The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term ‘data integration’ has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods.
Your institute does not have access to this article
Open Access articles citing this article.
scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously
Genome Biology Open Access 27 June 2022
Nature Communications Open Access 18 June 2022
Genome Biology Open Access 30 May 2022
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Navin, N. E. The first five years of single-cell cancer genomics and beyond. Genome Res. 25, 1499–1507 (2015).
Peng, G., Cui, G., Ke, J. & Jing, N. Using single-cell and spatial transcriptomes to understand stem cell lineage specification during early embryo development. Annu. Rev. Genomics Hum. Genet. 21, 163–181 (2020).
Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility, DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018).
Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell https://doi.org/10.1016/j.cell.2020.09.056 (2020).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016).
Swanson, E. et al. TEA-seq: a trimodal assay for integrated single cell measurement of transcription, epitopes, and chromatin accessibility. Preprint at bioRxiv https://doi.org/10.1101/2020.09.04.283887 (2020).
Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).
Macaulay, I. C., Ponting, C. P. & Voet, T. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 33, 155–168 (2017).
Chappell, L., Russell, A. J. C. & Voet, T. Single-cell (multi) omics technologies. Annu. Rev. Genomics Hum. Genet. 19, 15–41 (2018).
Hao, Y., Hao, S., Andersen-Nissen, E. & Mauck, W. M. Integrated analysis of multimodal single-cell data. Preprint at bioRxiv https://doi.org/10.1101/2020.10.12.335331 (2020).
Forcato, M., Romano, O. & Bicciato, S. Computational methods for the integrative analysis of single-cell data. Brief. Bioinform. 22, 20–29 (2021).
Ma, A., McDermaid, A., Xu, J., Chang, Y. & Ma, Q. Integrative methods and practical challenges for single-cell multi-omics. Trends Biotechnol. 38, 1007–1022 (2020).
Colomé-Tatché, M. & Theis, F. J. Statistical single cell multi-omics integration. Curr. Opin. Syst. Biol. 7, 54–59 (2018).
Lähnemann, D. et al. Eleven grand challenges in single-cell data science. Genome Biol. 21, 31 (2020).
Cheow, L. F. et al. Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat. Methods 13, 833–836 (2016).
Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0290-0 (2019).
Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 (2019).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Polański, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965 (2020).
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).
Barkas, N. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16, 695–698 (2019).
Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018).
Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).
Johansen, N. & Quon, G. scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data. Genome Biol. 20, 166 (2019).
Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Preprint at bioRxiv https://doi.org/10.1101/2020.05.22.111161 (2020).
Schadt, E. E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003).
Cantini, L. et al. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat. Commun. 12, 124 (2021).
Buettner, F., Pratanwanich, N., McCarthy, D. J., Marioni, J. C. & Stegle, O. f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq. Genome Biol. 18, 212 (2017).
Nica, A. C. & Dermitzakis, E. T. Expression quantitative trait loci: present and future. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120362 (2013).
Westra, H.-J. & Franke, L. From genome to function by studying eQTLs. Biochim. Biophys. Acta 1842, 1896–1902 (2014).
Hu, Y. et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol. 17, 88 (2016).
Liu, L. et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat. Commun. 10, 470 (2019).
Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).
Packer, J. & Trapnell, C. Single-cell multi-omics: an engine for new quantitative models of gene regulation. Trends Genet. 34, 653–665 (2018).
Zhou, X. & Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821–824 (2012).
Lippert, C. et al. FaST linear mixed models for genome-wide association studies. Nat. Methods 8, 833–835 (2011).
Yu, J. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208 (2006).
Price, A. L., Zaitlen, N. A., Reich, D. & Patterson, N. New approaches to population stratification in genome-wide association studies. Nat. Rev. Genet. 11, 459–463 (2010).
Henderson, C. R. Applications of Linear Models in Animal Breeding Univ. Guelph (1984).
Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).
Furlotte, N. A., Kang, H. M., Ye, C. & Eskin, E. Mixed-model coexpression: calculating gene coexpression while accounting for expression heterogeneity. Bioinformatics 27, i288–i294 (2011).
Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).
Fairfax, B. P. et al. Genetics of gene expression in primary immune cells identifies cell type–specific master regulators and roles of HLA alleles. Nat. Genet. 44, 502–510 (2012).
van der Wijst, M. G. P. et al. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50, 493–497 (2018).
Cuomo, A. S. E. et al. Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nat. Commun. 11, 810 (2020).
Strober, B. J. et al. Dynamic genetic regulation of gene expression during cellular differentiation. Science 364, 1287–1290 (2019).
Wills, Q. F. et al. Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat. Biotechnol. 31, 748–752 (2013).
Sarkar, A. K. et al. Discovery and characterization of variance QTLs in human induced pluripotent stem cells. PLoS Genet. 15, e1008045 (2019).
van der Wijst, M. et al. The single-cell eQTLGen consortium. eLife 9, e52155 (2020).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
Jerber, J. et al. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat. Genet. 53, 304–312 (2021).
Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).
Rubin, A. J. et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176, 361–376 (2019).
Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).
Schraivogel, D. et al. Targeted Perturb-seq enables genome-scale genetic screens in single cells. Nat. Methods 17, 629–635 (2020).
Gasperini, M. et al. A genome-wide framework for mapping gene regulation via cellular genetic screens. Cell 176, 1516 (2019).
Mimitou, E. P. et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16, 409–412 (2019).
Argelaguet, R. et al. Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature 576, 487–491 (2019).
Argelaguet, R. et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 21, 111 (2020).
Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116 (2020).
Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).
Xu, C., Tao, D. & Xu, C. A survey on multi-view learning. Preprint at https://arxiv.org/abs/1304.5634 (2013).
Argelaguet, R. et al. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).
Lock, E. F., Hoadley, K. A., Marron, J. S. & Nobel, A. B. Joint and Individual Variation Explained (JIVE) for integrated analysis of multiple data types. Ann. Appl. Stat. 7, 523–542 (2013).
Singh, A. et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 35, 3055–3062 (2019).
Meng, C., Kuster, B., Culhane, A. C. & Gholami, A. A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics 15, 162 (2014).
Klami, A., Virtanen, S., Leppäaho, E. & Kaski, S. Group factor analysis. IEEE Trans. Neural Netw. Learn. Syst. 26, 2136–2147 (2015).
Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).
Luo, C. et al. Single nucleus multi-omics links human cortical cell regulatory genome diversity to disease risk variants. Preprint at bioRxiv https://doi.org/10.1101/2019.12.11.873398 (2019).
Wang, C. et al. Integrative analyses of single-cell transcriptome and regulome using MAESTRO. Genome Biol. 21, 198 (2020).
Welch, J. D., Hartemink, A. J. & Prins, J. F. MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol. 18, 138 (2017).
Liu, J., Huang, Y., Singh, R., Vert, J.-P. & Noble, W. S. Jointly embedding multiple single-cell omics measurements. Preprint at bioRxiv https://doi.org/10.1101/644310 (2019).
Zheng, H. et al. Cross-domain fault diagnosis using knowledge transfer strategy: a review. IEEE Access 7, 129260–129290 (2019).
Ruder, S., Peters, M. E., Swayamdipta, S. & Wolf, T. Transfer learning in natural language processing. in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials 15–18 https://doi.org/10.18653/v1/n19-5004 (2019).
Wang, J. et al. Data denoising with transfer learning in single-cell transcriptomics. Nat. Methods 16, 875–878 (2019).
Lieberman, Y., Rokach, L. & Shay, T. CaSTLe—classification of single cells by transfer learning: harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments. PLoS ONE 13, e0205499 (2018).
Lotfollahi, M., Naghipourfar, M., Luecken, M. D. & Khajavi, M. Query to reference single-cell integration with transfer learning. Preprint at bioRxiv https://doi.org/10.1101/2020.07.16.205997 (2020).
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).
Eng, C.-H. L., Shah, S., Thomassie, J. & Cai, L. Profiling the transcriptome with RNA SPOTs. Nat. Methods 14, 1153–1155 (2017).
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
Giacomello, S. et al. Spatially resolved transcriptome profiling in model plant species. Nat. Plants 3, 17061 (2017).
Pijuan-Sala, B. et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566, 490–495 (2019).
Marioni, J. C. & Arendt, D. How single-cell genomics is changing evolutionary and developmental biology. Annu. Rev. Cell Dev. Biol. 33, 537–553 (2017).
Shafer, M. E. R. Cross-species analysis of single-cell transcriptomic data. Front. Cell Dev. Biol. 7, 175 (2019).
Vintsyuk, T. K. Speech discrimination by dynamic programming. Cybernetics 4, 52–57 (1972).
Cacchiarelli, D. et al. Aligning single-cell developmental and reprogramming trajectories identifies molecular determinants of myogenic reprogramming outcome. Cell Syst. 7, 258–268 (2018).
Alpert, A., Moore, L. S., Dubovik, T. & Shen-Orr, S. S. Alignment of single-cell trajectories to compare cellular expression dynamics. Nat. Methods 15, 267–270 (2018).
Do, V. H. et al. Dynamic pseudo-time warping of complex single-cell trajectories. Preprint at bioRxiv https://doi.org/10.1101/522672 (2019).
Velten, B., Braunger, J. M., Arnol, D., Argelaguet, R. & Stegle, O. Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO. Preprint at bioRxiv https://doi.org/10.1101/2020.11.03.366674 (2020).
Kanton, S. et al. Organoid single-cell genomic atlas uncovers human-specific features of brain development. Nature 574, 418–422 (2019).
Gabaldón, T. & Koonin, E. V. Functional and evolutionary implications of gene orthology. Nat. Rev. Genet. 14, 360–366 (2013).
Arendt, D. et al. The origin and evolution of cell types. Nat. Rev. Genet. 17, 744–757 (2016).
Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. gkab043 (2021).
Chidester, B., Zhou, T. & Ma, J. SpiceMix: integrative single-cell spatial modeling for inferring cell identity. Preprint at bioRxiv https://doi.org/10.1101/2020.11.29.383067 (2021).
Kleshchevnikov, V. et al. Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2020.11.15.378125 (2020).
Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020).
Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00830-w (2021).
Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).
Arnol, D., Schapiro, D., Bodenmiller, B., Saez-Rodriguez, J. & Stegle, O. Modeling cell–cell interactions from spatial molecular data with spatial variance component analysis. Cell Rep. 29, 202–211 (2019).
Rood, J. E. et al. Toward a common coordinate framework for the human body. Cell 179, 1455–1467 (2019).
Camp, J. G., Platt, R. & Treutlein, B. Mapping human cell phenotypes to genotypes with single-cell genomics. Science 365, 1401–1405 (2019).
Nieto, P., Elosua-Bayes, M. M., Trincado, J. L. & Marchese, D. A single-cell tumor immune atlas for precision oncology. Preprint at bioRxiv https://doi.org/10.1101/2020.10.26.354829 (2020).
Keener, A. B. Single-cell sequencing edges into clinical trials. Nat. Med. 25, 1322–1326 (2019).
Rajewsky, N. et al. LifeTime and improving European healthcare through cell-based interceptive medicine. Nature https://doi.org/10.1038/s41586-020-2715-9 (2020).
Shalek, A. K. & Benson, M. Single-cell analyses to tailor treatments. Sci. Transl. Med. 9, eaan4730 (2017).
Hotelling, H. Relations between two sets of variates. Biometrika 28, 321–377 (1936).
Meng, C. et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform. 17, 628–641 (2016).
Jin, S., Zhang, L. & Nie, Q. scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles. Genome Biol. 21, 25 (2020).
Stark, S. G. et al. SCIM: universal single-cell matching with unpaired feature sets. Bioinformatics 36, i919–i927 (2020).
Cao, K., Bai, X., Hong, Y. & Wan, L. Unsupervised topological alignment for single-cell multi-omics integration. Bioinformatics 36, i48–i56 (2020).
Duren, Z. et al. Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations. Proc. Natl Acad. Sci. USA 115, 7723–7728 (2018).
Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).
Vieira Braga, F. A. et al. A cellular census of human lungs identifies novel cell states in health and in asthma. Nat. Med. 25, 1153–1163 (2019).
Travaglini, K. J. et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature 587, 619–625 (2020).
Wang, A. et al. Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes. eLife 9, e62522 (2020).
Muraro, M. J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394 (2016).
Lawlor, M. et al. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Res. 27, 208–222 (2017).
Segerstolpe, Å. et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 24, 593–607 (2016).
Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016).
Cao, J. et al. A human cell atlas of fetal gene expression. Science 370, eaba7721 (2020).
Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020).
Bravo González-Blas, C. et al. Identification of genomic enhancers through spatial integration of single‐cell transcriptomics and epigenomics. Mol. Syst. Biol. 16, e9438 (2020).
Pijuan-Sala, B. et al. Single-cell chromatin accessibility maps reveal regulatory programs driving early mouse organogenesis. Nat. Cell Biol. 22, 487–497 (2020).
Preisel, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).
Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).
Lee, D.-S. et al. Simultaneous profiling of 3D genome structure and DNA methylation in single human cells. Nat. Methods 16, 999–1006 (2019).
Johnstone, I. M. & Titterington, D. M. Statistical challenges of high-dimensional datal. Philos. Trans. A Math. Phys. Eng. Sci. 367, 4237–4253 (2009).
Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017).
Hicks, S. C., Townes, F. W., Teng, M. & Irizarry, R. A. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 19, 562–578 (2018).
Buettner, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).
Svensson, V., Vento-Tormo, R. & Teichmann, S. A. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13, 599–604 (2018).
Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).
Vallejos, C. A., Marioni, J. C. & Richardson, S. BASiCS: Bayesian analysis of single-cell sequencing data. PLoS Comput. Biol. 11, e1004333 (2015).
Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).
R.A. and A.S.E.C. are supported by a PhD fellowship from the EMBL International PhD Programme. O.S. is supported by core funding from EMBL and the DKFZ, as well as the BMBF, the Volkswagen Foundation and the European Union (810296). J.C.M. acknowledges core funding from EMBL and core support from Cancer Research UK (C9545/A29580).
The authors declare no competing interests.
Peer review information Nature Biotechnology thanks Carl Herrmann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Argelaguet, R., Cuomo, A.S.E., Stegle, O. et al. Computational principles and challenges in single-cell data integration. Nat Biotechnol 39, 1202–1215 (2021). https://doi.org/10.1038/s41587-021-00895-7
scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously
Genome Biology (2022)
Genome Biology (2022)
Genome Biology (2022)
Nature Reviews Genetics (2022)
Nature Communications (2022)