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Nat Methods. 2019 Dec;16(12):1289-1296. doi: 10.1038/s41592-019-0619-0. Epub 2019 Nov 18.

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

Korsunsky I1,2,3,4, Millard N1,2,3,4, Fan J5, Slowikowski K1,2,3,4, Zhang F1,2,3,4, Wei K2, Baglaenko Y1,2,3,4, Brenner M2, Loh PR1,3,4, Raychaudhuri S6,7,8,9,10.

Author information

1
Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA.
2
Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
3
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
4
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
5
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
6
Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA. soumya@broadinstitute.org.
7
Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. soumya@broadinstitute.org.
8
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. soumya@broadinstitute.org.
9
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. soumya@broadinstitute.org.
10
Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK. soumya@broadinstitute.org.

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.

PMID:
31740819
PMCID:
PMC6884693
[Available on 2020-05-18]
DOI:
10.1038/s41592-019-0619-0

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