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

View ORCID ProfileIlya Korsunsky, View ORCID ProfileJean Fan, View ORCID ProfileKamil Slowikowski, View ORCID ProfileFan Zhang, View ORCID ProfileKevin Wei, View ORCID ProfileYuriy Baglaenko, View ORCID ProfileMichael Brenner, View ORCID ProfilePo-Ru Loh, View ORCID ProfileSoumya Raychaudhuri
doi: https://doi.org/10.1101/461954
Ilya Korsunsky
1Center for Data Sciences, Brigham and Women’s Hospital, Massachusetts, USA
2Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston
3Department of Biomedical Informatics, Harvard Medical School, Massachusetts, USA
4Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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  • ORCID record for Ilya Korsunsky
Jean Fan
5Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA
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Kamil Slowikowski
1Center for Data Sciences, Brigham and Women’s Hospital, Massachusetts, USA
2Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston
3Department of Biomedical Informatics, Harvard Medical School, Massachusetts, USA
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Fan Zhang
1Center for Data Sciences, Brigham and Women’s Hospital, Massachusetts, USA
2Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston
3Department of Biomedical Informatics, Harvard Medical School, Massachusetts, USA
4Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Kevin Wei
2Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston
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Yuriy Baglaenko
1Center for Data Sciences, Brigham and Women’s Hospital, Massachusetts, USA
2Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston
3Department of Biomedical Informatics, Harvard Medical School, Massachusetts, USA
4Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Michael Brenner
2Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston
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  • ORCID record for Michael Brenner
Po-Ru Loh
1Center for Data Sciences, Brigham and Women’s Hospital, Massachusetts, USA
3Department of Biomedical Informatics, Harvard Medical School, Massachusetts, USA
4Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Soumya Raychaudhuri
1Center for Data Sciences, Brigham and Women’s Hospital, Massachusetts, USA
2Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston
3Department of Biomedical Informatics, Harvard Medical School, Massachusetts, USA
4Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
6Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
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Abstract

The rapidly emerging diversity of single cell RNAseq datasets allows us to characterize the transcriptional behavior of cell types across a wide variety of biological and clinical conditions. With this comprehensive breadth comes a major analytical challenge. The same cell type across tissues, from different donors, or in different disease states, may appear to express different genes. A joint analysis of multiple datasets requires the integration of cells across diverse conditions. This is particularly challenging when datasets are assayed with different technologies in which real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Unlike available single-cell integration methods, Harmony can simultaneously account for multiple experimental and biological factors. We develop objective metrics to evaluate the quality of data integration. In four separate analyses, we demonstrate the superior performance of Harmony to four single-cell-specific integration algorithms. Moreover, we show that Harmony requires dramatically fewer computational resources. It is the only available algorithm that makes the integration of ∼ 106 cells feasible on a personal computer. We demonstrate that Harmony identifies both broad populations and fine-grained subpopulations of PBMCs from datasets with large experimental differences. In a meta-analysis of 14,746 cells from 5 studies of human pancreatic islet cells, Harmony accounts for variation among technologies and donors to successfully align several rare subpopulations. In the resulting integrated embedding, we identify a previously unidentified population of potentially dysfunctional alpha islet cells, enriched for genes active in the Endoplasmic Reticulum (ER) stress response. The abundance of these alpha cells correlates across donors with the proportion of dysfunctional beta cells also enriched in ER stress response genes. Harmony is a fast and flexible general purpose integration algorithm that enables the identification of shared fine-grained subpopulations across a variety of experimental and biological conditions.

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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 4.0 International license.
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Posted November 05, 2018.
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Fast, sensitive, and accurate integration of single cell data with Harmony
Ilya Korsunsky, Jean Fan, Kamil Slowikowski, Fan Zhang, Kevin Wei, Yuriy Baglaenko, Michael Brenner, Po-Ru Loh, Soumya Raychaudhuri
bioRxiv 461954; doi: https://doi.org/10.1101/461954
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Fast, sensitive, and accurate integration of single cell data with Harmony
Ilya Korsunsky, Jean Fan, Kamil Slowikowski, Fan Zhang, Kevin Wei, Yuriy Baglaenko, Michael Brenner, Po-Ru Loh, Soumya Raychaudhuri
bioRxiv 461954; doi: https://doi.org/10.1101/461954

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