StabMap: Mosaic single cell data integration using non-overlapping features
Open accessAbstract
Currently available single cell -omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilises mapping of single cell data by exploiting the non-overlapping features. StabMap is a flexible approach that first infers a mosaic data topology, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates disjoint mosaic data integration, and enables the use of novel spatial gene expression features for mapping dissociated single cell data onto a spatial transcriptomic reference.
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StabMap, Cobolt, MultiVI and Bridge Integration in Seurat v5 integrate unmatched datasets using separately collected multi-omics datasets.
“These methods include StabMap170, Cobolt171, MultiVI172 and ‘Bridge Integration’ in Seurat v5 (ref. 173).”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [170]
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Cites
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488 · see citing works
Simultaneous epitope and transcriptome measurement in single cells
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098 · see citing works
Spatially resolved, highly multiplexed RNA profiling in single cells
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969 · 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
Self-citationMcCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw777 · Cited by 1,852 · 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
Self-citationBenchmarking 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
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
Self-citationChromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146 · see citing works
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091 · see citing works
Self-citationA 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
Self-citationHastie, T., & Stuetzle, W. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478797 · Cited by 940 · see citing works
Differential abundance testing on single-cell data using k-nearest neighbor graphs
Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D., & Marioni, J. C. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01033-z · Cited by 938 · see citing works
Self-citationBBKNN: 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
Shah, S., Lubeck, E., Zhou, W., & Cai, L. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.001 · Cited by 671 · see citing works
The human body at cellular resolution: the NIH Human Biomolecular Atlas Program
HuBMAP Consortium, Writing Group, Snyder, M. P., Lin, S., Posgai, A., Atkinson, M., Regev, A., Rood, J., Rozenblatt-Rosen, O., Gaffney, L., et al. (2019) · Nature · doi:10.1038/s41586-019-1629-x · Cited by 595 · 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
Self-citationHastie, T., & Stuetzle, W. (1989) · Journal of the American Statistical Association · doi:10.2307/2289936 · Cited by 352 · see citing works
Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis
Lohoff, T., Ghazanfar, S., Missarova, A. M., Koulena, N., Pierson, N. G., Griffiths, J. A., Bardot, E., Eng, C.-H. L., Tyser, R. C. V., Argelaguet, R., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-01006-2 · Cited by 349 · see citing works
Self-citationOrmestad, M., Astorga, J., Landgren, H., Wang, T., Johansson, B. R., Miura, N., & Carlsson, P. (2006) · Development · doi:10.1242/dev.02252 · Cited by 240 · see citing works
Zhang, Z., Huynh, T., & Baldini, A. (2006) · Development · doi:10.1242/dev.02539 · Cited by 211 · see citing works
Lin, Y., Ghazanfar, S., Wang, K. Y. X., Gagnon-Bartsch, J. A., Lo, K., Su, X., Han, Z., Ormerod, J. T., Speed, T. P., Yang, P., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820006116 · Cited by 170 · see citing works
Self-citationBenchmarking atlas-level data integration in single-cell genomicsPreprint
Luecken, M. D., Büttner, M., Chaichoompu, K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.22.111161 · Cited by 122 · see citing works
Diverse Routes toward Early Somites in the Mouse Embryo
Guibentif, C., Griffiths, J. A., Imaz-Rosshandler, I., Ghazanfar, S., Nichols, J., Wilson, V. A., Göttgens, B., & Marioni, J. C. (2020) · Developmental Cell · doi:10.1016/j.devcel.2020.11.013 · Cited by 86 · see citing works
Self-citationKriebel, A. R., & Welch, J. D. (2022) · Nature Communications · doi:10.1038/s41467-022-28431-4 · Cited by 85 · see citing works
Investigating higher-order interactions in single-cell data with scHOT
Ghazanfar, S., Lin, Y., Su, X., Lin, D. M., Patrick, E., Han, Z., Marioni, J. C., & Yang, J. Y. H. (2020) · Nature Methods · doi:10.1038/s41592-020-0885-x · Cited by 84 · see citing works
Self-citationMultiMAP: dimensionality reduction and integration of multimodal data
Jain, M. S., Polański, K., Conde, C. D., Chen, X. X., Park, J.-E., Mamanova, L., Knights, A. J., Botting, R. A., Stephenson, E., Haniffa, M. A., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02565-y · Cited by 77 · see citing works
Computational methods for the integrative analysis of single-cell data
Forcato, M., Romano, O., & Bicciato, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa042 · Cited by 71 · see citing works
Bowling, S., Sritharan, D., Osorio, F. G., Nguyen, M., Cheung, P., Rodriguez-Fraticelli, A., Patel, S. H., Yuan, W.-C., Fujiwara, Y., Li, B. E., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.018 · Cited by 71 · see citing works
Ustiyan, V., Bolte, C., Zhang, Y., Han, L., Xu, Y. J., Yutzey, K. E., Zorn, A. M., Kalin, T. V., Shannon, J. M., & Kalinichenko, V. V. (2018) · Developmental Biology · doi:10.1016/j.ydbio.2018.08.011 · Cited by 65 · see citing works
MultiMAP: Dimensionality Reduction and Integration of Multimodal DataPreprint
Jain, M. S., Polański, K., Conde, C. D., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.16.431421 · Cited by 14 · see citing works
Investigating higher order interactions in single cell data with scHOTPreprint
Ghazanfar, S., Lin, Y., Su, X., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/841593 · Cited by 12 · see citing works
Self-citationCurated single cell multimodal landmark datasets for R/Bioconductor
Eckenrode, K. B., Righelli, D., Ramos, M., Argelaguet, R., Vanderaa, C., Geistlinger, L., Culhane, A. C., Gatto, L., Carey, V. J., Morgan, M. T., et al. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011324 · Cited by 9 · see citing works
Nuclei Isolation from Tissue for 10x Multiome v1Preprint
Shi, M., Weimer, A. K. K., & Snyder, M. (2021) · doi:10.17504/protocols.io.bukqnuvw · Cited by 5 · see citing works
Curated Single Cell Multimodal Landmark Datasets for R/BioconductorPreprint
Eckenrode, K. B., Righelli, D., Ramos, M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.27.466079 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Kriebel, A. R. & Welch, J. D. Nonnegative matrix factorization integrates single-cell multi- omic datasets with partially overlapping features. bioRxiv (2021).
Cited by
Sorted by citations
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
“To capture the vast range of antigens, somatic V(D)J recombination generates highly diverse AIR sequences across the population of B and T cells in an individual (Fig.”
BackgroundThe technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037 · 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
Microfluidics-free single-cell genomics with templated emulsification
Clark, I. C., Fontanez, K. M., Meltzer, R. H., Xue, Y., Hayford, C. E., May-Zhang, A. A., D’Amato, C., Osman, A., Zhang, J. Q., Hettige, P., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01685-z · Cited by 225 · see citing works
scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection
Zhang, Z., Sun, H., Mariappan, R., Chen, X. X., Chen, X., Jain, M. S., Efremova, M., Teichmann, S. A., Rajan, V., & Zhang, X. (2023) · Nature Communications · doi:10.1038/s41467-023-36066-2 · Cited by 60 · see citing works
Du, J., Cai, Z., & Roeder, K. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2214414119 · Cited by 48 · see citing works
WebAtlas pipeline for integrated single-cell and spatial transcriptomic data
Li, T., Horsfall, D., Basurto-Lozada, D., Roberts, K., Prete, M., Lawrence, J. E. G., He, P., Tuck, E., Moore, J., Yoldaş, A., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02371-x · Cited by 33 · see citing works
Self-citationFouché, A., & Zinovyev, A. Y. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1191961 · Cited by 13 · see citing works
van Buijtenen, E., Janssen, W., Vink, P. M., Habraken, M., Wingens, L. J. A., van Elsas, A., Huck, W. T. S., van Buggenum, J. A. G. L., & van Eenennaam, H. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100492 · Cited by 9 · see citing works
scTopoGAN: unsupervised manifold alignment of single-cell data
Singh, A., Biharie, K., Reinders, M. J. T., Mahfouz, A. E., & Abdelaal, T. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad171 · Cited by 7 · see citing works
Combining single-cell ATAC and RNA sequencing for supervised cell annotation
Gill, J., Dasgupta, A., Manry, B., & Markuzon, N. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06084-6 · Cited by 2 · see citing works
Bouwers, E. G. (2011) · PUB – Publications at Bielefeld University (Bielefeld University)
van Buijtenen, E., Janssen, W., Vink, P. M., Habraken, M., Wingens, L. J. A., van Elsas, A., Huck, W. T. S., van Buggenum, J. A. G. L., & van Eenennaam, H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.31.486501
Zhao, Y., Liu, P., Hu, L., Shi, Y., Li, Y., Wang, Y., Zeng, Z., Du, H., & Qian, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.10.540147
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Yan, X., Chen, J., Zheng, R., & Li, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.28.625798
Wang, Z., Wu, Z., & Deng, M. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06239-5
- Authors
- Ghazanfar, S., Guibentif, C., & Marioni, J. C.
- Affiliations
- European Bioinformatics Institute (GB)
- University of Gothenburg (SE)
- Corresponding author
- Ghazanfar, Shila
- Marioni, John C.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2022-02-25
- OpenAlex
- W4214637479
- Cited by
- 16
- References
- 32
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 9 · 2022: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Funders
- Wellcome Trust (A29580)
- Cancer Research UK (A:29580, C9545, C9545/A29580)
- University of Cambridge
- Vetenskapsrådet (2017-06278, A29580)
- Göteborgs Universitet
- National Institutes of Health (OT2OD026673)
- Cancer Research UK Cambridge Institute, University of Cambridge (A29580, C9545/A29580)
- 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 data integration, spatial transcriptomics, spatial gene expression, single-cell omics
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