Dynamic distribution decomposition for single-cell snapshot time series identifies subpopulations and trajectories during iPSC reprogramming
暂无分享,去创建一个
Will Macnair | Manfred Claassen | Jake P Taylor-King | Asbjørn N Riseth | M. Claassen | A. N. Riseth | J. Taylor-King | W. Macnair | Jake P. Taylor-King | Will Macnair | Manfred Claassen
[1] Joshua L. Proctor,et al. Discovering dynamic patterns from infectious disease data using dynamic mode decomposition , 2015, International health.
[2] Yannis Pantazis,et al. A unified approach for sparse dynamical system inference from temporal measurements , 2017, Bioinform..
[3] Tommi S. Jaakkola,et al. Learning population-level diffusions with generative recurrent networks , 2016, ICML 2016.
[4] Eli R. Zunder,et al. A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. , 2015, Cell stem cell.
[5] P. Maini,et al. A practical guide to stochastic simulations of reaction-diffusion processes , 2007, 0704.1908.
[6] Sean C. Bendall,et al. Wishbone identifies bifurcating developmental trajectories from single-cell data , 2016, Nature Biotechnology.
[7] B. O. Koopman,et al. Hamiltonian Systems and Transformation in Hilbert Space. , 1931, Proceedings of the National Academy of Sciences of the United States of America.
[8] Steven L. Brunton,et al. Dynamic Mode Decomposition with Control , 2014, SIAM J. Appl. Dyn. Syst..
[9] Steven L. Brunton,et al. On dynamic mode decomposition: Theory and applications , 2013, 1312.0041.
[10] Yvan Saeys,et al. A comparison of single-cell trajectory inference methods: towards more accurate and robust tools , 2018, bioRxiv.
[11] D. Lauffenburger,et al. Physicochemical modelling of cell signalling pathways , 2006, Nature Cell Biology.
[12] John von Neumann,et al. Zusatze Zur Arbeit ,,Zur Operatorenmethode... , 1932 .
[13] Y. Saeys,et al. Computational methods for trajectory inference from single‐cell transcriptomics , 2016, European journal of immunology.
[14] A. Kolmogoroff. Über die analytischen Methoden in der Wahrscheinlichkeitsrechnung , 1931 .
[15] M. Mackey,et al. Probabilistic properties of deterministic systems , 1985, Acta Applicandae Mathematicae.
[16] Théorie des probabilités continues , 1906 .
[17] Carsten Carstensen,et al. Remarks around 50 lines of Matlab: short finite element implementation , 1999, Numerical Algorithms.
[18] R. Erban,et al. Reactive boundary conditions for stochastic simulations of reaction–diffusion processes , 2007, Physical biology.
[19] Stefan Klus,et al. On the numerical approximation of the Perron-Frobenius and Koopman operator , 2015, 1512.05997.
[20] D. Gilbarg,et al. Elliptic Partial Differential Equa-tions of Second Order , 1977 .
[21] Clarence W. Rowley,et al. A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition , 2014, Journal of Nonlinear Science.
[22] Sean C. Bendall,et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia , 2013, Nature Biotechnology.
[23] Malgorzata Nowicka,et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. , 2019, F1000Research.
[24] I. Hellmann,et al. Comparative Analysis of Single-Cell RNA Sequencing Methods , 2016, bioRxiv.
[25] Hao Wu,et al. Data-Driven Model Reduction and Transfer Operator Approximation , 2017, J. Nonlinear Sci..
[26] H. Akaike. A new look at the statistical model identification , 1974 .
[27] W. Ziemer. Weakly Differentiable Functions: Sobolev Spaces and Functions of Bounded Variation , 1989 .
[28] Jorge Goncalves,et al. Koopman-Based Lifting Techniques for Nonlinear Systems Identification , 2017, IEEE Transactions on Automatic Control.
[29] A. D. Fokker. Die mittlere Energie rotierender elektrischer Dipole im Strahlungsfeld , 1914 .
[30] Fabian J. Theis,et al. Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series , 2017, bioRxiv.
[31] J. Lygeros,et al. Moment-based inference predicts bimodality in transient gene expression , 2012, Proceedings of the National Academy of Sciences.
[32] Jake P. Taylor-King,et al. Operator Fitting for Parameter Estimation of Stochastic Differential Equations , 2017, 1709.05153.
[33] P. Rigollet,et al. Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming , 2017, bioRxiv.
[34] Manfred Claassen,et al. Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series , 2016, PLoS Comput. Biol..
[35] Daniel T Gillespie,et al. Stochastic simulation of chemical kinetics. , 2007, Annual review of physical chemistry.
[36] Nicholas J. Higham,et al. Functions of matrices - theory and computation , 2008 .
[37] G. Nolan,et al. Mass Cytometry: Single Cells, Many Features , 2016, Cell.
[38] P. Schmid,et al. Dynamic mode decomposition of numerical and experimental data , 2008, Journal of Fluid Mechanics.
[39] Bingni W. Brunton,et al. Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition , 2014, Journal of Neuroscience Methods.
[40] Bengt Fornberg,et al. A primer on radial basis functions with applications to the geosciences , 2015, CBMS-NSF regional conference series in applied mathematics.
[41] S. Ulam. A collection of mathematical problems , 1960 .
[42] Dirk P. Kroese,et al. Kernel density estimation via diffusion , 2010, 1011.2602.
[43] B. Øksendal. Stochastic differential equations : an introduction with applications , 1987 .
[44] Bernd Bodenmiller,et al. Influence of node abundance on signaling network state and dynamics analyzed by mass cytometry , 2017, Nature Biotechnology.
[45] J. Neumann. Zur Operatorenmethode In Der Klassischen Mechanik , 1932 .
[46] Anne E Carpenter,et al. Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.