Fast, Sensitive and Accurate Integration of Single-cell Data with Harmony

Overview

Journal Nat Methods

Specialties Biomedical Engineering
Pathology

Date 2019 Nov 20

PMID 31740819

Citations 3029

Authors

Ilya Korsunsky

Nghia Millard

Jean Fan

Kamil Slowikowski

Fan Zhang

Kevin Wei

Yuriy Baglaenko

Michael Brenner

Po-Ru Loh

Soumya Raychaudhuri

Affiliations

Soon will be listed here.

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 ~10 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.

Citing Articles

A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients.

Pita-Juarez Y, Karagkouni D, Kalavros N, Melms J, Niezen S, Delorey T Genome Biol. 2025; 26(1):56.

PMID: 40087773 DOI: 10.1186/s13059-025-03499-5.

Integrated analysis of single-cell and bulk transcriptomes uncovers clinically relevant molecular subtypes in human prostate cancer.

Ding T, He L, Lin G, Xu L, Zhu Y, Wang X Chin J Cancer Res. 2025; 37(1):90-114.

PMID: 40078560 PMC: 11893346. DOI: 10.21147/j.issn.1000-9604.2025.01.07.

Single-Cell Transcriptional Profiling Reveals Cell Type-Specific Sex-Dependent Molecular Patterns of Schizophrenia.

Zhou R, Zhang T, Sun B Int J Mol Sci. 2025; 26(5).

PMID: 40076849 PMC: 11900070. DOI: 10.3390/ijms26052227.

Spatial Gene Expression of Human Coronary Arteries Revealed the Molecular Features of Diffuse Intimal Thickening in Explanted Hearts.

Li B, Leung S, Elishaev M, Cheng W, Mocci G, Bjorkegren J Int J Mol Sci. 2025; 26(5).

PMID: 40076574 PMC: 11900230. DOI: 10.3390/ijms26051949.

LiverSCA 2.0: An Enhanced Comprehensive Cell Atlas for Human Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma.

Suoangbaji T, Long R, Ng I, Mak L, Ho D Cancers (Basel). 2025; 17(5).

PMID: 40075736 PMC: 11898674. DOI: 10.3390/cancers17050890.

References

Liu J, Hunter C, Du A, Ediger B, Walp E, Murray J . Islet-1 regulates Arx transcription during pancreatic islet alpha-cell development. J Biol Chem. 2011; 286(17):15352-60. PMC: 3083195. DOI: 10.1074/jbc.M111.231670. View

Pijuan-Sala B, Griffiths J, Guibentif C, Hiscock T, Jawaid W, Calero-Nieto F . A single-cell molecular map of mouse gastrulation and early organogenesis. Nature. 2019; 566(7745):490-495. PMC: 6522369. DOI: 10.1038/s41586-019-0933-9. View

Ashburner M, Ball C, Blake J, Botstein D, Butler H, Cherry J . Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25(1):25-9. PMC: 3037419. DOI: 10.1038/75556. View

Jia S, Ivanov A, Blasevic D, Muller T, Purfurst B, Sun W . Insm1 cooperates with Neurod1 and Foxa2 to maintain mature pancreatic β-cell function. EMBO J. 2015; 34(10):1417-33. PMC: 4492000. DOI: 10.15252/embj.201490819. View

Hie B, Bryson B, Berger B . Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat Biotechnol. 2019; 37(6):685-691. PMC: 6551256. DOI: 10.1038/s41587-019-0113-3. View

Stegle O, Parts L, Piipari M, Winn J, Durbin R . Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat Protoc. 2012; 7(3):500-7. PMC: 3398141. DOI: 10.1038/nprot.2011.457. View

Haghverdi L, Lun A, Morgan M, Marioni J . Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018; 36(5):421-427. PMC: 6152897. DOI: 10.1038/nbt.4091. View

Muraro M, Dharmadhikari G, Grun D, Groen N, Dielen T, Jansen E . A Single-Cell Transcriptome Atlas of the Human Pancreas. Cell Syst. 2016; 3(4):385-394.e3. PMC: 5092539. DOI: 10.1016/j.cels.2016.09.002. View

Kuleshov M, Jones M, Rouillard A, Fernandez N, Duan Q, Wang Z . Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016; 44(W1):W90-7. PMC: 4987924. DOI: 10.1093/nar/gkw377. View

10.

Grun D, Muraro M, Boisset J, Wiebrands K, Lyubimova A, Dharmadhikari G . De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data. Cell Stem Cell. 2016; 19(2):266-277. PMC: 4985539. DOI: 10.1016/j.stem.2016.05.010. View

11.

Mizoguchi F, Slowikowski K, Wei K, Marshall J, Rao D, Chang S . Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis. Nat Commun. 2018; 9(1):789. PMC: 5824882. DOI: 10.1038/s41467-018-02892-y. View

12.

Regev A, Teichmann S, Lander E, Amit I, Benoist C, Birney E . The Human Cell Atlas. Elife. 2017; 6. PMC: 5762154. DOI: 10.7554/eLife.27041. View

13.

La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V . RNA velocity of single cells. Nature. 2018; 560(7719):494-498. PMC: 6130801. DOI: 10.1038/s41586-018-0414-6. View

14.

Zhang F, Wei K, Slowikowski K, Fonseka C, Rao D, Kelly S . Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat Immunol. 2019; 20(7):928-942. PMC: 6602051. DOI: 10.1038/s41590-019-0378-1. View

15.

Burcelin R, Knauf C, Cani P . Pancreatic alpha-cell dysfunction in diabetes. Diabetes Metab. 2008; 34 Suppl 2:S49-55. DOI: 10.1016/S1262-3636(08)73395-0. View

16.

Buttner M, Miao Z, Wolf F, Teichmann S, Theis F . A test metric for assessing single-cell RNA-seq batch correction. Nat Methods. 2018; 16(1):43-49. DOI: 10.1038/s41592-018-0254-1. View

17.

Chen E, Tan C, Kou Y, Duan Q, Wang Z, Meirelles G . Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013; 14:128. PMC: 3637064. DOI: 10.1186/1471-2105-14-128. View

18.

Lein E, Hawrylycz M, Ao N, Ayres M, Bensinger A, Bernard A . Genome-wide atlas of gene expression in the adult mouse brain. Nature. 2006; 445(7124):168-76. DOI: 10.1038/nature05453. View

19.

Lawlor N, George J, Bolisetty M, Kursawe R, Sun L, Sivakamasundari V . Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Res. 2016; 27(2):208-222. PMC: 5287227. DOI: 10.1101/gr.212720.116. View

20.

Baron M, Veres A, Wolock S, Faust A, Gaujoux R, Vetere A . A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Syst. 2016; 3(4):346-360.e4. PMC: 5228327. DOI: 10.1016/j.cels.2016.08.011. View