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Gene trajectory inference for single-cell records by optimum transport metrics
Technology tamfitronics
Mahdessian, D. et al. Spatiotemporal dissection of the cell cycle with single-cell proteogenomics. Nature 590649–654 (2021).
Scialdone, A. et al. Computational assignment of cell-cycle stage from single-cell transcriptome records. Programs 8554–61 (2015).
Skinner, S. O. et al. Single-cell prognosis of transcription kinetics throughout the cell cycle. eLife 5e12175 (2016).
Cao, J., Zhou, W., Steemers, F., Trapnell, C. & Shendure, J. Sci-destiny characterizes the dynamics of gene expression in single cells. Nat. Biotechnol. 38980–988 (2020).
Qu, R. et al. Decomposing a deterministic route to mesenchymal niche formation by two intersecting morphogen gradients. Dev. Cell 571053–1067 (2022).
Macaulay, I. C. et al. Single-cell RNA-sequencing reveals a exact spectrum of differentiation in hematopoietic cells. Cell Find. 14966–977 (2016).
Chu, L.-F. et al. Single-cell RNA-seq reveals unique regulators of human embryonic stem cell differentiation to definitive endoderm. Genome Biol. 17173 (2016).
Chen, R., Wu, X., Jiang, L. & Zhang, Y. Single-cell RNA-seq reveals hypothalamic cell selection. Cell Find. 183227–3241 (2017).
Avenue, Okay. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19477 (2018).
Cao, J. et al. The one-cell transcriptional panorama of mammalian organogenesis. Nature 566496–502 (2019).
Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference thru a topology preserving draw of single cells. Genome Biol. 2059 (2019).
Van den Berge, Okay. et al. Trajectory-basically based differential expression prognosis for single-cell sequencing records. Nat. Common. 111201 (2020).
Deconinck, L., Cannoodt, R., Saelens, W., Deplancke, B. & Saeys, Y. Most contemporary advances in trajectory inference from single-cell omics records. Curr. Opin. Syst. Biol. 27100344 (2021).
Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference solutions. Nat. Biotechnol. 37547–554 (2019).
Lange, M. et al. CellRank for directed single-cell destiny mapping. Nat. Programs 19159–170 (2022).
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Programs 14979–982 (2017).
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Programs 13845–848 (2016).
Setty, M. et al. Characterization of cell destiny probabilities in single-cell records with Palantir. Nat. Biotechnol. 37451–460 (2019).
Lönnberg, T. et al. Single-cell RNA-seq and computational prognosis utilizing temporal mixture modeling resolves Th1/Tfh destiny bifurcation in malaria. Sci. Immunol. 2eaal2192 (2017).
Tritschler, S. et al. Ideas and barriers for discovering out developmental trajectories from single cell genomics. Pattern 146dev170506 (2019).
Trapnell, C. et al. The dynamics and regulators of cell destiny choices are printed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32381–386 (2014).
Ruijtenberg, S. & van den Heuvel, S. Coordinating cell proliferation and differentiation: antagonism between cell cycle regulators and cell form-explicit gene expression. Cell Cycle 15196–212 (2016).
Rougny, A., Paulevé, L., Teboul, M. & Delaunay, F. A detailed draw of coupled circadian clock and cell cycle with qualitative dynamics validation. BMC Bioinformatics 22240 (2021).
Gupta, Okay. et al. Single-cell prognosis reveals a hair follicle dermal niche molecular differentiation trajectory that begins outdated to morphogenesis. Dev. Cell 4817–31 (2019).
Sood, P. et al. Modular, cascade-bask in transcriptional program of regeneration in stentor. eLife 11e80778 (2022).
Zhu, H., Zhao, S. D., Ray, A., Zhang, Y. & Li, X. A comprehensive temporal patterning gene community in Drosophila medulla neuroblasts printed by single-cell RNA sequencing. Nat. Common. 131247 (2022).
Li, J. et al. Systematic reconstruction of molecular cascades regulating GP pattern utilizing single-cell RNA-seq. Cell Find. 151467–1480 (2016).
Huizing, G.-J., Peyré, G. & Cantini, L. Optimum transport improves cell–cell similarity inference in single-cell omics records. Bioinformatics 382169–2177 (2022).
Bellazzi, R., Codegoni, A., Gualandi, S., Nicora, G. & Vercesi, E. The gene mover’s distance: single-cell similarity by utilizing optimum transport. Preprint at arXiv 10.48550/arXiv.2102.01218 (2021).
Orlova, D.Y. et al. Earth mover’ s distance (EMD): an exact metric for comparing biomarker expression ranges in cell populations. PLoS ONE 11e0151859 (2016).
Schiebinger, G. et al. Optimum-transport prognosis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176928–943 (2019).
Zhang, S., Afanassiev, A., Greenstreet, L., Matsumoto, T. & Schiebinger, G. Optimum transport prognosis reveals trajectories in well-liked-pronounce programs. PLoS Comput. Biol. 17e1009466 (2021).
Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic records. Nat. Common. 112084 (2020).
Moriel, N. et al. NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimum transport. Nat. Protoc. 164177–4200 (2021).
Demetci, P., Santorella, R., Sandstede, B., Noble, W. S. & Singh, R. SCOT: single-cell multi-omics alignment with optimum transport. J. Comput. Biol. 293–18 (2022).
Coifman, R. R. & Lafon, S. Diffusion maps. Appl. Comput. Harmon. Anal. 215–30 (2006).
Singer, A. From graph to manifold Laplacian: the convergence charge. App. Comput. Harmon. Anal. 21128–134 (2006).
Tacke, F. & Randolph, G. J. Migratory destiny and differentiation of blood monocyte subsets. Immunobiology 211609–618 (2006).
Van de Veerdonk, F. L. & Netea, M. G. Diversity: an indicator of monocyte society. Immunity 33289–291 (2010).
Patel, A. A. et al. The destiny and lifespan of human monocyte subsets in well-liked pronounce and systemic irritation. J. Exp. Med. 2141913–1923 (2017).
Chitu, V. & Stanley, E. R. Colony-stimulating element-1 in immunity and irritation. Curr. Opin. Immunol. 1839–48 (2006).
Imhof, B. A. & Dunon, D. Leukocyte migration and adhesion. Adv. Immunol. 58345–416 (1995).
Ghebrehiwet, B., Hosszu, Okay. Okay., Valentino, A., Ji, Y. & Peerschke, E. I. Monocyte expressed macromolecular C1 and C1q receptors as molecular sensors of hazard: implications in SLE. Entrance. Immunol. 5278 (2014).
Heger, L. et al. Subsets of CD1c+ DCs: dendritic cell versus monocyte lineage. Entrance. Immunol. 11559166 (2020).
Higashi, N. et al. The macrophage C-form lectin explicit for galactose/N-acetylgalactosamine is an endocytic receptor expressed on monocyte-derived immature dendritic cells. J. Biol. Chem. 27720686–20693 (2002).
Myung, P., Andl, T. & Atit, R. The origins of pores and skin selection: classes from dermal fibroblasts. Pattern 149dev200298 (2022).
Chen, D., Jarrell, A., Guo, C., Lang, R. & Atit, R. Dermal β-catenin project per epidermal Wnt ligands is required for fibroblast proliferation and hair follicle initiation. Pattern 1391522–1533 (2012).
Fu, J. & Hsu, W. Epidermal Wnt controls hair follicle induction by orchestrating dynamic signaling crosstalk between the epidermis and dermis. J. Make investments. Dermatol. 133890–898 (2013).
Hastie, T. J. Generalized Additive Gadgetspp. 249–307 (Routledge, 2017).
Picket, S. mgcv: Mixed GAM Computation Automobile with GCV/AIC/REML Smoothness Estimation (College of Bath, 2012).
Pott, S. & Lieb, J. D. Single-cell ATAC–seq: strength in numbers. Genome Biol. 16172 (2015).
Ståhl, P. L. et al. Visualization and prognosis of gene expression in tissue sections by spatial transcriptomics. Science 35378–82 (2016).
Macaulay, I. C., Ponting, C. P. & Voet, T. Single-cell multiomics: rather a lot of measurements from single cells. Trends Genet. 33155–168 (2017).
Balasubramanian, M. & Schwartz, E. L. The isomap algorithm and topological balance. Science 2957 (2002).
Bernstein, M., De Silva, V., Langford, J. C. & Tenenbaum, J. B. Graph Approximations to Geodesics on Embedded Manifolds Technical Document (Department of Psychology, Stanford College, 2000).
Dassule, H. R., Lewis, P., Bei, M., Maas, R. & McMahon, A. P. Sonic hedgehog regulates enhance and morphogenesis of the teeth. Pattern 1274775–4785 (2000).
Picket worker, A. C., Rao, S., Wells, J. M., Campbell, Okay. & Lang, R. A. Generation of mice with a conditional null allele for Wntless. Genesis 48554–558 (2010).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression records. Nat. Biotechnol. 33495–502 (2015).
Santos, A., Wernersson, R. & Jensen, L. J. Cyclebase 3.0: a multi-organism database on cell-cycle rules and phenotypes. Nucleic Acids Res. 43D1140–D1144 (2015).
Liu, Z. et al. Reconstructing cell cycle pseudo time-assortment by utilizing single-cell transcriptome records. Nat. Common. 822 (2017).
Günesdogan, U., Jäckle, H. & Herzig, A. Histone provide regulates s share timing and cell cycle progression. eLife 3e02443 (2014).
Moon, Okay. R. et al. Visualizing structure and transitions in excessive-dimensional organic records. Nat. Biotechnol. 371482–1492 (2019).
Picket, S. & Picket, M. S. Kit ‘mgcv’. pupil.google.com/citations?view_op=view_citation&hl=it&user=EskiIyEAAAAJ&citation_for_view=EskiIyEAAAAJ:kh2fBNsKQNwC (2015).
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states thru dynamical modeling. Nat. Biotechnol. 381408–1414 (2020).
