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Fast, sensitive and accurate integration of single-cell data with Harmony

Nature Methods volume 16, pages 1289–1296 (2019)Cite this article

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Abstract

The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony (https://github.com/immunogenomics/harmony), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~106 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data.

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Fig. 1: Overview of Harmony algorithm.
Fig. 2: Quantitative assessment of dataset mixing and cell-type accuracy with cell-line datasets.
Fig. 3: Computational efficiency benchmarks. BBKNN, Scanorama, MNN Correct and MultiCCA are compared on five downsampled HCA datasets of increasing sizes.
Fig. 4: Fine-grained subpopulation identification in PBMCs across technologies.
Fig. 5: Integration of pancreatic islet cells by both donor and technology.
Fig. 6: Harmony integrates spatially resolved transcriptomic with dissociated scRNAseq datasets.

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Data availability

All data analyzed in this article are publicly available through online sources. We included links to all data sources in Supplementary Table 8.

Code availability

Harmony and LISI are available as R packages on https://github.com/immunogenomics/harmony and https://github.com/immunogenomics/lisi. Scripts to reproduce results of the primary analyses will be made available on https://github.com/immunogenomics/harmony2019. Additionally, vignettes are included as Supplementary Notes. Supplementary Note 1 provides a detailed walkthrough of Harmony, connecting theoretical algorithm components to their code implementations. Supplementary Note 2 demonstrates the LISI metric and how to evaluate its statistical significance. Supplementary Note 1 uses Harmony with simulated datasets.

Change history

  • 26 August 2020

    In the supplementary information originally posted for this article, the Supplementary Results and Supplementary Notes 1–3 were missing. The error has been corrected online.

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Acknowledgements

This work was supported in part by funding from the National Institutes of Health (grant nos. UH2AR067677 and U19AI111224 and no. 1R01AR063759 (to S.R.) and T32 AR007530-31 (to I.K.)). We thank members of the Raychaudhuri and Brenner labs for comments and discussion. I.K. and K.W. were funded as part of a collaborative research agreement with F. Hoffmann-La Roche Ltd (Basel, Switzerland), to S.R. and M.B.B.

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Authors and Affiliations

  1. Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA

    Ilya Korsunsky, Nghia Millard, Kamil Slowikowski, Fan Zhang, Yuriy Baglaenko, Po-ru Loh & Soumya Raychaudhuri

  2. Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Ilya Korsunsky, Nghia Millard, Kamil Slowikowski, Fan Zhang, Kevin Wei, Yuriy Baglaenko, Michael Brenner & Soumya Raychaudhuri

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

    Ilya Korsunsky, Nghia Millard, Kamil Slowikowski, Fan Zhang, Yuriy Baglaenko, Po-ru Loh & Soumya Raychaudhuri

  4. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Ilya Korsunsky, Nghia Millard, Kamil Slowikowski, Fan Zhang, Yuriy Baglaenko, Po-ru Loh & Soumya Raychaudhuri

  5. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Jean Fan

  6. Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK

    Soumya Raychaudhuri

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  1. Ilya Korsunsky
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Contributions

S.R. and I.K. conceived the research. I.K. led computational work under the guidance of S.R., assisted by N.M., P.L., J.F. and K.S. All authors participated in interpretation and writing the manuscript.

Corresponding author

Correspondence to Soumya Raychaudhuri.

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Competing interests

I.K. does paid bioinformatics consulting through Brilyant LLC.

Additional information

Peer review information Nicole Rusk was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–19, Supplementary Results and Supplementary Notes 1–3.

Reporting Summary

Supplementary Software 1

Harmony R package. Software to perform Harmony integration analysis.

Supplementary Software 2

LISI R package. Software to compute the Local Inverse Simpson’s Index.

Supplementary Tables 1–8

Jurkat LISI, Time benchmark, Memory Benchmark, HCA LISI, PBMC LISI, Inhibitory, Excitatory, Data Sources.

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Korsunsky, I., Millard, N., Fan, J. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 16, 1289–1296 (2019). https://doi.org/10.1038/s41592-019-0619-0

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