Single-cell omics approaches provide high-resolution data on cellular phenotypes, developmental dynamics and communication networks in diverse tissues and conditions. Emerging technologies now measure different modalities of individual cells, such as genomes, epigenomes, transcriptomes and proteomes, in addition to spatial profiling. Combined with analytical approaches, these data open new avenues for accurate reconstruction of gene-regulatory and signaling networks driving cellular identity and function. Here we summarize computational methods for analysis and integration of single-cell omics data across different modalities and discuss their applications, challenges and future directions.
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References
Stuart, T. et al. Cell 177, 1888–1902.e21 (2019).
Welch, J. D. et al. Cell 177, 1873–1887.e17 (2019).
Lopez, R. et al. A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. Preprint at arXiv https://arxiv.org/abs/1905.02269 (2019).
Kester, L. & van Oudenaarden, A. Cell Stem Cell 23, 166–179 (2018).
Ludwig, L. S. et al. Cell 176, 1325–1339.e22 (2019).
Xu, J. et al. eLife 8, e45105 (2019).
McCarthy, D. J. et al. Cardelino: integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants. Preprint at bioRxiv https://doi.org/10.1101/413047 (2018).
Satpathy, A. T. et al. Nat. Med. 24, 580–590 (2018).
Cuomo, A. S. E. et al. Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Preprint at bioRxiv https://doi.org/10.1101/630996 (2018).
Aibar, S. et al. Nat. Methods 14, 1083–1086 (2017).
Hainer, S. J., Bošković, A., McCannell, K. N., Rando, O. J. & Fazzio, T. G. Cell 177, 1319–1329.e11 (2019).
Welch, J. D., Hartemink, A. J. & Prins, J. F. Genome Biol. 18, 138 (2017).
Burdziak, C., Azizi, E., Prabhakaran, S. & Pe’er, D. A nonparametric multi-view model for estimating cell type-specific gene regulatory networks. Preprint at arXiv https://arxiv.org/abs/1902.08138 (2019).
Henriksson, J. Single Cell Methods.: Methods. Mol. Biol. 1979, 395–406 (2019).
Krishnaswamy, S. et al. Science 346, 1250689 (2014).
Qin, X. et al. Single-cell signalling analysis of heterocellular organoids. Preprint at bioRxiv https://doi.org/10.1101/659896 (2019).
Stoeckius, M. et al. Nat. Methods 14, 865–868 (2017).
Peterson, V. M. et al. Nat. Biotechnol. 35, 936–939 (2017).
Gayoso, A. et al. A joint model of RNA expression and surface protein abundance in single cells. Preprint at bioRxiv https://doi.org/10.1101/791947 (2019).
Markowetz, F., Kostka, D., Troyanskaya, O. G. & Spang, R. Bioinformatics 23, i305–i312 (2007).
Pirkl, M. & Beerenwinkel, N. Bioinformatics 34, i964–i971 (2018).
Mayr, U., Serra, D. & Liberali, P. Development 146, dev176727 (2019).
Halpern, K. B. et al. Nature 542, 352–356 (2017).
Karaiskos, N. et al. The Drosophila embryo at single cell transcriptome resolution. Science 358, 194–199 (2017).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Nat. Biotechnol. 33, 495–502 (2015).
Achim, K. et al. Nat. Biotechnol. 33, 503–509 (2015).
Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB v2.0: inferring cell-cell communication from combined expression of multi-subunit receptor-ligand complexes. Preprint at bioRxiv https://doi.org/10.1101/680926 (2019).
Colomé-Tatché, M. & Theis, F. J. Curr. Opin. Syst. Biol. 7, 54–59 (2018).
Packer, J. & Trapnell, C. Trends Genet. 34, 653–665 (2018).
Argelaguet, R. et al. MOFA: a probabilistic framework for comprehensive integration of structured single-cell data. Preprint at bioRxiv https://doi.org/10.1101/837104 (2019).
Acknowledgements
We thank E. Dann and M. Sarkin Jain for careful and critical reading of the manuscript. We are grateful to J. Eliasova for help with the illustrations.
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Efremova, M., Teichmann, S.A. Computational methods for single-cell omics across modalities. Nat Methods 17, 14–17 (2020). https://doi.org/10.1038/s41592-019-0692-4
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DOI: https://doi.org/10.1038/s41592-019-0692-4