Fast, sensitive and accurate integration of single-cell data with Harmony
Open accessPublished version of a preprint
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Both Harmony's and scVI's design inherently incorporates batch labels in its training process.
“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]
The Online Methods > Harmony overview (page unavailable) states that Harmony takes batch assignments as input, and its algorithm uses them during clustering and correction. The copy does not establish the claim about scVI’s design or training process; a search for “scVI” found no relevant passage. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMulti-omics integration is a natural application area where specialized agents analyze transcriptomic, epigenomic, proteomic, and spatial datasets, reconciling heterogeneous signals through shared representation spaces.
“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]
The Abstract says Harmony projects cells into a shared embedding, and the Results section demonstrates integration of transcriptomic and spatially resolved datasets. Our copy does not support the claim that specialized agents analyze epigenomic or proteomic datasets, or that Harmony integrates those modalities. (LLM, conf=0.88)
Experimental — accuracy not yet measuredModuleEigengenes can optionally employ the harmony algorithm following SVD, which increases runtime but not memory usage.
“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]
The Online Methods > Harmony section (page not provided) describes Harmony operating on a PCA embedding, supporting its use after dimensionality reduction. The Results > Harmony Scales to Large Data section reports Harmony’s runtime and memory usage, but does not assess its use in ModuleEigengenes or show that adding it increases runtime without increasing memory usage. (LLM, conf=0.85)
Experimental — accuracy not yet measuredBatch effects between samples were corrected using harmonypy on the scaled topic distributions.
“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]
The Online Methods > Harmony section describes an algorithm that corrects batch effects in cell embeddings, supporting the citation for the batch-correction method. Our copy identifies an R implementation, however, not harmonypy or version 0.0.6; it also does not describe correction of scaled topic distributions. No page number is available in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredHarmony batch correction was used to correct PCA embeddings for technical batch effects between experiments.
“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]
Integrating data from multiple experiments requires the use of algorithms such as Seurat's canonical correlation analysis (CCA), mutual nearest neighbors (MNN), or Harmony to batch correction.
“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]
Harmony correction resulted in shared hepatocyte clusters across individuals.
“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]
Using BANKSY-derived spatial domains and Harmony correction, the study identified 18 reproducible niches shared across 20 samples.
“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]
To control for the technical bias caused by different sequencing runs, the R package harmony v0.1.1 was applied.
“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]
Harmony is well suited for correcting batch effects that may be present in a dimensionality-reduced single-cell expression dataset.
“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]
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 ComBat or Harmony methods.
“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]
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 ComBat or Harmony.
“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]
Batch effects between samples were corrected using harmonypy on the scaled topic distributions.
“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]
Ts21 and control nuclei were annotated independently to minimize integration bias.
“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]
Both scGPT and Geneformer produce cell embeddings intended to project potentially noisy gene expression measurements to a more biologically relevant latent space, and then these cell embeddings are fine-tuned for cell type classification.
“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]
The Online Methods (Analysis Details) describe Harmony’s cell embeddings, and Performance and Benchmarking describes using an embedding for cell-type prediction. Neither passage addresses scGPT or Geneformer, whether their embeddings project noisy measurements into a biologically relevant latent space, or whether those embeddings are fine-tuned for classification. Searches for the model names and fine-tuning found no supporting passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredSCING GRN module based dimensionality reduction corrects for batch effect and retains biological information in a similar manner to dedicated batch correction methods such as FastMNN, Harmony, and Seurat.
“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]
The different single-cell datasets were integrated using Harmony to deal with experimental and biological confounding factors and remove batch effects.
“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]
Integration using three independent methodologies (Harmony, Conos, and LIGER) yielded highly reliable cell type results based on visual inspection of UMAP distributions and low LISI values for cell type coherence when compared to Seurat integration.
“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]
The main integration was compared to three independent methods: Harmony, Conos, and LIGER.
“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]
Current proposed single-cell foundation models produce cell embeddings intended to project noisy gene expression measurements to a more biologically relevant latent space and improve cell type resolution.
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799 · see citing works
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · see citing works
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool
Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma’ayan, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-128 · Cited by 7,814 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · see citing works
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572 · see citing works
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358 · see citing works
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118 · see citing works
Self-citationHierarchical Mixtures of Experts and the EM Algorithm
Jordan, M. I., & Jacobs, R. A. (1994) · Neural Computation · doi:10.1162/neco.1994.6.2.181 · Cited by 2,730 · see citing works
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · see citing works
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment
Azizi, E., Carr, A. J., Plitas, G., Cornish, A. E., Konopacki, C., Prabhakaran, S., Nainys, J., Wu, K., Kiseliovas, V., Setty, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.060 · Cited by 1,976 · see citing works
Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930 · see citing works
Expansion of the Gene Ontology knowledgebase and resources
The Gene Ontology Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1108 · Cited by 1,850 · see citing works
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.2 · Cited by 1,586 · see citing works
Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes
Segerstolpe, Å., Palasantza, A., Eliasson, P., Andersson, E.-M., Andréasson, A.-C., Sun, X., Picelli, S., Sabirsh, A., Clausen, M., Bjursell, M. K., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.020 · Cited by 1,550 · see citing works
A step-by-step workflow for low-level analysis of single-cell RNA-seq data
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.1 · Cited by 1,511 · see citing works
Baron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.011 · Cited by 1,508 · see citing works
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457 · see citing works
Khan, A., Fornés, O., Stigliani, A., Gheorghe, M., Castro-Mondragon, J. A., van der Lee, R., Bessy, A., Chèneby, J., Kulkarni, S. R., Tan, G., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1126 · Cited by 1,436 · see citing works
Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region
Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., Rubinstein, N. D., Hao, J., Regev, A., Dulac, C., et al. (2018) · Science · doi:10.1126/science.aau5324 · Cited by 1,365 · see citing works
Zhang, F., Wei, K., Slowikowski, K., Fonseka, C. Y., Rao, D. A., Kelly, S., Goodman, S. M., Tabechian, D., Hughes, L. B., Salomon-Escoto, K., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0378-1 · Cited by 1,355 · see citing works
Self-citationConcept Decompositions for Large Sparse Text Data Using Clustering
Dhillon, I. S., & Modha, D. S. (2001) · Machine Learning · doi:10.1023/a:1007612920971 · Cited by 1,308 · see citing works
A Single-Cell Transcriptome Atlas of the Human Pancreas
Muraro, M. J., Dharmadhikari, G., Grün, D., Gröen, N., Dielen, T., Jansen, E., van Gurp, L., Engelse, M. A., Carlotti, F., de Koning, E. J. P., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.09.002 · Cited by 1,288 · see citing works
Stegle, O., Parts, L., Piipari, M., Winn, J., & Durbin, R. (2012) · Nature Protocols · doi:10.1038/nprot.2011.457 · Cited by 1,049 · see citing works
A single-cell molecular map of mouse gastrulation and early organogenesis
Pijuan-Sala, B., Griffiths, J. A., Guibentif, C., Hiscock, T. W., Jawaid, W., Calero-Nieto, F. J., Mulas, C., Ibarra-Soria, X., Tyser, R. C. V., Ho, D. L. L., et al. (2019) · Nature · doi:10.1038/s41586-019-0933-9 · Cited by 1,033 · see citing works
Exponential scaling of single-cell RNA-seq in the past decade
Svensson, V., Vento‐Tormo, R., & Teichmann, S. A. (2018) · Nature Protocols · doi:10.1038/nprot.2017.149 · Cited by 936 · see citing works
BBKNN: fast batch alignment of single cell transcriptomes
Polański, K., Young, M. D., Miao, Z., Meyer, K. B., Teichmann, S. A., & Park, J.-E. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz625 · Cited by 932 · see citing works
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Hie, B., Bryson, B. D., & Berger, B. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0113-3 · Cited by 853 · see citing works
The immune cell landscape in kidneys of patients with lupus nephritis
the Accelerating Medicines Partnership in SLE network, Arazi, A., Rao, D. A., Berthier, C. C., Davidson, A., Liu, Y., Hoover, P., Chicoine, A. T., Eisenhaure, T., Jonsson, A. H., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0398-x · Cited by 828 · see citing works
Self-citationMissing data and technical variability in single-cell RNA-sequencing experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2018) · Biostatistics · doi:10.1093/biostatistics/kxx053 · Cited by 635 · see citing works
Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2015) · doi:10.1101/025528 · Cited by 625 · see citing works
Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis
Mizoguchi, F., Slowikowski, K., Wei, K., Marshall, J. L., Rao, D. A., Chang, S. K., Nguyen, H., Noss, E. H., Turner, J. D., Earp, B. E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-02892-y · Cited by 592 · see citing works
Self-citationDe Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data
Grün, D., Muraro, M. J., Boisset, J.-C., Wiebrands, K., Lyubimova, A., Dharmadhikari, G., van den Born, M., van Es, J., Jansen, E., Clevers, H., et al. (2016) · Cell Stem Cell · doi:10.1016/j.stem.2016.05.010 · Cited by 586 · see citing works
Lawlor, N. T., George, J., Bolisetty, M. T., Kursawe, R., Sun, L., Vijayakumar, S., Kycia, I., Robson, P., & Stitzel, M. L. (2016) · Genome Research · doi:10.1101/gr.212720.116 · Cited by 540 · see citing works
A test metric for assessing single-cell RNA-seq batch correction
Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-018-0254-1 · Cited by 511 · see citing works
Pdx1 Maintains β Cell Identity and Function by Repressing an α Cell Program
Gao, T., McKenna, B. D., Li, C., Reichert, M., Nguyen, J., Singh, T., Yang, C., Pannikar, A., Doliba, N. M., Zhang, T., et al. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2013.12.002 · Cited by 414 · see citing works
the Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium, Der, E., Suryawanshi, H., Morozov, P., Kustagi, M., Goilav, B., Ranabothu, S., Izmirly, P., Clancy, R., Belmont, H. M., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0386-1 · Cited by 403 · see citing works
Self-citationPdx1 ( MODY4 ) regulates pancreatic beta cell susceptibility to ER stress
Sachdeva, M. M., Claiborn, K. C., Khoo, C., Yang, J., Groff, D., Mirmira, R. G., & Stoffers, D. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0904849106 · Cited by 221 · see citing works
Exponential scaling of single-cell RNA-seq in the last decadePreprint
Svensson, V., Tormo, R. V., & Teichmann, S. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01379 · Cited by 166 · see citing works
Zhang, F., Wei, K. S., Slowikowski, K., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/351130 · Cited by 149 · see citing works
Self-citationClose, J., Xu, H., De Marco García, N., Batista-Brito, R., Rossignol, E., Rudy, B., & Fishell, G. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.3583-12.2012 · Cited by 141 · see citing works
Dimensionality Reduction Via Graph Structure Learning
Mao, Q., Wang, L., Steve Goodison, B., & Sun, Y. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783309 · Cited by 104 · see citing works
Insm1 cooperates with Neurod1 and Foxa2 to maintain mature pancreatic β‐cell function
Jia, S., Ivanov, A., Blasevic, D., Müller, T. H., Purfürst, B., Sun, W., Chen, W., Poy, M. N., Rajewsky, N., & Birchmeier, C. (2015) · The EMBO Journal · doi:10.15252/embj.201490819 · Cited by 103 · see citing works
X-Box Binding Protein 1 Is Essential for Insulin Regulation of Pancreatic α-Cell Function
Akiyama, M., Liew, C. W., Lu, S., Hu, J. Y., Martinez, R., Hambro, B., Kennedy, R. T., & Kulkarni, R. (2013) · Diabetes · doi:10.2337/db12-1747 · Cited by 79 · see citing works
Pancreatic α-cell dysfunction in diabetes
Burcelin, R., Knauf, C., & Cani, P. D. (2008) · Diabetes & Metabolism · doi:10.1016/s1262-3636(08)73395-0 · Cited by 66 · see citing works
MafB Is Critical for Glucagon Production and Secretion in Mouse Pancreatic α Cells In Vivo
Katoh, M., Jung, Y., Ugboma, C. M., Shimbo, M., Kuno, A., Basha, W. A. A., Kudo, T., Oishi, H., & Takahashi, S. (2018) · Molecular and Cellular Biology · doi:10.1128/mcb.00504-17 · Cited by 49 · see citing works
Islet-1 Regulates Arx Transcription during Pancreatic Islet α-Cell Development
Liu, J., Hunter, C. S., Du, A., Ediger, B. N., Walp, E. R., Murray, J., Stein, R. W., & May, C. L. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.m111.231670 · Cited by 37 · see citing works
Park, J.-E., Polański, K., Meyer, K. B., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/397042 · Cited by 36 · see citing works
Assessment of batch-correction methods for scRNA-seq data with a new test metricPreprint
Büttner, M., Miao, Z., Wolf, F., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/200345 · Cited by 23 · see citing works
Panoramic stitching of heterogeneous single-cell transcriptomic dataPreprint
Hie, B., Bryson, B. D., & Berger, B. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/371179 · Cited by 21 · see citing works
Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., Rubinstein, N. D., Hao, J., Regev, A., Dulac, C. G., et al. (2018) · Data Archiving and Networked Services (DANS) · doi:10.5061/dryad.8t8s248 · Cited by 21 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
HCA Lung Biological Network, Sungnak, W., Huang, N., Bécavin, C., Berg, M., Queen, R., Litvinukova, M., Talavera-López, C., Maatz, H., Reichart, D., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0868-6 · Cited by 2,501 · see citing works
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231 · see citing works
Single-cell chromatin state analysis with Signac
Stuart, T., Srivastava, A., Madad, S., Lareau, C. A., & Satija, R. (2021) · Nature Methods · doi:10.1038/s41592-021-01282-5 · Cited by 1,769 · see citing works
Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma
Zhang, Q., He, Y., Luo, N., Patel, S. J., Han, Y., Gao, R., Modak, M., Carotta, S., Haslinger, C., Kind, D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.10.003 · Cited by 1,624 · see citing works
Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment
Schulte-Schrepping, J., Reusch, N., Paclik, D., Baßler, K., Schlickeiser, S., Zhang, B., Krämer, B., Krammer, T., Brumhard, S., Bonaguro, L., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.001 · Cited by 1,461 · see citing works
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · see citing works
Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works
scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023) · doi:10.1101/2023.04.30.538439 · Cited by 1,383 · see citing works
“In our benchmarking experiments, we compared scGPT with three popular integration methods: scVI [41], Seurat [42], and Harmony [43].”
MethodInfluentialA benchmark of batch-effect correction methods for single-cell RNA sequencing data
Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113 · see citing works
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193 · see citing works
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059 · see citing works
Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
Ding, J., Adiconis, X., Simmons, S. K., Kowalczyk, M. S., Hession, C. C., Marjanovic, N. D., Hughes, T. K., Wadsworth, M. H., Burks, T., Nguyen, L. T., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0465-8 · Cited by 871 · see citing works
Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches
Guilliams, M., Bonnardel, J., Haest, B., Vanderborght, B., Wagner, C., Remmerie, A., Bujko, A., Martens, L., Thoné, T., Browaeys, R., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.018 · Cited by 985 · see citing works
COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas
Ren, X., Wen, W., Fan, X., Hou, W., Su, B., Cai, P., Li, J., Liu, Y., Tang, F., Zhang, F., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.053 · Cited by 865 · see citing works
Self-citationDominguez, C. X., Müller, S., Keerthivasan, S., Koeppen, H., Hung, J., Gierke, S., Breart, B., Foreman, O., Bainbridge, T. W., Castiglioni, A., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0644 · Cited by 857 · see citing works
Qi, F., Qian, S., Zhang, S., & Zhang, Z. (2020) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2020.03.044 · Cited by 905 · see citing works
Single-cell multi-omics analysis of the immune response in COVID-19
Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID-19 BioResource Collaboration, Stephenson, E., Reynolds, G., Botting, R. A., Calero-Nieto, F. J., Morgan, M. D., Tuong, Z. K., Bach, K., Sungnak, W., Worlock, K. B., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01329-2 · Cited by 742 · see citing works
A Python library for probabilistic analysis of single-cell omics data
Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789 · see citing works
hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data
Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E., & Swarup, V. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100498 · Cited by 773 · see citing works
Hezaveh, K., Shinde, R. S., Klötgen, A., Halaby, M. J., Lamorte, S., Ciudad, M. T., Quevedo, R., Neufeld, L., Liu, Z. Q., Jin, R., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.01.006 · Cited by 746 · see citing works
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
Bravo González-Blas, C., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01938-4 · Cited by 778 · see citing works
Spatial multi-omic map of human myocardial infarction
Kuppe, C., Ramirez Flores, R. O., Li, Z., Hayat, S., Levinson, R. T., Liao, X., Hannani, M. T., Tanevski, J., Wünnemann, F., Nagai, J. S., et al. (2022) · Nature · doi:10.1038/s41586-022-05060-x · Cited by 772 · see citing works
Homeostatic mini-intestines through scaffold-guided organoid morphogenesis
Nikolaev, M., Mitrofanova, O., Broguiere, N., Geraldo, S., Dutta, D., Tabata, Y., Elci, B., Brandenberg, N., Kolotuev, I., Gjorevski, N., et al. (2020) · Nature · doi:10.1038/s41586-020-2724-8 · Cited by 755 · see citing works
Long, Y., Ang, K. S., Li, M., Chong, K. L. K., Sethi, R., Zhong, C., Xu, H., Ong, Z., Sachaphibulkij, K., Chen, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36796-3 · Cited by 700 · see citing works
Pombo Antunes, A. R., Scheyltjens, I., Lodi, F., Messiaen, J., Antoranz, A., Duerinck, J., Kancheva, D., Martens, L., De Vlaminck, K., Van Hove, H., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00789-y · Cited by 704 · see citing works
Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209620 · Cited by 691 · see citing works
“We compare scVI to several methods, including MNN (Haghverdi et al , 2018), Seurat Alignment (Butler et al , 2018), ComBat (Johnson et al , 2007), Harmony (Korsunsky et al , 2019), Scanorama (Hie et al , 2019) and principal component analysis (PCA).”
Denisenko, E., Guo, B. B., Jones, M., Hou, R., de Kock, L., Lassmann, T., Poppe, D., Clément, O., Simmons, R. K., Lister, R., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-02048-6 · Cited by 686 · see citing works
Zhou, Y., Yang, D., Yang, Q., Lv, X., Huang, W., Zhou, Z., Wang, Y., Zhang, Z., Yuan, T., Ding, X., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-20059-6 · Cited by 672 · see citing works
Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury
Kirita, Y., Wu, H., Uchimura, K., Wilson, P. C., & Humphreys, B. D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005477117 · Cited by 650 · see citing works
Smajić, S., Prada-Medina, C. A., Landoulsi, Z., Ghelfi, J., Delcambre, S., Dietrich, C., Jarazo, J., Henck, J., Balachandran, S., Pachchek, S., et al. (2022) · Brain · doi:10.1093/brain/awab446 · Cited by 590 · see citing works
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
Cao, Z.-J., & Gao, G. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01284-4 · Cited by 616 · see citing works
Remmerie, A., Martens, L., Thoné, T., Castoldi, A., Seurinck, R., Pavie, B., Roels, J., Vanneste, B., De Prijck, S., Vanhockerhout, M., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.08.004 · Cited by 592 · see citing works
Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01001-7 · Cited by 602 · see citing works
Joint probabilistic modeling of single-cell multi-omic data with totalVI
Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods · doi:10.1038/s41592-020-01050-x · Cited by 592 · see citing works
Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529 · see citing works
Dick, S. A., Wong, A., Hamidzada, H., Nejat, S., Nechanitzky, R., Vohra, S., Mueller, B., Zaman, R., Kantores, C., Aronoff, L., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.abf7777 · Cited by 538 · see citing works
Association of COVID-19 inflammation with activation of the C5a–C5aR1 axis
Carvelli, J., Demaria, O., Vély, F., Batista, L., Chouaki Benmansour, N., Fares, J., Carpentier, S., Thibult, M.-L., Morel, A., Remark, R., et al. (2020) · Nature · doi:10.1038/s41586-020-2600-6 · Cited by 526 · see citing works
SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis
Wendisch, D., Dietrich, O., Mari, T., von Stillfried, S., Ibarra, I. L., Mittermaier, M., Mache, C., Chua, R. L., Knoll, R., Timm, S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.033 · Cited by 528 · see citing works
Mild respiratory COVID can cause multi-lineage neural cell and myelin dysregulation
Fernández-Castañeda, A., Lu, P., Geraghty, A. C., Song, E., Lee, M.-H., Wood, J., O’Dea, M. R., Dutton, S., Shamardani, K., Nwangwu, K., et al. (2022) · Cell · doi:10.1016/j.cell.2022.06.008 · Cited by 521 · see citing works
Huang, Q., Wu, X., Wang, Z., Chen, X., Wang, L., Lu, Y., Xiong, D., Liu, Q., Tian, Y., Lin, H., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.020 · Cited by 486 · see citing works
Single-cell transcriptomics reveals cell-type-specific diversification in human heart failure
Koenig, A. L., Shchukina, I., Amrute, J., Andhey, P. S., Zaitsev, K., Lai, L., Bajpai, G., Bredemeyer, A., Smith, G., Jones, C., et al. (2022) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00028-6 · Cited by 482 · see citing works
APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes
Blanchard, J. W., Akay, L. A., Davila-Velderrain, J., von Maydell, D., Mathys, H., Davidson, S. M., Effenberger, A., Chen, C.-Y., Maner-Smith, K., Hajjar, I., et al. (2022) · Nature · doi:10.1038/s41586-022-05439-w · Cited by 493 · see citing works
“Harmony, which internally uses the data integration procedure 39 implemented in the software package Harmony (https://github.com/immunogenomics/harmony).”
Autism genes converge on asynchronous development of shared neuron classes
Paulsen, B., Velasco, S., Kedaigle, A. J., Pigoni, M., Quadrato, G., Deo, A. J., Adiconis, X., Uzquiano, A., Sartore, R., Yang, S. M., et al. (2022) · Nature · doi:10.1038/s41586-021-04358-6 · Cited by 439 · see citing works
Computational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472 · see citing works
Comprehensive analysis of single cell ATAC-seq data with SnapATAC
Fang, R., Preissl, S., Li, Y., Hou, X., Lucero, J., Wang, X., Motamedi, A., Shiau, A. K., Zhou, X., Xie, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21583-9 · Cited by 455 · see citing works
Molecular and spatial signatures of mouse brain aging at single-cell resolution
Allen, W. E., Blosser, T. R., Sullivan, Z. A., Dulac, C., & Zhuang, X. (2023) · Cell · doi:10.1016/j.cell.2022.12.010 · Cited by 423 · see citing works
“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…”
InfluentialHwang, W. L., Jagadeesh, K. A., Guo, J. A., Hoffman, H. I., Yadollahpour, P., Reeves, J. W., Mohan, R., Drokhlyansky, E., Van Wittenberghe, N., Ashenberg, O., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01134-8 · Cited by 426 · see citing works
Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy
Chaffin, M., Papangeli, I., Simonson, B., Akkad, A.-D., Hill, M. C., Arduini, A., Fleming, S. J., Melanson, M., Hayat, S., Kost-Alimova, M., et al. (2022) · Nature · doi:10.1038/s41586-022-04817-8 · Cited by 418 · see citing works
Balboa, D., Barsby, T., Lithovius, V., Saarimäki-Vire, J., Omar-Hmeadi, M., Dyachok, O., Montaser, H., Lund, P.-E., Yang, M., Ibrahim, H., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01219-z · Cited by 433 · see citing works
Sadick, J. S., O’Dea, M. R., Hasel, P., Dykstra, T., Faustin, A., & Liddelow, S. A. (2022) · Neuron · doi:10.1016/j.neuron.2022.03.008 · Cited by 415 · see citing works
Chemical reprogramming of human somatic cells to pluripotent stem cells.
Guan, J., Wang, G., Wang, J., Zhang, Z., Fu, Y., Cheng, L., Meng, G., Lyu, Y., Zhu, J., Li, Y., et al. (2022) · Nature · doi:10.1038/s41586-022-04593-5 · Cited by 388 · see citing works
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 · see citing works
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 · see citing works
Identification of a broadly fibrogenic macrophage subset induced by type 3 inflammation
Fabre, T., Barron, A. M. S., Christensen, S. M., Asano, S., Bound, K., Lech, M. P., Wadsworth, M. H., Chen, X., Wang, C., Wang, J., et al. (2023) · Science Immunology · doi:10.1126/sciimmunol.add8945 · Cited by 355 · see citing works
Single-cell proteomics enabled by next-generation sequencing or mass spectrometry
Bennett, H. M., Stephenson, W., Rose, C. M., & Darmanis, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01791-5 · Cited by 351 · see citing works
Corces, M. R., Shcherbina, A., Kundu, S., Gloudemans, M. J., Frésard, L., Granja, J. M., Louie, B. H., Eulalio, T., Shams, S., Bagdatli, S. T., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00721-x · Cited by 358 · see citing works
Bacher, P., Rosati, E., Esser, D., Martini, G. R., Saggau, C., Schiminsky, E., Dargvainiene, J., Schröder, I., Wieters, I., Khodamoradi, Y., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.11.016 · Cited by 314 · see citing works
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 · see citing works
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
Ahern, D. J., Ai, Z., Ainsworth, M., Allan, C., Allcock, A., Angus, B., Ansari, M. A., Arancibia-Cárcamo, C. V., Aschenbrenner, D., Attar, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.012 · Cited by 328 · see citing works
Maturation and persistence of the anti-SARS-CoV-2 memory B cell response
Sokal, A., Chappert, P., Barba-Spaeth, G., Roeser, A., Fourati, S., Azzaoui, I., Vandenberghe, A., Fernandez, I., Meola, A., Bouvier-Alias, M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.050 · Cited by 340 · see citing works
Compromised Humoral Functional Evolution Tracks with SARS-CoV-2 Mortality
Zohar, T., Loos, C., Fischinger, S., Atyeo, C., Wang, C., Slein, M. D., Burke, J., Yu, J., Feldman, J., Hauser, B. M., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.052 · Cited by 297 · see citing works
Inferring and perturbing cell fate regulomes in human brain organoids
Fleck, J. S., Jansen, S. M. J., Wollny, D., Zenk, F., Seimiya, M., Jain, A., Okamoto, R., Santel, M., He, Z., Camp, J. G., et al. (2023) · Nature · doi:10.1038/s41586-022-05279-8 · Cited by 315 · see citing works
Capturing Cardiogenesis in Gastruloids
Rossi, G., Broguiere, N., Miyamoto, M., Boni, A., Guiet, R., Girgin, M., Kelly, R. G., Kwon, C., & Lutolf, M. P. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2020.10.013 · Cited by 306 · see citing works
Andrews, T. S., Atif, J., Liu, J. C., Perciani, C. T., Ma, X., Thoeni, C., Slyper, M., Eraslan, G., Segerstolpe, A., Manuel, J., et al. (2022) · Hepatology Communications · doi:10.1002/hep4.1854 · Cited by 307 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 298 · see citing works
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 · see citing works
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 · see citing works
Xu, H., Ding, J., Porter, C. B. M., Wallrapp, A., Tabaka, M., Ma, S., Fu, S., Guo, X., Riesenfeld, S. J., Su, C., et al. (2019) · Immunity · doi:10.1016/j.immuni.2019.09.004 · Cited by 273 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies
Reichart, D., Lindberg, E. L., Maatz, H., Miranda, A. M. A., Viveiros, A., Shvetsov, N., Gärtner, A., Nadelmann, E. R., Lee, M., Kanemaru, K., et al. (2022) · Science · doi:10.1126/science.abo1984 · Cited by 240 · see citing works
A harmonized atlas of mouse spinal cord cell types and their spatial organization
Russ, D. E., Cross, R. B. P., Li, L., Koch, S. C., Matson, K. J. E., Yadav, A., Alkaslasi, M. R., Lee, D. I., Le Pichon, C. E., Menon, V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25125-1 · Cited by 255 · see citing works
A Map of Transcriptional Heterogeneity and Regulatory Variation in Human Microglia
Young, A. M. H., Kumasaka, N., Calvert, F., Hammond, T. R., Knights, A., Panousis, N., Park, J. S., Schwartzentruber, J., Liu, J., Kundu, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00875-2 · Cited by 213 · see citing works
In vivo CRISPR screens reveal the landscape of immune evasion pathways across cancer
Dubrot, J., Du, P. P., Lane-Reticker, S. K., Kessler, E. A., Muscato, A. J., Mehta, A., Freeman, S. S., Allen, P. M., Olander, K. E., Ockerman, K. M., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01315-x · Cited by 218 · see citing works
Decoding molecular and cellular heterogeneity of mouse nucleus accumbens
Chen, R., Blosser, T. R., Djekidel, M. N., Hao, J., Bhattacherjee, A., Chen, W., Tuesta, L. M., Zhuang, X., & Zhang, Y. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00938-x · Cited by 191 · see citing works
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 · see citing works
SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
Persad, S., Choo, Z.-N., Dien, C., Sohail, N., Masilionis, I., Chaligné, R., Nawy, T., Brown, C. C., Sharma, R., Pe’er, I., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01716-9 · Cited by 222 · see citing works
DISCO: A database of Deeply Integrated human Single-Cell Omics data
Li, M., Zhang, X., Ang, K. S., Ling, J., Sethi, R., Lee, N. Y. S., Ginhoux, F., & Chen, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1020 · Cited by 205 · see citing works
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 · see citing works
Jorfi, M., Park, J., Hall, C. K., Lin, C.-C. J., Chen, M., von Maydell, D., Kruskop, J. M., Kang, B., Choi, Y., Prokopenko, D., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01415-3 · Cited by 214 · see citing works
Early Alzheimer’s disease pathology in human cortex involves transient cell states
Gazestani, V., Kamath, T., Nadaf, N. M., Dougalis, A., Burris, S. J., Rooney, B., Junkkari, A., Vanderburg, C., Pelkonen, A., Gomez-Budia, M., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.005 · Cited by 209 · see citing works
SpaGE: Spatial Gene Enhancement using scRNA-seq
Abdelaal, T., Mourragui, S., Mahfouz, A., & Reinders, M. J. T. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa740 · Cited by 207 · see citing works
Single-cell analysis of prenatal and postnatal human cortical development
Velmeshev, D., Perez, Y., Yan, Z., Valencia, J. E., Castaneda-Castellanos, D. R., Wang, L., Schirmer, L., Mayer, S., Wick, B., Wang, S., et al. (2023) · Science · doi:10.1126/science.adf0834 · Cited by 204 · see citing works
A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma
Pozniak, J., Pedri, D., Landeloos, E., Van Herck, Y., Antoranz, A., Vanwynsberghe, L., Nowosad, A., Roda, N., Makhzami, S., Bervoets, G., et al. (2024) · Cell · doi:10.1016/j.cell.2023.11.037 · Cited by 199 · see citing works
Epigenomic dissection of Alzheimer’s disease pinpoints causal variants and reveals epigenome erosion
Xiong, X., James, B. T., Boix, C. A., Park, Y. P., Galani, K., Victor, M. B., Sun, N., Hou, L., Ho, L.-L., Mantero, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.040 · Cited by 205 · see citing works
Terekhova, M., Swain, A., Bohacova, P., Aladyeva, E., Arthur, L., Laha, A., Mogilenko, D. A., Burdess, S., Sukhov, V., Kleverov, D., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.10.013 · Cited by 199 · see citing works
Single-cell sequencing to multi-omics: technologies and applications
Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 195 · see citing works
“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
Moorman, A., Benitez, E. K., Cambuli, F., Jiang, Q., Mahmoud, A., Lumish, M., Hartner, S., Balkaran, S., Bermeo, J., Asawa, S., et al. (2025) · Nature · doi:10.1038/s41586-024-08150-0 · Cited by 229 · see citing works
Statistical and machine learning methods for spatially resolved transcriptomics data analysis
Zeng, Z., Li, Y., Li, Y., & Luo, Y. (2022) · Genome Biology · doi:10.1186/s13059-022-02653-7 · Cited by 186 · see citing works
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 · see citing works
Delacher, M., Simon, M., Sanderink, L., Hotz-Wagenblatt, A., Wuttke, M., Schambeck, K., Schmidleithner, L., Bittner, S., Pant, A., Ritter, U., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.03.007 · Cited by 164 · see citing works
FOXA2 drives lineage plasticity and KIT pathway activation in neuroendocrine prostate cancer
Han, M., Li, F., Zhang, Y., Dai, P., He, J., Li, Y., Zhu, Y., Zheng, J., Huang, H., Bai, F., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.10.011 · Cited by 166 · see citing works
Targeting TBK1 to overcome resistance to cancer immunotherapy
Sun, Y., Revach, O.-Y., Anderson, S., Kessler, E. A., Wolfe, C. H., Jenney, A., Mills, C. E., Robitschek, E. J., Davis, T. G. R., Kim, S., et al. (2023) · Nature · doi:10.1038/s41586-023-05704-6 · Cited by 174 · see citing works
A comparative atlas of single-cell chromatin accessibility in the human brain
Li, Y. E., Preissl, S., Miller, M., Johnson, N. D., Wang, Z., Jiao, H., Zhu, C., Wang, Z., Xie, Y., Poirion, O., et al. (2023) · Science · doi:10.1126/science.adf7044 · Cited by 173 · see citing works
- 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
- 57
- Citations by year
- 2026: 2634 · 2025: 2714 · 2024: 2218 · 2023: 1370 · 2022: 845 · 2021: 464 · 2020: 196 · 2019: 37 · 2014: 1
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
