PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
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Abstract
Abstract Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions ( https://github.com/theislab/paga ). PAGA maps provide interpretable discrete and continuous latent coordinates for both disconnected and continuous structure in data, preserve the global topology of data, allow analyzing data at different resolutions and result in much higher computational efficiency of the typical exploratory data analysis workflow — one million cells take on the order of a minute, a speedup of 130 times compared to UMAP. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, confirm the reconstruction of lineage relations of adult planaria and the zebrafish embryo, benchmark computational performance on a neuronal dataset and detect a biological trajectory in one deep-learning processed image dataset.
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Within the MkPs, the proliferative effects of IFNa were modest in the mutated cells compared to those at baseline.
“However, in contrast to the other myeloid progenitors, within the MkPs (wherein the CALR mutation exerts the greatest phenotypic effects due to the activation of the thrombopoietin receptor 21,54-57 ), the proliferative effects of IFNa were modest in the mutated cells compared to those at baseline ( Fig. 3e ), suggesting that the rate of cell cycle entry of the mutated cells was already near or at”
From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [57]
The Results section on hematopoiesis discusses progenitor-cell trajectories and gene-expression changes, not IFNa treatment or proliferation in CALR-mutated MkPs. Searches for IFNa, CALR, and thrombopoietin-receptor effects found no passage addressing the claimed baseline comparison. In the quoted sentence, the numbered citations appear beside the separate CALR-receptor explanation; the IFNa comparison points to Fig. 3e. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe rate of cell cycle entry of the mutated cells was already near or at the point of saturation due to the CALR mutation.
“However, in contrast to the other myeloid progenitors, within the MkPs (wherein the CALR mutation exerts the greatest phenotypic effects due to the activation of the thrombopoietin receptor 21,54-57 ), the proliferative effects of IFNa were modest in the mutated cells compared to those at baseline ( Fig. 3e ), suggesting that the rate of cell cycle entry of the mutated cells was already near or at”
From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [57]
The Results section discusses PAGA analyses of hematopoietic differentiation and megakaryocyte progenitors, but not CALR-mutated cells or their proliferation. Searches for CALR, IFNa, and saturation of cell-cycle entry found no passage addressing the claim; the copy is not truncated. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe resulting KNN graph captures the underlying topology of the expression data.
“The resulting graph captures the underlying topology of the expression data (Wolf et al, 2019).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Wolf et al, 2019
PAGA has been shown to adequately approximate the topology of the data while coarse-graining the visualization using clusters.
“This tool has been shown to adequately approximate the topology of the data while coarse‐graining the visualization using clusters.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Wolf et al, 2019
UMAP and SPRING's ForceAtlas2 arguably represent the best approximation of the underlying topology.
“UMAP and SPRING's force‐directed layout algorithm ForceAtlas2 arguably represent the best approximation of the underlying topology (Wolf et al, 2019, Supplemental Note 4).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Wolf et al, 2019
PAGA places an edge between cluster nodes whose cells are more similar than expected, using a statistical model for cell cluster interactions.
“Using a statistical model for cell cluster interactions, PAGA places an edge between cluster nodes whose cells are more similar than expected.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Wolf et al, 2019
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Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051 · 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
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · see citing works
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256 · see citing works
A tutorial on spectral clustering
von Luxburg, U. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9033-z · Cited by 10,519 · 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
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881 · see citing works
Self-citationTrapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314 · 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
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · see citing works
Graph drawing by force‐directed placement
Fruchterman, T. M. J., & Reingold, E. M. (1991) · Software: Practice and Experience · doi:10.1002/spe.4380211102 · Cited by 6,352 · see citing works
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070 · 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
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
Street, K., Risso, D., Fletcher, R. B., Das, D., Ngai, J., Yosef, N., Purdom, E., & Dudoit, S. (2018) · BMC Genomics · doi:10.1186/s12864-018-4772-0 · Cited by 3,159 · see citing works
Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098679 · Cited by 2,909 · see citing works
Deep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530 · see citing works
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
Levine, J. H., Simonds, E. F., Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Litvin, O., Fienberg, H. G., Jager, A., Zunder, E. R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.047 · Cited by 2,303 · 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
Self-citationComputing Communities in Large Networks Using Random Walks
Pons, P., & Matthieu Latapy, B. (2005) · Lecture notes in computer science · doi:10.1007/11569596_31 · Cited by 2,147 · see citing works
Single-cell mRNA quantification and differential analysis with Census
Qiu, X., Hill, A. J., Packer, J. S., Lin, D., Ma, Y.-A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4150 · Cited by 1,885 · see citing works
Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F., & Zucker, S. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500334102 · Cited by 1,675 · see citing works
A comparison of single-cell trajectory inference methods
Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670 · see citing works
Amir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550 · 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
Diffusion pseudotime robustly reconstructs lineage branching
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F., & Theis, F. J. (2016) · Nature Methods · doi:10.1038/nmeth.3971 · Cited by 1,385 · see citing works
Self-citationFouss, F., Pirotte, A., Renders, J.-M., & Saerens, M. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.46 · Cited by 1,265 · see citing works
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · see citing works
Single-cell RNA-seq denoising using a deep count autoencoder
Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., & Theis, F. J. (2019) · Nature Communications · doi:10.1038/s41467-018-07931-2 · Cited by 1,084 · see citing works
Self-citationTranscriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors
Paul, F., Arkin, Y., Giladi, A., Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.013 · Cited by 1,082 · see citing works
Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE
Qiu, P., Simonds, E. F., Bendall, S. C., Gibbs, K. D., Bruggner, R. V., Linderman, M. D., Sachs, K., Nolan, G. P., & Plevritis, S. K. (2011) · Nature Biotechnology · doi:10.1038/nbt.1991 · Cited by 956 · see citing works
Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq
Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., & Linnarsson, S. (2011) · Genome Research · doi:10.1101/gr.110882.110 · Cited by 949 · see citing works
Human haematopoietic stem cell lineage commitment is a continuous process
Velten, L., Haas, S. F., Raffel, S., Blaszkiewicz, S., Islam, S., Hennig, B. P., Hirche, C., Lutz, C., Buss, E. C., Nowak, D., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3493 · Cited by 925 · see citing works
Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Simonds, E. F., Chen, T. J., Shenfeld, D. K., Nolan, G. P., & Pe’er, D. (2014) · Cell · doi:10.1016/j.cell.2014.04.005 · Cited by 906 · see citing works
Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis
Farrell, J. A., Wang, Y., Riesenfeld, S. J., Shekhar, K., Regev, A., & Schier, A. F. (2018) · Science · doi:10.1126/science.aar3131 · Cited by 876 · see citing works
Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo
Wagner, D. E., Weinreb, C., Collins, Z. M., Briggs, J. A., Megason, S. G., & Klein, A. M. (2018) · Science · doi:10.1126/science.aar4362 · Cited by 849 · see citing works
A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation
Nestorowa, S., Hamey, F. K., Pijuan Sala, B., Diamanti, E., Shepherd, M., Laurenti, E., Wilson, N. K., Kent, D. G., & Göttgens, B. (2016) · Blood · doi:10.1182/blood-2016-05-716480 · Cited by 768 · see citing works
Self-citationVisualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
Wang, B., Zhu, J., Pierson, E., Ramazzotti, D., & Batzoglou, S. (2017) · Nature Methods · doi:10.1038/nmeth.4207 · Cited by 706 · see citing works
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
Ji, Z., & Ji, H. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw430 · Cited by 624 · see citing works
De 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
Identification of cell types from single-cell transcriptomes using a novel clustering method
Xu, C., & Su, Z. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv088 · Cited by 559 · see citing works
Wishbone identifies bifurcating developmental trajectories from single-cell data
Setty, M., Tadmor, M. D., Reich-Zeliger, S., Angel, O., Salame, T. M., Kathail, P., Choi, K., Bendall, S., Friedman, N., & Pe'er, D. (2016) · Nature Biotechnology · doi:10.1038/nbt.3569 · Cited by 558 · see citing works
Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition
Singh, G., Memoli, F., & Carlsson, G. (2007) · The Eurographics Association · doi:10.2312/spbg/spbg07/091-100 · Cited by 514 · see citing works
Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics
Plass, M., Solana, J., Wolf, F. A., Ayoub, S., Misios, A., Glažar, P., Obermayer, B., Theis, F. J., Kocks, C., & Rajewsky, N. (2018) · Science · doi:10.1126/science.aaq1723 · Cited by 474 · see citing works
Self-citationPopulation snapshots predict early haematopoietic and erythroid hierarchies
Tusi, B. K., Wolock, S. L., Weinreb, C., Hwang, Y., Hidalgo, D., Zilionis, R., Waisman, A., Huh, J. R., Klein, A. M., & Socolovsky, M. (2018) · Nature · doi:10.1038/nature25741 · Cited by 384 · see citing works
Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, A. J., Tanaka, Y., Wilkinson, A. C., Buettner, F., Macaulay, I. C., Jawaid, W., Diamanti, E., et al. (2015) · Nature Biotechnology · doi:10.1038/nbt.3154 · Cited by 380 · see citing works
Self-citationFundamental limits on dynamic inference from single-cell snapshots
Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M., & Klein, A. M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714723115 · Cited by 341 · see citing works
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
Venna, J., Peltonen, J., Nybo, K., Aidos, H., & Samuel Kaski, A. (2010) · doi:10.5555/1756006.1756019 · Cited by 311 · see citing works
SPRING: a kinetic interface for visualizing high dimensional single-cell expression dataPreprint
Weinreb, C., Wolock, S. L., & Klein, A. M. (2017) · bioRxiv · doi:10.1101/090332 · Cited by 310 · see citing works
SPRING: a kinetic interface for visualizing high dimensional single-cell expression data
Weinreb, C., Wolock, S., & Klein, A. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx792 · Cited by 298 · see citing works
Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomicsPreprint
Street, K., Risso, D., Fletcher, R. B., Das, D., Ngai, J., Yosef, N., Purdom, E. A., & Dudoit, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/128843 · Cited by 290 · see citing works
Reconstructing cell cycle and disease progression using deep learning
Eulenberg, P., Köhler, N., Blasi, T., Filby, A. J., Carpenter, A. E., Rees, P. C. T., Theis, F. J., & Wolf, F. A. (2017) · Nature Communications · doi:10.1038/s41467-017-00623-3 · Cited by 272 · see citing works
Self-citationRizvi, A. H., Camara, P. G., Kandror, E. K., Roberts, T. J., Schieren, I., Maniatis, T., & Rabadan, R. (2017) · Nature Biotechnology · doi:10.1038/nbt.3854 · Cited by 248 · see citing works
Wittmann, D. M., Krumsiek, J., Sáez-Rodríguez, J., Lauffenburger, D. A., Klamt, S., & Theis, F. J. (2009) · BMC Systems Biology · doi:10.1186/1752-0509-3-98 · Cited by 241 · see citing works
Self-citationA single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice
Dahlin, J. S., Hamey, F. K., Pijuan-Sala, B., Shepherd, M., Lau, W. W. Y., Nestorowa, S., Weinreb, C., Wolock, S., Hannah, R., Diamanti, E., et al. (2018) · Blood · doi:10.1182/blood-2017-12-821413 · Cited by 233 · see citing works
Self-citationvan Unen, V., Höllt, T., Pezzotti, N., Li, N., Reinders, M. J. T., Eisemann, E., Koning, F., Vilanova, A., & Lelieveldt, B. P. F. (2017) · Nature Communications · doi:10.1038/s41467-017-01689-9 · Cited by 229 · see citing works
Herring, C. A., Banerjee, A., McKinley, E. T., Simmons, A. J., Ping, J., Roland, J. T., Franklin, J. L., Liu, Q., Gerdes, M. J., Coffey, R. J., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2017.10.012 · Cited by 178 · see citing works
Hierarchical Differentiation of Myeloid Progenitors Is Encoded in the Transcription Factor Network
Krumsiek, J., Marr, C., Schroeder, T., & Theis, F. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022649 · Cited by 174 · see citing works
Self-citationA comparison of single-cell trajectory inference methods: towards more accurate and robust toolsPreprint
Saelens, W., Cannoodt, R., Todorov, H., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/276907 · Cited by 98 · see citing works
Principal Graph and Structure Learning Based on Reversed Graph Embedding
Mao, Q., Wang, L., Tsang, I. W.-H., & Sun, Y. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2635657 · Cited by 92 · see citing works
Single cell RNA-seq denoising using a deep count autoencoderPreprint
Eraslan, G., Simon, L. M., Mircea, M., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/300681 · Cited by 86 · see citing works
Self-citationHu, Y., & Shi, L. (2015) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1343 · Cited by 80 · see citing works
Chen, J., Schlitzer, A., Chakarov, S., Ginhoux, F., & Poidinger, M. (2016) · Nature Communications · doi:10.1038/ncomms11988 · Cited by 72 · see citing works
Görgens, A., Ludwig, A., Möllmann, M., Krawczyk, A., Dürig, J., Hanenberg, H., Horn, P. A., & Giebel, B. (2014) · Stem Cell Reports · doi:10.1016/j.stemcr.2014.09.016 · Cited by 50 · see citing works
Hierarchical data organization, clustering and denoising via localized diffusion folders
David, G., & Averbuch, A. Z. (2011) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2011.09.002 · Cited by 46 · see citing works
Robust lineage reconstruction from high-dimensional single-cell data
Giecold, G. C., Marco, E., Garcia, S. P., Trippa, L., & Yuan, G. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw452 · Cited by 38 · see citing works
Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA SequencingPreprint
Lopez, R., Regier, J., Cole, M., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/292037 · Cited by 31 · see citing works
Microbial Exposure Regulates the Development of Anti-Blood Group Antibodies
Arthur, C. M., Sullivan, H. C., Gerner‐Smidt, C., Kamili, N. A., Bennett, A. L., Patel, S. R., Henry, S., & Stowell, S. R. (2016) · Blood · doi:10.1182/blood.v128.22.20.20 · Cited by 17 · see citing works
Pattern-Based Graph Abstraction
Rensink, A., & Zambon, E. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33654-6_5 · Cited by 16 · see citing works
Traag, V. (2015) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.595481 · Cited by 10 · see citing works
Görgens, A., Ludwig, A.-K., Möllmann, M., et al. (2015) · Stem Cell Reports · doi:10.1016/j.stemcr.2015.06.010 · Cited by 6 · see citing works
Introduction to a review series on therapeutic antibodies
Stevenson, F. K., & Bollard, C. M. (2017) · Blood · doi:10.1182/blood-2017-10-798769 · Cited by 6 · see citing works
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- [38]Wolf FA, Hamey F, Plass M, Solana J, Dahlin JS, Göttgens B, Rajewsky N, Simon L, Theis FJ. PAGA: Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. GitHub repository. 2019. https://github.com/theislab/paga. PMC6425583 30890159
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Self-citationA single-cell transcriptomic atlas characterizes ageing tissues in the mouse
The Tabula Muris Consortium, Almanzar, N., Antony, J., Baghel, A. S., Bakerman, I., Bansal, I., Barres, B. A., Beachy, P. A., Berdnik, D., Bilen, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2496-1 · Cited by 1,247 · 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
Self-citationBest 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
“Univariate statistical models, which analyse change in abundance for each cell type individually, such as Poisson regression or Wilcoxon rank-sum tests, may perceive some cell-type population shifts as statistically sound effects, although they are purely a statistical artefact caused by the…”
MethodSelf-citationConstruction of a human cell landscape at single-cell level
Han, X., Zhou, Z., Fei, L., Sun, H., Wang, R., Chen, Y., Chen, H., Wang, J., Tang, H., Ge, W., et al. (2020) · Nature · doi:10.1038/s41586-020-2157-4 · Cited by 1,087 · see citing works
Visualizing structure and transitions in high-dimensional biological data
Moon, K. R., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D. B., Chen, W. S., Yim, K., Elzen, A. V. D., Hirn, M. J., Coifman, R. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0336-3 · Cited by 1,065 · see citing works
Schiebinger, G., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Gould, J., Liu, S., Lin, S., Berube, P., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.006 · Cited by 829 · 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
scGen predicts single-cell perturbation responses
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0494-8 · Cited by 661 · see citing works
Self-citationDissecting cell identity via network inference and in silico gene perturbation
Kamimoto, K., Stringa, B., Hoffmann, C. M., Jindal, K., Solnica-Krezel, L., & Morris, S. A. (2023) · Nature · doi:10.1038/s41586-022-05688-9 · Cited by 677 · 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
Self-citationSpatially resolved cell atlas of the mouse primary motor cortex by MERFISH
Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Cotter, K., Zeng, H., Dong, H., & Zhuang, X. (2021) · Nature · doi:10.1038/s41586-021-03705-x · Cited by 498 · see citing works
A single-cell atlas of the human healthy airways
Deprez, M., Zaragosi, L.-E., Truchi, M., Garcia, S. R., Arguel, M.-J., Lebrigand, K., Paquet, A., Pee’r, D., Marquette, C.-H., Leroy, S., et al. (2019) · doi:10.1101/2019.12.21.884759 · Cited by 493 · see citing works
Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics
Plass, M., Solana, J., Wolf, F. A., Ayoub, S., Misios, A., Glažar, P., Obermayer, B., Theis, F. J., Kocks, C., & Rajewsky, N. (2018) · Science · doi:10.1126/science.aaq1723 · Cited by 474 · see citing works
Self-citationHwang, 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
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
Single-Cell Transcriptomics Meets Lineage Tracing
Kester, L., & van Oudenaarden, A. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.04.014 · Cited by 375 · see citing works
The Specious Art of Single-Cell Genomics
Chari, T., & Pachter, L. (2021) · doi:10.1101/2021.08.25.457696 · Cited by 379 · see citing works
The triumphs and limitations of computational methods for scRNA-seq
Kharchenko, P. V. (2021) · Nature Methods · doi:10.1038/s41592-021-01171-x · Cited by 321 · 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
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
Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · see citing works
Self-citationSingle-cell technologies in hepatology: new insights into liver biology and disease pathogenesis
Ramachandran, P., Matchett, K. P., Dobie, R., Wilson-Kanamori, J. R., & Henderson, N. C. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-020-0304-x · Cited by 253 · see citing works
APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer’s disease
Nguyen, A. T., Wang, K., Hu, G., Wang, X., Miao, Z., Azevedo, J. A., Suh, E., Van Deerlin, V. M., Choi, D., Roeder, K., et al. (2020) · Acta Neuropathologica · doi:10.1007/s00401-020-02200-3 · Cited by 241 · see citing works
Profiling cellular diversity in sponges informs animal cell type and nervous system evolution
Musser, J. M., Schippers, K. J., Nickel, M., Mizzon, G., Kohn, A. B., Pape, C., Ronchi, P., Papadopoulos, N., Tarashansky, A. J., Hammel, J. U., et al. (2021) · Science · doi:10.1126/science.abj2949 · Cited by 242 · see citing works
Self-citationGeneration of a p16 Reporter Mouse and Its Use to Characterize and Target p16high Cells In Vivo
Omori, S., Wang, T.-W., Johmura, Y., Kanai, T., Nakano, Y., Kido, T., Susaki, E. A., Nakajima, T., Shichino, S., Ueha, S., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.09.006 · Cited by 194 · see citing works
Temporal modelling using single-cell transcriptomics
Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195 · see citing works
Ma, S., Wang, S., Ye, Y., Ren, J., Chen, R., Li, W., Li, J., Zhao, L., Zhao, Q., Sun, G., et al. (2022) · Cell Stem Cell · doi:10.1016/j.stem.2022.04.017 · Cited by 189 · see citing works
Gorin, G., Fang, M., Chari, T., & Pachter, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010492 · Cited by 186 · see citing works
Zheng, X., Wang, X., Cheng, X., Liu, Z., Yin, Y., Li, X., Huang, Z., Wang, Z., Guo, W., Ginhoux, F., et al. (2023) · Nature Cancer · doi:10.1038/s43018-023-00599-8 · Cited by 172 · see citing works
Absence of microglia promotes diverse pathologies and early lethality in Alzheimer’s disease mice
Kiani Shabestari, S., Morabito, S., Danhash, E. P., McQuade, A., Sanchez, J. R., Miyoshi, E., Chadarevian, J. P., Claes, C., Coburn, M. A., Hasselmann, J., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110961 · Cited by 167 · see citing works
Continuous and Discrete Neuron Types of the Adult Murine Striatum
Stanley, G., Gokce, O., Malenka, R. C., Südhof, T. C., & Quake, S. R. (2020) · Neuron · doi:10.1016/j.neuron.2019.11.004 · Cited by 158 · see citing works
A human multi-lineage hepatic organoid model for liver fibrosis
Guan, Y., Enejder, A., Wang, M., Fang, Z., Cui, L., Chen, S.-Y., Wang, J., Tan, Y., Wu, M., Chen, X., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26410-9 · Cited by 149 · see citing works
Adeno-to-squamous transition drives resistance to KRAS inhibition in LKB1 mutant lung cancer
Tong, X., Patel, A. S., Kim, E., Li, H., Chen, Y., Li, S., Liu, S., Dilly, J., Kapner, K. S., Zhang, N., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.01.012 · Cited by 142 · see citing works
Network inference with Granger causality ensembles on single-cell transcriptomics
Deshpande, A., Chu, L.-F., Stewart, R., & Gitter, A. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110333 · Cited by 130 · see citing works
Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology
Kukanja, P., Langseth, C. M., Rubio Rodríguez-Kirby, L. A., Agirre, E., Zheng, C., Raman, A., Yokota, C., Avenel, C., Tiklová, K., Guerreiro-Cacais, A. O., et al. (2024) · Cell · doi:10.1016/j.cell.2024.02.030 · Cited by 124 · see citing works
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
González-Blas, C. B., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2022) · doi:10.1101/2022.08.19.504505 · Cited by 778 · see citing works
scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108 · see citing works
Self-citationTrzupek, D., Dunstan, M., Cutler, A. J., Lee, M., Godfrey, L., Jarvis, L., Rainbow, D. B., Aschenbrenner, D., Jones, J. L., Uhlig, H. H., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00756-z · Cited by 105 · see citing works
CellOracle: Dissecting cell identity via network inference and in silico gene perturbation
Kamimoto, K., Hoffmann, C. M., & Morris, S. A. (2020) · doi:10.1101/2020.02.17.947416 · Cited by 81 · see citing works
Early satellite cell communication creates a permissive environment for long-term muscle growth
Murach, K. A., Peck, B. D., Policastro, R. A., Vechetti, I. J., Van Pelt, D. W., Dungan, C. M., Denes, L. T., Fu, X., Brightwell, C. R., Zentner, G. E., et al. (2021) · iScience · doi:10.1016/j.isci.2021.102372 · Cited by 75 · see citing works
Single-nucleus RNA-seq2 reveals functional crosstalk between liver zonation and ploidy
Richter, M. L., Deligiannis, I. K., Yin, K., Danese, A., Lleshi, E., Coupland, P., Vallejos, C. A., Matchett, K. P., Henderson, N. C., Colome-Tatche, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24543-5 · Cited by 75 · see citing works
Exploring single cells in space and time during tissue development, homeostasis and regeneration
Mayr, U., Serra, D., & Liberali, P. (2019) · Development · doi:10.1242/dev.176727 · Cited by 63 · see citing works
Weng, C., Xi, J., Li, H., Cui, J., Gu, A., Lai, S., Leskov, K., Ke, L., Jin, F., & Li, Y. (2020) · Nature Metabolism · doi:10.1038/s42255-020-00314-2 · Cited by 65 · see citing works
Graph representation learning for single-cell biology
Hetzel, L., Fischer, D. S., Günnemann, S., & Theis, F. J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.05.008 · Cited by 55 · see citing works
Self-citationZhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Zeng, H., Dong, H., & Zhuang, X. (2020) · doi:10.1101/2020.06.04.105700 · Cited by 44 · see citing works
CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks
Buterez, D., Bica, I., Tariq, I., Andrés-Terré, H., & Liò, P. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab804 · Cited by 44 · see citing works
Gene regulatory network reconfiguration in direct lineage reprogramming
Kamimoto, K., Adil, M. T., Jindal, K., Hoffmann, C. M., Kong, W., Yang, X., & Morris, S. A. (2023) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.11.010 · Cited by 42 · see citing works
Peng, G., Cui, G., Ke, J., & Jing, N. (2020) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120219-083220 · Cited by 42 · see citing works
Differentiation reveals latent features of aging and an energy barrier in murine myogenesis
Kimmel, J. C., Yi, N., Roy, M., Hendrickson, D. G., & Kelley, D. R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109046 · Cited by 42 · see citing works
Squidiff: predicting cellular development and responses to perturbations using a diffusion model
He, S., Zhu, Y., Tavakol, D. N., Ye, H., Lao, Y.-H., Zhu, Z., Xu, C., Chauhan, S., Garty, G., Tomer, R., et al. (2026) · Nature Methods · doi:10.1038/s41592-025-02877-y · Cited by 38 · see citing works
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2018) · doi:10.1101/478503 · Cited by 21 · see citing works
Self-citationMorrell, E. D., Holton, S. E., Lawrance, M., Orlov, M., Franklin, Z., Mitchem, M. A., DeBerg, H., Gersuk, V. H., Garay, A., Barnes, E., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43223-0 · Cited by 20 · see citing works
Lyu, T., Liu, Y., Li, B., Xu, R., Guo, J., & Zhu, D. (2023) · Biology Direct · doi:10.1186/s13062-023-00390-w · Cited by 18 · see citing works
Interpreting Single-Cell and Spatial Omics Data Using Deep Neural Network Training Dynamics
Karin, J., Mintz, R., Raveh, B., & Nitzan, M. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00721-5 · Cited by 16 · see citing works
Liu, B., Polack, M., Coudray, N., Claudio Quiros, A., Sakellaropoulos, T., Le, H., Karimkhan, A., Crobach, A. S. L. P., van Krieken, J. H. J. M., Yuan, K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57541-y · Cited by 25 · see citing works
“Next, we generated a PAGA plot where each HPC is a node connected by lines based on vector similarity.”
InfluentialA comparative transcriptomic analysis of mouse demyelination models and multiple sclerosis lesions
Aboelnour, E. L., Vanoverbeke, V. R., Maupin, E. A., Hatfield, M. M., & Adams, K. L. (2026) · Nature Communications · doi:10.1038/s41467-026-72383-y · Cited by 4 · see citing works
Agboraw, E., Haese-Hill, W., Hentzschel, F., Briggs, E., Aghabi, D., Heawood, A., Harding, C. R., Shiels, B., Crouch, K., Somma, D., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf091 · Cited by 4 · see citing works
Type 1 interferon perturbates clonal competition by reshaping human blood development
Lama, C., Isakov, D., Rosenberg, S., Quijada-Álamo, M., Saurty-Seerunghen, M. S., Moein, S., Nozais, M., Abera, T.-A., Sakaguchi, O., Totwani, M., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02751-3 · Cited by 3 · see citing works
- Authors
- Wolf, F. A., Hamey, F. K., Plass, M., Solana, J., Dahlin, J. S., Göttgens, B., Rajewsky, N., Simon, L., & Theis, F. J.
- Affiliations
- Helmholtz Munich (DE)
- Wellcome/MRC Cambridge Stem Cell Institute (GB)
- Max Delbrück Center (DE)
- Karolinska University Hospital (SE)
- Corresponding author
- Theis, Fabian J.
- ORCID
- Venue
- Genome Biology
- Published
- 2019-03-19
- Volume
- 20
- Issue
- 1
- Pages
- 59-59
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC6425583
- OpenAlex
- W2950372712
- Cited by
- 1,698
- References
- 72
- Citations by year
- 2026: 226 · 2025: 301 · 2024: 324 · 2023: 225 · 2022: 246 · 2021: 174 · 2020: 138 · 2019: 53 · 2018: 10 · 2017: 1
- FWCI
- 52.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Wellcome Trust (206328/Z/17/Z, 079895/Z/06/Z, 203151/Z/16/Z)
- Deutsches Zentrum für Herz-Kreislaufforschung (DZHK BER1.2VD)
- Cancer Research UK
- Deutsche Forschungsgemeinschaft (RA 838/5-1, Subproject A17)
- Vetenskapsrådet
- Blood Cancer UK
- National Institutes of Health
- Medical Research Council (MC_PC_12009)
- Cambridge Institute for Medical Research, University of Cambridge
- National Institute of Diabetes and Digestive and Kidney Diseases (R24DK106766)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Paga, graph abstraction, single-cell RNA sequencing, cell trajectory inference, cell clustering, topology of manifolds, cell-type resolution, hematopoietic development, Tricladida, zebrafish embryos, neuronal connectivity, computational efficiency
- MeSH terms
- Algorithms, Animals, Computer Graphics, Embryo, Nonmammalian, Hematopoietic Stem Cells, Humans, Planarians, Reference Standards, Software, Zebrafish, Sequence Analysis, RNA, Gene Expression Regulation, Developmental, Computational Biology, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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