, USA

    Loyal A. Goff & Genevieve L. Stein-O’Brien

  8. Single Cell Training and Analysis Center, Johns Hopkins University, Baltimore, MD, USA

    Loyal A. Goff, Elana J. Fertig & Genevieve L. Stein-O’Brien

  9. Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Ben Kinny-Köster

  10. Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA

    Elana J. Fertig

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  1. Jeanette A. I. Johnson
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Contributions

E.J.F., G.L.S.-O. and T.S. originally conceived of the project. E.D.-M. and M.L. prepared a preliminary draft of the manuscript. A.P.T. and J.A.I.J. wrote PyCoGAPS with guidance from G.L.S.-O. A.P.T. implemented the PyCoGAPS GenePattern Notebook and introduced Docker support. M.R. and J.T.M. provided critical GenePattern Notebook support and collaboration. J.A.I.J. and A.P.T. wrote user guides, and J.T.M. performed the PDAC Atlas single-cell analysis included in them. J.B. created the CoGAPS website. All authors read, edited and approved the final manuscript.

Corresponding authors

Correspondence to Elana J. Fertig or Genevieve L. Stein-O’Brien.

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Key references using this protocol

Stein-O’Brien, G. L. et al. Cell Syst. 8, 395–411.e8 (2019): https://doi.org/10.1016/j.cels.2019.04.004

Clark, B. S. et al. Neuron 102, 1111–1126.e5 (2019): https://doi.org/10.1016/j.neuron.2019.04.010

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Johnson, J.A.I., Tsang, A.P., Mitchell, J.T. et al. Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS. Nat Protoc 18, 3690–3731 (2023). https://doi.org/10.1038/s41596-023-00892-x

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