Poincaré maps for analyzing complex hierarchies in single-cell data.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
11 06 2020
11 06 2020
Historique:
received:
26
07
2019
accepted:
25
05
2020
entrez:
13
6
2020
pubmed:
13
6
2020
medline:
25
8
2020
Statut:
epublish
Résumé
The need to understand cell developmental processes spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry, a suboptimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation issue we propose Poincaré maps, a method that harness the power of hyperbolic geometry into the realm of single-cell data analysis. Often understood as a continuous extension of trees, hyperbolic geometry enables the embedding of complex hierarchical data in only two dimensions while preserving the pairwise distances between points in the hierarchy. This enables the use of our embeddings in a wide variety of downstream data analysis tasks, such as visualization, clustering, lineage detection and pseudotime inference. When compared to existing methods - unable to address all these important tasks using a single embedding - Poincaré maps produce state-of-the-art two-dimensional representations of cell trajectories on multiple scRNAseq datasets.
Identifiants
pubmed: 32528075
doi: 10.1038/s41467-020-16822-4
pii: 10.1038/s41467-020-16822-4
pmc: PMC7290024
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2966Références
Moignard, V. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33, 269 (2015).
doi: 10.1038/nbt.3154
Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).
doi: 10.1016/j.cell.2015.11.013
Olsson, A. et al. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature 537, 698 (2016).
doi: 10.1038/nature19348
Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20–e31 (2016).
doi: 10.1182/blood-2016-05-716480
Ferrell Jr, J. E. Bistability, bifurcations, and waddington’s epigenetic landscape. Curr. Biol. 22, R458–R466 (2012).
doi: 10.1016/j.cub.2012.03.045
Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331 (2017).
doi: 10.1038/nature21350
Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015).
doi: 10.1093/bioinformatics/btv325
Wolf, F. A. et al. Paga: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59 (2019).
doi: 10.1186/s13059-019-1663-x
Moon, K. R. et al. Visualizing structure and transitions for biological data exploration. Available at SSRN 3155891 (2018).
Wang, B., Zhu, J., Pierson, E., Ramazzotti, D. & Batzoglou, S. Visualization and analysis of single-cell rna-seq data by kernel-based similarity learning. Nat. Methods 14, 414 (2017).
doi: 10.1038/nmeth.4207
Ding, J., Condon, A. & Shah, S. P. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat. Commun. 9, 2002 (2018).
doi: 10.1038/s41467-018-04368-5
Amodio, M. et al. Exploring single-cell data with deep multitasking neural networks. Nat. Methods 16, 1139–1145 (2019).
Levine, J. H. et al. Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
doi: 10.1016/j.cell.2015.05.047
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053 (2018).
doi: 10.1038/s41592-018-0229-2
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979 (2017).
doi: 10.1038/nmeth.4402
Haghverdi, L., Buettner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. methods 13, 845 (2016).
doi: 10.1038/nmeth.3971
Maaten, Lvd & Hinton, G. Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579–2605 (2008).
McInnes, L. & Healy, J. Umap: uniform manifold approximation and projection for dimension reduction. https://arxiv.org/abs/1802.03426 (2018).
Gromov, M. Metric Structures for Riemannian and Non-riemannian Spaces (Springer Science & Business Media, 2007).
Nickel, M. & Kiela, D. Poincaré embeddings for learning hierarchical representations. In Advances in Neural Information Processing Systems 30. (eds. Guyon, I. et al.) 6338–6347 (Curran Associates, Inc., 2017).
Jacomy, M., Venturini, T., Heymann, S. & Bastian, M. Forceatlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PloS ONE 9, e98679 (2014).
doi: 10.1371/journal.pone.0098679
Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492 (2019).
doi: 10.1038/s41587-019-0336-3
Ding, J. & Regev, A. Deep generative model embedding of single-cell RNA-seq profiles on hyperspheres and hyperbolic spaces. https://doi.org/10.1101/853457 (2019).
Magwene, P. M., Lizardi, P. & Kim, J. Reconstructing the temporal ordering of biological samples using microarray data. Bioinformatics 19, 842–850 (2003).
doi: 10.1093/bioinformatics/btg081
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381 (2014).
doi: 10.1038/nbt.2859
Von Luxburg, U. A tutorial on spectral clustering. Stat. Comput. 17, 395–416 (2007).
doi: 10.1007/s11222-007-9033-z
Chebotarev, P. Y. & Shamis, E. V. The Matrix-Forest Theorem and Measuring Relations in Small Social Groups. Automat. Remote Control 58, 1505–1514 (1997).
Tenenbaum, J. B., De Silva, V. & Langford, J. C. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000).
doi: 10.1126/science.290.5500.2319
Belkin, M. & Niyogi, P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373–1396 (2003).
doi: 10.1162/089976603321780317
Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008, P10008 (2008).
doi: 10.1088/1742-5468/2008/10/P10008
Plass, M. et al. Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Science 360, eaaq1723 (2018).
doi: 10.1126/science.aaq1723
Packer, J. S. et al. A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science 365, eaax1971 (2019).
doi: 10.1126/science.aax1971
Murphy, K. & Weaver, C. Janeway’s Immunobiology (Garland Science, 2016).
Lee, J. A. & Verleysen, M. Scale-independent quality criteria for dimensionality reduction. Pattern Recognit. Lett. 31, 2248–2257 (2010).
doi: 10.1016/j.patrec.2010.04.013
Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).
doi: 10.1126/science.1198704
Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637 (2016).
doi: 10.1038/nbt.3569
Marco, E. et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc. Natl Acad. Sci. 111, E5643–E5650 (2014).
doi: 10.1073/pnas.1408993111
Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with spade. Nat. Biotechnol. 29, 886 (2011).
doi: 10.1038/nbt.1991
Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human b cell development. Cell 157, 714–725 (2014).
doi: 10.1016/j.cell.2014.04.005
Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 1–12 (2017).
doi: 10.1038/s41467-016-0009-6
Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).
doi: 10.1016/j.cell.2015.04.044
Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).
doi: 10.1016/j.cell.2018.05.060
Parekh, S., Ziegenhain, C., Vieth, B., Enard, W. & Hellmann, I. zumis-a fast and flexible pipeline to process rna sequencing data with umis. Gigascience 7, giy059 (2018).
doi: 10.1093/gigascience/giy059
Luecken, M. D. & Theis, F. J. Current best practices in single-cell rna-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).
Svensson, V. et al. Power analysis of single-cell rna-sequencing experiments. Nat. Methods 14, 381 (2017).
doi: 10.1038/nmeth.4220
Chebotarev, P. Spanning forests and the golden ratio. Discret. Appl. Math. 156, 813-821 (2008).
doi: 10.1016/j.dam.2007.08.030
Bonnabel, S. Stochastic gradient descent on Riemannian manifolds. IEEE Trans. Autom. Contr. 58, 2217–2229 (2013).
doi: 10.1109/TAC.2013.2254619
Nickel, M. & Kiela, D. Learning continuous hierarchies in the lorentz model of hyperbolic geometry. In Proceedings of the 35th International Conference on Machine Learning. (eds. Dy, J. & Krause, A.) 3779–3788 (PMLR, Sweden, 2018).
Johnson, J., Douze, M. & Jégou, H. Billion-scale similarity search with GPUs. IEEE Trans. Big Data https://doi.org/10.1109/TBDATA.2019.2921572 (2019).