[1] |
V, Exponential scaling of single-cell RNA-seq in the past decade, Nat. Protoc., 13, 599-604 (2018) · doi:10.1038/nprot.2017.149 |
[2] |
G, Massively parallel digital transcriptional profiling of single cells, Nat. Commun., 8, 14049 (2017) · doi:10.1038/ncomms14049 |
[3] |
T. Stuart, R. Satija, Integrative single-cell analysis, Nat. Rev. Genet., 20 (2019), 257-272. http://www.nature.com/articles/s41576-019-0093-7 |
[4] |
W, A comparison of single-cell trajectory inference methods: towards more accurate and robust tools, Nat. Biotechn., 37, 547-554 (2019) · doi:10.1038/s41587-019-0071-9 |
[5] |
V, Challenges in unsupervised clustering of single-cell RNA-seq data, Nat. Rev. Genet., 20, 273-282 (2019) · doi:10.1038/s41576-018-0088-9 |
[6] |
R, Transition state characteristics during cell differentiation, PLoS Comput. Biol., 14, e1006405 (2018) · doi:10.1371/journal.pcbi.1006405 |
[7] |
E, Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape, Proc. Nat. Academy Sci., 111, E5643-E5650 (2014) · doi:10.1073/pnas.1408993111 |
[8] |
A, Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome, Nat. Commun., 8, 15599 (2017) · doi:10.1038/ncomms15599 |
[9] |
S, ScEpath: Energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data, Bioinformatics, 34, 2077-2086 (2018) · doi:10.1093/bioinformatics/bty058 |
[10] |
J, HopLand: Single-cell pseudotime recovery using continuous Hopfield network-based modeling of Waddington’s epigenetic landscape, Bioinformatics, 33, i102-i109 (2017) · doi:10.1093/bioinformatics/btx232 |
[11] |
M. Zwiessele, N. D. Lawrence, Topslam: Waddington Landscape Recovery for Single Cell Experiments, preprint, BioRxiv, 2017: 057778. https://doi.org/10.1101/057778 |
[12] |
H, Modeling acute myeloid leukemia in a continuum of differentiation states, Lett. Biomath., 5, S69-S98 (2018) |
[13] |
S, A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation, Blood, 128, 20-32 (2016) · doi:10.1182/blood-2016-05-716480 |
[14] |
F, Transcriptional heterogeneity and lineage commitment in myeloid progenitors, Cell, 163, 1663-1677 (2015) · doi:10.1016/j.cell.2015.11.013 |
[15] |
L, Diffusion maps for high-dimensional single-cell analysis of differentiation data, Bioinformatics, 31, 2989-2998 (2015) · doi:10.1093/bioinformatics/btv325 |
[16] |
M, Identifying and removing the cell-cycle effect from single-cell rna-sequencing data, Sci. Rep., 6, 33892 (2016) · doi:10.1038/srep33892 |
[17] |
J, Potential landscape and flux framework of nonequilibrium networks: robustness, dissipation, and coherence of biochemical oscillations, Proc. Nat. Acad. Sci., 105, 12271-12276 (2008) · doi:10.1073/pnas.0800579105 |
[18] |
Z, The generalized cross entropy method, with applications to probability density estimation, Methodol. Comput. Appl. Probab., 13, 1-27 (2011) · Zbl 1207.94041 · doi:10.1007/s11009-009-9133-7 |
[19] |
M, A structured population model of cell differentiation, SIAM J. Appl. Math., 71, 1918-1940 (2011) · Zbl 1235.35030 · doi:10.1137/100816584 |
[20] |
C, Fundamental limits on dynamic inference from single-cell snapshots, Proc. Nat. Acad. Sci., 115, E2467-E2476 (2018) · doi:10.1073/pnas.1714723115 |
[21] |
G, Generative modeling of single-cell time series with prescient enables prediction of cell trajectories with interventions, Nat. Commun., 12, 3222 (2021) · doi:10.1038/s41467-021-23518-w |
[22] |
L. C. Evans, An Introduction to Stochastic Differential Equations, American Mathematical Society, 2014. |
[23] |
F, 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 |
[24] |
L. C. Evans, Partial Differential Equations, 2nd edition, American Mathematical Society, 2010. · Zbl 1194.35001 |
[25] |
R, State-transition analysis of time-sequential gene expression identifies critical points that predict development of acute myeloid leukemia, Cancer Res., 80, 3157-3169 (2020) · doi:10.1158/0008-5472.CAN-20-0354 |
[26] |
A. W. Bowman, A. Azzalini, Applied Smoothing Techniques for Data Analysis, Oxford University Press Inc., New York, 1997. · Zbl 0889.62027 |
[27] |
Q, Cbf \(\beta \)-smmhc creates aberrant megakaryocyte-erythroid progenitors prone to leukemia initiation in mice, Blood, 128, 1503-1515 (2016) · doi:10.1182/blood-2016-01-693119 |
[28] |
P, Fusion between transcription factor cbf beta/pebp2 beta and a myosin heavy chain in acute myeloid leukemia, Science, 261, 1041-1044 (1993) · doi:10.1126/science.8351518 |
[29] |
P, Identification of the chimeric protein product of the cbfb-myh11 fusion gene in inv(16) leukemia cells, Genes Chromosomes Cancer, 16, 77-87 (1996) · doi:10.1002/(SICI)1098-2264(199606)16:2<77::AID-GCC1>3.0.CO;2-%23 |
[30] |
L, The fusion gene cbfb-myh11 blocks myeloid differentiation and predisposes mice to acute myelomonocytic leukaemia, Nat. Genet., 23, 144-146 (1999) · doi:10.1038/13776 |
[31] |
Y, Cbf \(\beta \)-smmhc induces distinct abnormal myeloid progenitors able to develop acute myeloid leukemia, Cancer Cell, 9, 57-68 (2006) · doi:10.1016/j.ccr.2005.12.014 |
[32] |
Y, Cbf \(\beta \)-smmhc impairs differentiation of common lymphoid progenitors and reveals an essential role for runx in early b-cell development, Blood, 111, 1543-1551 (2008) · doi:10.1182/blood-2007-07-104422 |
[33] |
C, Elucidation of the phenotypic, functional, and molecular topography of a myeloerythroid progenitor cell hierarchy, Cell Stem Cell, 1, 428-442 (2007) · doi:10.1016/j.stem.2007.07.005 |
[34] |
K, A clonogenic common myeloid progenitor that gives rise to all myeloid lineages, Nature, 404, 193-197 (2000) · doi:10.1038/35004599 |
[35] |
S, A 17-gene stemness score for rapid determination of risk in acute leukaemia, Nature, 540, 433-437 (2016) · doi:10.1038/nature20598 |
[36] |
C, GPR56 identifies primary human acute myeloid leukemia cells with high repopulating potential in vivo, Blood, 127, 2018-2027 (2017) · doi:10.1182/blood-2015-11-683649 |
[37] |
T, CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer, PLOS Comput. Biol., 14, e1006935 (2019) · doi:10.1371/journal.pcbi.1006935 |
[38] |
M, Leveraging single cell RNA sequencing experiments to model intra-tumor heterogeneity, Clin. Cancer Inf., 3, 1-10 (2019) · doi:10.1200/CCI.18.00074 |
[39] |
E, Single-cell RNA sequencing to explore immune cell heterogeneity, Nat. Rev. Immunol., 18, 35-45 (2018) · doi:10.1038/nri.2017.76 |
[40] |
G, Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming, Cell, 176, 928-943 (2019) · doi:10.1016/j.cell.2019.01.006 |
[41] |
G, Reconstructing developmental landscapes and trajectories from single-cell data, Curr. Opin. Syst. Biol., 27, 100351 (2021) · doi:10.1016/j.coisb.2021.06.002 |
[42] |
M, Characterization of cell fate probabilities in single-cell data with Palantir, Nat. Biotechnol., 37, 451-460 (2019) · doi:10.1038/s41587-019-0068-4 |
[43] |
S, Inferring cell-state transition dynamics from lineage trees and endpoint single-cell measurements, Cell Syst., 3, 419-433 (2016) · doi:10.1016/j.cels.2016.10.015 |
[44] |
D, Inferring population dynamics from single-cell RNA-sequencing time series data, Nat. Biotechnol., 37, 461-468 (2019) · doi:10.1038/s41587-019-0088-0 |
[45] |
Q, Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data, PLOS Comput. Biol., 18, e1009821 (2022) · doi:10.1371/journal.pcbi.1009821 |
[46] |
A. Sharma, E. Y. Cao, V. Kumar, X. Zhang, H. S. Leong, A. M. L. Wong, et al., Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy, Nat. Commun., https://doi.org/10.1038/s41467-018-07261-3. |
[47] |
M, Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq, Nat. Commun., 9, 3588 (2018) · doi:10.1038/s41467-018-06052-0 |
[48] |
G, RNA velocity of single cells, Nature, 560, 494-498 (2018) · doi:10.1038/s41586-018-0414-6 |
[49] |
G. Eraslan, Ž. Avsec, J. Gagneur, F. J. Theis, Deep learning : new computational modelling techniques for genomics, Nat. Rev. Genet., 20 (2019). https://doi.org/10.1038/s41576-019-0122-6 |
[50] |
N, Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI, Sci. Rep., 9, 10063 (2019) · doi:10.1038/s41598-019-46296-4 |
[51] |
R, The 2019 mathematical oncology roadmap, Phys. Biol., 16, 4 (2019) · doi:10.1088/1478-3975/ab1a09 |
[52] |
X, Mapping transcriptomic vector fields of single cells, Cell, 185, 690-711 (2022) · doi:10.1016/j.cell.2021.12.045 |
[53] |
S, Comprehensive evaluation of noise reduction methods for single-cell rna sequencing data, Briefings Bioinf., 23, bbab565 (2022) · doi:10.1093/bib/bbab565 |
[54] |
M, Cell fate decision as high-dimensional critical state transition, PLoS Biol., 14, 1-28 (2016) · doi:10.1371/journal.pbio.2000640 |
[55] |
C, Landscape reveals critical network structures for sharpening gene expression boundaries, BMC Syst. Biol., 12, 67 (2018) · doi:10.1186/s12918-018-0595-5 |
[56] |
J, Determining relative dynamic stability of cell states using boolean network model, Sci. Rep., 8, 12077 (2018) · doi:10.1038/s41598-018-30544-0 |
[57] |
B, Estimating human hematopoietic stem cell kinetics using granulocyte telomere lengths, Exp. Hematol., 32, 1040-1050 (2004) · doi:10.1016/j.exphem.2004.07.023 |
[58] |
E, Kinetics of granulopoiesis, Clin. Haematol., 8, 351-370 (1979) |
[59] |
S, Cell cycle regulation of hematopoietic stem or progenitor cells, Int. J. Hematol., 103, 487-497 (2016) · doi:10.1007/s12185-016-1984-4 |
[60] |
E, Cell cycle regulation in hematopoietic stem cells, J. Cell Biol., 195, 709-720 (2011) · doi:10.1083/jcb.201102131 |
[61] |
T, The impact of CD34+ cell dose on engraftment after SCTs: personalized estimates based on mathematical modeling, Bone Marrow Transp., 49, 30-37 (2014) · doi:10.1038/bmt.2013.138 |
[62] |
R, Geometric diffusions as a tool for harmonic analysis and structure definition of data : Diffusion maps, Proc. Natl. Acad. Sci., 102, 7426-7431 (2005) · Zbl 1405.42043 · doi:10.1073/pnas.0500334102 |
[63] |
L, Diffusion pseudotime robustly reconstructs lineage branching, Nat. Methods, 13, 845-848 (2016) · doi:10.1038/nmeth.3971 |
[64] |
M, Bastian, 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 |