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Fast, sensitive and accurate integration of single-cell data with Harmony
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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 ~10 6 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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“Importantly, both Harmony’s and scVI’s design inherently incorporates batch labels in its training process.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [21]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Multi-omics integration represents another natural application area, where specialized agents analyze transcriptomic, epigenomic, proteomic, and spatial datasets, reconciling heterogeneous signals through shared representation spaces, a task particularly suited to foundation models capable of cross-modal alignment [12, 43, 44].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [44]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Optionally, ModuleEigengenes can employ the harmony22 algorithm following SVD, which increases runtime but not memory usage.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [22]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Batch effects between samples were corrected using harmonypy89 (v0.0.6) on the scaled topic distributions, and Leiden clustering with resolution 0.6 resulted in 11 clusters, corresponding to 14 cell types based on previous labelling.”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [89]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Harmony batch correction was then used to correct PCA embeddings for technical batch effects between experiments 54 .”
From Targeting TBK1 to overcome resistance to cancer immunotherapy (2023) · cites this paper as [54]
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In the citing paper
“Integrating data from multiple experiments requires the use of employ algorithms such as Seurat’s canonical correlation analysis (CCA), mutual nearest neighbors (MNN), or Harmony to batch correction [16, 17].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [16]
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“To mitigate these effects, we applied Harmony,39 which resulted in shared hepatocyte clusters across individuals (Figure 2A; STAR Methods).”
From Cellular senescence in human liver under normal aging and cancer (2026) · cites this paper as [39]
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In the citing paper
“Using BANKSY50-derived spatial domains and Harmony39 correction, we identified 18 reproducible niches shared across 20 samples (Figures S5A–S5C; STAR Methods).”
From Cellular senescence in human liver under normal aging and cancer (2026) · cites this paper as [39]
The cited source says what the claim attributes to it.
In the citing paper
“To control for the technical bias caused by different sequencing runs, the R package harmony v0.1.1 92 was applied.”
From Autoantigen-specific CD4+ T cells acquire an exhausted phenotype and persist in human antigen-specific autoimmune diseases (2024) · cites this paper as [92]
The cited source says what the claim attributes to it.
In the citing paper
“In particular, Harmony22 is an algorithm well suited for correcting batch effects that may be present in a dimensionality-reduced single-cell expression dataset,79”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [22]
The cited source says what the claim attributes to it.
In the citing paper
“Interestingly, the fine-tuned Geneformer’s cell embeddings primarily clustered by cell types and showed improved integration of platforms compared to the original data even after batch effect removal using the ComBat17 or Harmony18 methods (Extended Data Fig. 4b–f).”
From Transfer learning enables predictions in network biology (2023) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“Geneformer fine-tuned with only Drop-seq data automatically integrated DroNc-seq data such that the fine-tuned Geneformer cell embeddings primarily clustered by cell types and showed improved integration of platforms compared to the original data even after batch effect removal using the ComBat17 or Harmony18 methods.”
From Transfer learning enables predictions in network biology (2023) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“Batch effects between samples were corrected using harmonypy70 (v.0.0.6) on the scaled topic distributions”
From Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation (2024) · cites this paper as [70]
The cited source says what the claim attributes to it.
In the citing paper
“Following batch effect correction (14), Ts21 and control nuclei were annotated independently to minimize integration bias and identify putative Ts21-specific cell subpopulations or states.”
From Molecular and cellular processes disrupted in the early postnatal Down syndrome prefrontal cortex (2026) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“RunPCA was used to perform PCA on the scaled data and subsequently the samples were integrated using Harmony’s RunHarmony function with samples as the variable to remove.”
From SORLA up-regulation suppresses pathological effects in aged tauopathy mouse brain (2026) · cites this paper as Korsunsky, Millard et al. 2019
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In the citing paper
“Both scGPT and Geneformer produce cell embeddings intended to project potentially noisy gene expression measurements to a more biologically relevant latent space [18–21], and then these cell embeddings are fine-tuned for cell type classification.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [21]
Experimental — accuracy not yet measuredIn the citing paper
“We found that the SCING GRN module based dimensionality reduction carried the ability to correct for batch effect and retain biological information (Figure S6) in a similar manner to dedicated batch correction methods such as FastMNN,41 Harmony,42 and Seurat.43”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [42]
In the citing paper
“The different single-cell datasets were integrated in a single object using the R package Harmony v1.0 92 for dealing with experimental and biological confounding factors and removing batch effects.”
From AAV vectors trigger DNA damage response-dependent pro-inflammatory signalling in human iPSC-derived CNS models and mouse brain (2025) · cites this paper as [92]
In the citing paper
“In each case, highly reliable cell type results were observed, based on visual inspection of UMAP distributions and low Local Inverse Simpson Index (LISI) values for cell type coherence when compared to Seurat integration.”
From A harmonized atlas of mouse spinal cord cell types and their spatial organization (2021) · cites this paper as [25]
In the citing paper
“This integration was compared to three independent methods (Harmony25, Conos26, and LIGER27).”
From A harmonized atlas of mouse spinal cord cell types and their spatial organization (2021) · cites this paper as [25]
In the citing paper
“Current proposed single-cell foundation models produce cell embeddings. These embeddings are intended to project potentially noisy gene expression measurements to a more biologically relevant latent space and to thus improve our ability to resolve cell types, consistent with previous machine learning methods in this field (including scVI) [30, 31, 24, 32].”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [32]
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Molecular and spatial signatures of mouse brain aging at single-cell resolution
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“We then co-embedded the MERFISH and snRNA-seq data, including all measured cells, using the Harmony algorithm 35 and performed an integrated clustering analysis across these two data modalities (Figures 1C and 1D), which showed good correspondence with the clustering results from the snRNA-seq data…”
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Autism genes converge on asynchronous development of shared neuron classes
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Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy
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Chemical reprogramming of human somatic cells to pluripotent stem cells.
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MultiVI: deep generative model for the integration of multi-modal data
Ashuach, T., Gabitto, M. I., Jordan, M. I., & Yosef, N. (2021) · doi:10.1101/2021.08.20.457057 · Cited by 385
Local and systemic responses to SARS-CoV-2 infection in children and adults
Yoshida, M., Worlock, K. B., Huang, N., Lindeboom, R. G. H., Butler, C. R., Kumasaka, N., Dominguez Conde, C., Mamanova, L., Bolt, L., Richardson, L., et al. (2022) · Nature · doi:10.1038/s41586-021-04345-x · Cited by 365
Identification of a broadly fibrogenic macrophage subset induced by type 3 inflammation
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Single-cell proteomics enabled by next-generation sequencing or mass spectrometry
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Integrative Single-Cell RNA-Seq and ATAC-Seq Analysis of Human Developmental Hematopoiesis
Ranzoni, A. M., Tangherloni, A., Berest, I., Riva, S. G., Myers, B., Strzelecka, P. M., Xu, J., Panada, E., Mohorianu, I., Zaugg, J. B., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2020.11.015 · Cited by 331
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
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Maturation and persistence of the anti-SARS-CoV-2 memory B cell response
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Compromised Humoral Functional Evolution Tracks with SARS-CoV-2 Mortality
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Capturing Cardiogenesis in Gastruloids
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Inferring and perturbing cell fate regulomes in human brain organoids
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Lee, H., Fenster, R. J., Pineda, S. S., Gibbs, W. S., Mohammadi, S., Davila-Velderrain, J., Garcia, F. J., Therrien, M., Novis, H. S., Gao, F., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.021 · Cited by 279
Franjic, D., Skarica, M., Ma, S., Arellano, J. I., Tebbenkamp, A. T. N., Choi, J., Xu, C., Li, Q., Morozov, Y. M., Andrijevic, D., et al. (2022) · Neuron · doi:10.1016/j.neuron.2021.10.036 · Cited by 308
Joint profiling of histone modifications and transcriptome in single cells from mouse brain
Zhu, C., Zhang, Y., Li, Y. E., Lucero, J., Behrens, M. M., & Ren, B. (2021) · Nature Methods · doi:10.1038/s41592-021-01060-3 · Cited by 301
Epigenetic memory of coronavirus infection in innate immune cells and their progenitors
Cheong, J.-G., Ravishankar, A., Sharma, S., Parkhurst, C. N., Grassmann, S. A., Wingert, C. K., Laurent, P., Ma, S., Paddock, L., Miranda, I. C., et al. (2023) · Cell · doi:10.1016/j.cell.2023.07.019 · Cited by 309
Targeting immune–fibroblast cell communication in heart failure
Amrute, J. M., Luo, X., Penna, V., Yang, S., Yamawaki, T., Hayat, S., Bredemeyer, A., Jung, I.-H., Kadyrov, F. F., Heo, G. S., et al. (2024) · Nature · doi:10.1038/s41586-024-08008-5 · Cited by 339
Single-cell brain organoid screening identifies developmental defects in autism
Li, C., Fleck, J. S., Martins-Costa, C., Burkard, T. R., Themann, J., Stuempflen, M., Peer, A. M., Vertesy, Á., Littleboy, J. B., Esk, C., et al. (2023) · Nature · doi:10.1038/s41586-023-06473-y · Cited by 283
BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis
Singhal, V., Chou, N., Lee, J., Yue, Y., Liu, J., Chock, W. K., Lin, L., Chang, Y.-C., Teo, E. M. L., Aow, J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01664-3 · Cited by 282
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DestVI identifies continuums of cell types in spatial transcriptomics data
Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01272-8 · Cited by 276
“For algorithms based on embeddings and nearest neighbors imputation (scVI, Harmony and Scanorama), we impute by calculating the average gene expression of the k-nearest neighbors of the embedding of the spot s restricted to the single-cell data of cell type c only.”
MethodHotspot identifies informative gene modules across modalities of single-cell genomics
DeTomaso, D., & Yosef, N. (2021) · Cell Systems · doi:10.1016/j.cels.2021.04.005 · Cited by 267
HypoMap—a unified single-cell gene expression atlas of the murine hypothalamus
Steuernagel, L., Lam, B. Y. H., Klemm, P., Dowsett, G. K. C., Bauder, C. A., Tadross, J. A., Hitschfeld, T. S., del Rio Martin, A., Chen, W., de Solis, A. J., et al. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00657-y · Cited by 267
Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies
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A harmonized atlas of mouse spinal cord cell types and their spatial organization
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A Map of Transcriptional Heterogeneity and Regulatory Variation in Human Microglia
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Decoding molecular and cellular heterogeneity of mouse nucleus accumbens
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Complement activation induces excessive T cell cytotoxicity in severe COVID-19
Georg, P., Astaburuaga-García, R., Bonaguro, L., Brumhard, S., Michalick, L., Lippert, L. J., Kostevc, T., Gäbel, C., Schneider, M., Streitz, M., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.040 · Cited by 218
SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
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DISCO: A database of Deeply Integrated human Single-Cell Omics data
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A spatially resolved atlas of the human lung characterizes a gland-associated immune niche
Madissoon, E., Oliver, A. J., Kleshchevnikov, V., Wilbrey-Clark, A., Polanski, K., Richoz, N., Ribeiro Orsi, A., Mamanova, L., Bolt, L., Elmentaite, R., et al. (2023) · Nature Genetics · doi:10.1038/s41588-022-01243-4 · Cited by 219
In vivo CRISPR screens reveal the landscape of immune evasion pathways across cancer
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Early Alzheimer’s disease pathology in human cortex involves transient cell states
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SpaGE: Spatial Gene Enhancement using scRNA-seq
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Single-cell analysis of prenatal and postnatal human cortical development
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A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma
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Epigenomic dissection of Alzheimer’s disease pinpoints causal variants and reveals epigenome erosion
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“Integrating data from multiple experiments requires the use of employ algorithms such as Seurat’s canonical correlation analysis (CCA), mutual nearest neighbors (MNN), or Harmony to batch correction [16, 17].”
MethodProgressive plasticity during colorectal cancer metastasis
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Statistical and machine learning methods for spatially resolved transcriptomics data analysis
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An atlas of gene regulatory elements in adult mouse cerebrum
Li, Y. E., Preissl, S., Hou, X., Zhang, Z., Zhang, K., Qiu, Y., Poirion, O. B., Li, B., Chiou, J., Liu, H., et al. (2021) · Nature · doi:10.1038/s41586-021-03604-1 · Cited by 185
Metadata
- Authors
- Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Raychaudhuri, Soumya
- ORCID
- Venue
- Nature Methods
- Published
- 2019-11-18
- Volume
- 16
- Issue
- 12
- Pages
- 1289-1296
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC6884693
- OpenAlex
- W2984472267
- Cited by
- 10,488
- References
- 56
- Citations by year
- 2026: 2634 · 2025: 2714 · 2024: 2218 · 2023: 1370 · 2022: 845 · 2021: 464 · 2020: 196 · 2019: 37
- FWCI
- 131.99
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (UH2AR067677, U19AI111224, T32 AR007530-31)
- F. Hoffmann-La Roche Ltd
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- single-cell RNA sequencing, multi-omics integration, harmony, cell type clustering, peripheral blood mononuclear cells, pancreatic islet cells, mouse embryo development, spatial transcriptomics, computational efficiency
- MeSH terms
- Algorithms, Animals, Base Sequence, Humans, Jurkat Cells, Mice, HEK293 Cells, Single-Cell Analysis, Datasets as Topic
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