RNA velocity unraveled
Open accessPublished version of a preprint
Abstract
We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
RNA velocity is inferred incorrectly in the presence of transcriptional bursts.
“Cases in which RNA velocity is inferred incorrectly include the presence of transcriptional bursts70,71.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [71]
In steady-state populations, RNA velocity infers erroneous directions between independent terminal cell populations.
“Additionally, steady-state populations pose further challenges where RNA velocity infers erroneous directions between independent, terminal cell populations70,71.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [71]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables
W., J. W., Abramowitz, M., & Stegun, I. A. (1965) · Mathematics of Computation · doi:10.2307/2004114 · Cited by 19,788 · see citing works
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723 · see citing works
Self-citationStochastic Processes in Physics and Chemistry
(2007) · Elsevier eBooks · doi:10.1016/b978-0-444-52965-7.x5000-4 · Cited by 9,070 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
Trapnell, 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
Gillespie, D. T. (1976) · Journal of Computational Physics · doi:10.1016/0021-9991(76)90041-3 · Cited by 6,029 · see citing works
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181 · see citing works
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479 · cited 1× · see citing works
“Several recent studies strongly discourage normalization of UMI-based counts [108, 109], although this perspective is not universal [110, 111].”
BackgroundStatistical Inference Using Extreme Order Statistics
Pickands, J. (1975) · The Annals of Statistics · doi:10.1214/aos/1176343003 · Cited by 4,173 · 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
Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables.
H., C., Abramowitz, M., & Stegun, I. A. (1964) · Journal of the American Statistical Association · doi:10.2307/2282672 · Cited by 3,234 · see citing works
Generalizing RNA velocity to transient cell states through dynamical modeling
Bergen, V., Lange, M., Peidli, S., Wolf, F. A., & Theis, F. J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0591-3 · Cited by 2,951 · 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
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231 · 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
Stochastic mRNA Synthesis in Mammalian Cells
Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y., & Tyagi, S. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040309 · Cited by 1,737 · see citing works
Cells of the adult human heart
Litviňuková, M., Talavera-López, C., Maatz, H., Reichart, D., Worth, C. L., Lindberg, E. L., Kanda, M., Polanski, K., Heinig, M., Lee, M., et al. (2020) · Nature · doi:10.1038/s41586-020-2797-4 · Cited by 1,729 · see citing works
The origins of genome complexity
Lynch, M., & Conery, J. S. (2003) · Science · doi:10.1126/science.1089370 · Cited by 1,698 · see citing works
Wolf, F. A., Hamey, F. K., Plass, M., Solana, J., Dahlin, J. S., Göttgens, B., Rajewsky, N., Simon, L., & Theis, F. J. (2019) · Genome Biology · doi:10.1186/s13059-019-1663-x · Cited by 1,698 · 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
Intrinsic and extrinsic contributions to stochasticity in gene expression
Swain, P. S., Elowitz, M. B., & Siggia, E. D. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.162041399 · Cited by 1,661 · see citing works
Real-Time Kinetics of Gene Activity in Individual Bacteria
Golding, I., Paulsson, J., Zawilski, S. M., & Cox, E. C. (2005) · Cell · doi:10.1016/j.cell.2005.09.031 · Cited by 1,468 · see citing works
The Traveling Salesman Problem and Its Variations
Gutin, G., & Punnen, A. P. (2006) · Combinatorial optimization · doi:10.1007/b101971 · Cited by 1,393 · see citing works
Museum of Spatial Transcriptomics
Moses, L., & Pachter, L. (2021) · doi:10.1101/2021.05.11.443152 · Cited by 1,255 · see citing works
Self-citationMuseum of spatial transcriptomics
Moses, L., & Pachter, L. (2022) · Nature Methods · doi:10.1038/s41592-022-01409-2 · Cited by 1,255 · see citing works
Self-citationThe art of using t-SNE for single-cell transcriptomics
Kobak, D., & Berens, P. (2019) · Nature Communications · doi:10.1038/s41467-019-13056-x · Cited by 1,118 · see citing works
Construction 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
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071 · see citing works
Milo, R., & Phillips, R. (2015) · Garland Science eBooks · doi:10.1201/9780429258770 · Cited by 1,034 · see citing works
Fortuna, E., Gianni, P., & Trager, B. (1961) · American Mathematical Monthly · doi:10.2307/2311524 · Cited by 875 · see citing works
A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer
Bassez, A., Vos, H., Van Dyck, L., Floris, G., Arijs, I., Desmedt, C., Boeckx, B., Vanden Bempt, M., Nevelsteen, I., Lambein, K., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01323-8 · Cited by 867 · see citing works
Continuous Univariate Distributions.Vol. 1
Kocherlakota, S., Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995) · Biometrics · doi:10.2307/2533292 · Cited by 861 · see citing works
A Python library for probabilistic analysis of single-cell omics data
Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789 · see citing works
Self-citationModels of stochastic gene expression
Paulsson, J. (2005) · Physics of Life Reviews · doi:10.1016/j.plrev.2005.03.003 · Cited by 774 · see citing works
A Modern Course in Statistical Physics
Reichl, L. E. (2016) · Wiley eBooks · doi:10.1002/9783527690497 · Cited by 772 · see citing works
The finite state projection algorithm for the solution of the chemical master equation
Munsky, B. E., & Khammash, M. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2145882 · Cited by 760 · see citing works
Using Gene Expression Noise to Understand Gene Regulation
Munsky, B., Neuert, G., & van Oudenaarden, A. (2012) · Science · doi:10.1126/science.1216379 · Cited by 745 · see citing works
Markovian Modeling of Gene-Product Synthesis
Peccoud, J., & Ycart, B. (1995) · Theoretical Population Biology · doi:10.1006/tpbi.1995.1027 · Cited by 739 · see citing works
CellRank for directed single-cell fate mapping
Lange, M., Bergen, V., Klein, M., Setty, M. N., Reuter, B., Bakhti, M., Lickert, H., Ansari, M., Gote-Schniering, J., Schiller, H. B., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01346-6 · Cited by 735 · see citing works
The adult human testis transcriptional cell atlas
Guo, J., Grow, E. J., Mlčochová, H., Maher, G. J., Lindskog, C., Nie, X., Guo, Y., Takei, Y., Yun, J., Cai, L., et al. (2018) · Cell Research · doi:10.1038/s41422-018-0099-2 · Cited by 715 · see citing works
Single-cell RNA counting at allele and isoform resolution using Smart-seq3
Hagemann-Jensen, M., Ziegenhain, C., Chen, P., Ramsköld, D., Hendriks, G.-J., Larsson, A. J. M., Faridani, O. R., & Sandberg, R. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0497-0 · Cited by 700 · see citing works
Single-RNA counting reveals alternative modes of gene expression in yeast
Zenklusen, D., Larson, D. R., & Singer, R. H. (2008) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1514 · Cited by 684 · see citing works
An accurate and robust imputation method scImpute for single-cell RNA-seq data
Li, W. V., & Li, J. J. (2018) · Nature Communications · doi:10.1038/s41467-018-03405-7 · Cited by 682 · see citing works
Shah, S., Lubeck, E., Zhou, W., & Cai, L. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.001 · Cited by 671 · see citing works
A First Course in Order Statistics
Arnold, B. C., Balakrishnan, N., & Nagaraja, H. N. (2008) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898719062 · Cited by 660 · see citing works
Diffusion maps for high-dimensional single-cell analysis of differentiation data
Haghverdi, L., Buettner, F., & Theis, F. J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv325 · Cited by 649 · see citing works
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Jiang, R., Sun, T., Song, D., & Li, J. J. (2022) · Genome Biology · doi:10.1186/s13059-022-02601-5 · Cited by 614 · see citing works
Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy
Couturier, C., Ayyadhury, S., Le, P. U., Nadaf, J., Monlong, J., Riva, G., Allache, R., Baig, S., Yan, X., Bourgey, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17186-5 · Cited by 562 · see citing works
Spatiotemporal structure of cell fate decisions in murine neural crest
Soldatov, R., Kaucka, M., Kastriti, M. E., Petersen, J., Chontorotzea, T., Englmaier, L., Akkuratova, N., Yang, Y., Häring, M., Dyachuk, V., et al. (2019) · Science · doi:10.1126/science.aas9536 · Cited by 546 · see citing works
Genomic encoding of transcriptional burst kinetics
Larsson, A. J. M., Johnsson, P., Hagemann-Jensen, M., Hartmanis, L., Faridani, O. R., Reinius, B., Segerstolpe, Å., Rivera, C. M., Ren, B., & Sandberg, R. (2019) · Nature · doi:10.1038/s41586-018-0836-1 · Cited by 534 · see citing works
Blastocyst-like structures generated from human pluripotent stem cells
Yu, L., Wei, Y., Duan, J., Schmitz, D. A., Sakurai, M., Wang, L., Wang, K., Zhao, S., Hon, G. C., & Wu, J. (2021) · Nature · doi:10.1038/s41586-021-03356-y · Cited by 496 · see citing works
Transcriptional burst frequency and burst size are equally modulated across the human genome
Dar, R. D., Razooky, B. S., Singh, A., Trimeloni, T. V., McCollum, J. M., Cox, C. D., Simpson, M. L., & Weinberger, L. S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1213530109 · Cited by 491 · see citing works
Rates of in situ transcription and splicing in large human genes
Singh, J., & Padgett, R. A. (2009) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1666 · Cited by 487 · see citing works
van Kampen, N. G. (2007) · Elsevier eBooks · doi:10.1016/b978-044452965-7/50006-4 · Cited by 482 · see citing works
Modular, efficient and constant-memory single-cell RNA-seq preprocessing
Melsted, P., Booeshaghi, A. S., Liu, L., Gao, F., Moses, L., Min, K. H., Beltrame, E. D. V., Hjörleifsson, K. E., Gehring, J., & Pachter, L. S. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00870-2 · Cited by 478 · see citing works
Self-citationFeature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A. (2019) · Genome Biology · doi:10.1186/s13059-019-1861-6 · Cited by 471 · see citing works
Genetic Determinants and Cellular Constraints in Noisy Gene Expression
Sánchez, Á., & Golding, I. (2013) · Science · doi:10.1126/science.1242975 · Cited by 460 · see citing works
A Critical and Comparative Review of Fluorescent Tools for Live-Cell Imaging
Specht, E. A., Braselmann, E., & Palmer, A. E. (2016) · Annual Review of Physiology · doi:10.1146/annurev-physiol-022516-034055 · Cited by 445 · see citing works
Mapping transcriptomic vector fields of single cells
Qiu, X., Zhang, Y., Martin-Rufino, J. D., Weng, C., Hosseinzadeh, S., Yang, D., Pogson, A. N., Hein, M. Y., Min, K. H., Wang, L., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.045 · Cited by 436 · see citing works
Embracing the dropouts in single-cell RNA-seq analysis
Qiu, P. (2020) · Nature Communications · doi:10.1038/s41467-020-14976-9 · Cited by 396 · 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
Self-citationDroplet scRNA-seq is not zero-inflated
Svensson, V. (2019) · doi:10.1101/582064 · Cited by 356 · see citing works
Widespread intron retention diversifies most cancer transcriptomes
Dvinge, H., & Bradley, R. K. (2015) · Genome Medicine · doi:10.1186/s13073-015-0168-9 · Cited by 346 · see citing works
Fundamental 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
Control of Transcript Variability in Single Mammalian Cells
Battich, N., Stoeger, T., & Pelkmans, L. (2015) · Cell · doi:10.1016/j.cell.2015.11.018 · Cited by 324 · see citing works
Dynamics and Spatial Genomics of the Nascent Transcriptome by Intron seqFISH
Shah, S., Takei, Y., Zhou, W., Lubeck, E., Yun, J., Eng, C.-H. L., Koulena, N., Cronin, C. J., Karp, C., Liaw, E. J., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.035 · Cited by 316 · see citing works
Solving the chemical master equation for monomolecular reaction systems analytically
Jahnke, T., & Huisinga, W. (2006) · Journal of Mathematical Biology · doi:10.1007/s00285-006-0034-x · Cited by 314 · see citing works
Single-cell transcriptomic analyses provide insights into the developmental origins of neuroblastoma
Jansky, S., Sharma, A. K., Körber, V., Quintero, A., Toprak, U. H., Wecht, E. M., Gartlgruber, M., Greco, A., Chomsky, E., Grünewald, T. G. P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00806-1 · Cited by 290 · see citing works
Veloso, A., Kirkconnell, K. S., Magnuson, B., Biewen, B., Paulsen, M. T., Wilson, T. E., & Ljungman, M. (2014) · Genome Research · doi:10.1101/gr.171405.113 · Cited by 281 · see citing works
Dynamic Heterogeneity and DNA Methylation in Embryonic Stem Cells
Singer, Z. S., Yong, J., Tischler, J., Hackett, J. A., Altınok, A., Surani, M. A., Cai, L., & Elowitz, M. B. (2014) · Molecular Cell · doi:10.1016/j.molcel.2014.06.029 · Cited by 275 · see citing works
RNA velocity—current challenges and future perspectives
Bergen, V., Soldatov, R. A., Kharchenko, P. V., & Theis, F. J. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110282 · Cited by 271 · see citing works
Lönnberg, T., Svensson, V., James, K. R., Fernandez‐Ruiz, D., Sebina, I., Montandon, R., Soon, M. S. F., Fogg, L. G., Nair, A. S., Liligeto, U. N., et al. (2017) · Science Immunology · doi:10.1126/sciimmunol.aal2192 · Cited by 260 · see citing works
Sequence census methods for functional genomics
Wold, B. J., & Myers, R. (2007) · Nature Methods · doi:10.1038/nmeth1157 · Cited by 257 · see citing works
Kim, J. K., Kolodziejczyk, A. A., Ilicic, T., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Communications · doi:10.1038/ncomms9687 · Cited by 251 · see citing works
Bursty Gene Expression in the Intact Mammalian Liver
Halpern, K. B., Tanami, S., Landen, S., Chapal, M., Szlak, L., Hutzler, A., Nizhberg, A., & Itzkovitz, S. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.01.027 · Cited by 248 · see citing works
Concepts and limitations for learning developmental trajectories from single cell genomics
Tritschler, S., Büttner, M., Fischer, D. S., Lange, M., Bergen, V., Lickert, H., & Theis, F. J. (2019) · Development · doi:10.1242/dev.170506 · Cited by 233 · see citing works
Generalizing RNA velocity to transient cell states through dynamical modelingPreprint
Bergen, V., Lange, M., Peidli, S., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/820936 · Cited by 229 · see citing works
Detection and evaluation of intron retention events in the human transcriptome
Galante, P. A. F., Sakabe, N. J., Kirschbaum-Slager, N., & SOUZA, S. J. D. (2004) · RNA · doi:10.1261/rna.5123504 · Cited by 228 · see citing works
Transcription in Living Cells: Molecular Mechanisms of Bursting
Rodriguez, J., & Larson, D. R. (2020) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-011520-105250 · Cited by 224 · see citing works
Mixture models: Theory, geometry and applications
Jensen, D. R. (1997) · Journal of Statistical Planning and Inference · doi:10.1016/s0378-3758(96)00097-3 · Cited by 222 · see citing works
Chernozhukov, V. (2005) · The Annals of Statistics · doi:10.1214/009053604000001165 · Cited by 210 · see citing works
Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data
Kim, J. K., & Marioni, J. C. (2013) · Genome biology · doi:10.1186/gb-2013-14-1-r7 · Cited by 207 · see citing works
Tian, L., Jabbari, J. S., Thijssen, R., Gouil, Q. A., Amarasinghe, S. L., Voogd, O., Kariyawasam, H., Du, M. R. M., Schuster, J., Wang, C., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02525-6 · Cited by 207 · see citing works
Listening to the noise: random fluctuations reveal gene network parameters
Munsky, B. E., Trinh, B., & Khammash, M. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.75 · Cited by 205 · see citing works
Splicing graphs and EST assembly problem
Heber, S., Alekseyev, M. A., Sze, S., Tang, H., & Pevzner, P. A. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s181 · Cited by 194 · see citing works
Intron retention in mRNA: No longer nonsense
Wong, J. J. L., Au, A. Y. M., Ritchie, W., & Rasko, J. E. J. (2015) · BioEssays · doi:10.1002/bies.201500117 · Cited by 192 · see citing works
Wan, Y., Anastasakis, D. G., Rodriguez, J., Palangat, M., Gudla, P. R., Zaki, G., Tandon, M., Pegoraro, G., Chow, C. C., Hafner, M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.012 · Cited by 188 · see citing works
Pimentel, H., Parra, M., Gee, S. L., Mohandas, N., Pachter, L. S., & Conboy, J. G. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1168 · Cited by 187 · see citing works
Self-citationStem Cell Differentiation as a Non-Markov Stochastic Process
Stumpf, P. S., Smith, R. C. G., Lenz, M., Schuppert, A., Müller, F.-J., Babtie, A. C., Chan, T. E., Stumpf, M. P. H., Please, C. P., Howison, S., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2017.08.009 · Cited by 181 · see citing works
Petukhov, V., Guo, J., Baryawno, N., Sévère, N., Scadden, D. T., Samsonova, M. G., & Kharchenko, P. V. (2018) · Genome biology · doi:10.1186/s13059-018-1449-6 · Cited by 176 · see citing works
Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
Cole, M. B., Risso, D., Wagner, A., DeTomaso, D., Ngai, J., Purdom, E., Dudoit, S., & Yosef, N. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.010 · Cited by 173 · see citing works
Wiley Series in Probability and Mathematical Statistics
Basilevsky, A. (1994) · Wiley series in probability and statistics · doi:10.1002/9780470316894.oth1 · Cited by 168 · see citing works
Fundamental behaviors emerge from simulations of a living minimal cell
Thornburg, Z. R., Bianchi, D. M., Brier, T. A., Gilbert, B. R., Earnest, E. E., Melo, M. C. R., Safronova, N., Sáenz, J. P., Cook, A. T., Wise, K. S., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.025 · Cited by 165 · see citing works
Reconstructing cell cycle pseudo time-series via single-cell transcriptome data
Liu, Z., Lou, H., Xie, K., Wang, H. E., Chen, N., Aparicio, O. M., Zhang, M. Q., Jiang, R., & Chen, T. (2017) · Nature Communications · doi:10.1038/s41467-017-00039-z · Cited by 156 · see citing works
Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells
Cao, Z., & Grima, R. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910888117 · Cited by 152 · see citing works
Single-cell analysis of transcription kinetics across the cell cycle
Skinner, S. O., Xu, H., Nagarkar-Jaiswal, S., Freire, P. R., Zwaka, T. P., & Golding, I. (2016) · eLife · doi:10.7554/elife.12175 · Cited by 151 · see citing works
The barcode, UMI, set format and BUStools
Melsted, P., Ntranos, V., & Pachter, L. S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz279 · Cited by 151 · see citing works
Self-citationZeisel, A., Köstler, W. J., Molotski, N., Tsai, J. M., Krauthgamer, R., Jacob‐Hirsch, J., Rechavi, G., Soen, Y., Jung, S., Yarden, Y., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.62 · Cited by 150 · see citing works
Comparison of Transformations for Single-Cell RNA-Seq Data
Ahlmann-Eltze, C., & Huber, W. (2021) · doi:10.1101/2021.06.24.449781 · Cited by 148 · see citing works
Hie, B., Yang, K., & Kim, P. S. (2022) · Cell Systems · doi:10.1016/j.cels.2022.01.003 · Cited by 147 · see citing works
Combining experiments with multi-cell agent-based modeling to study biological tissue patterning
Thorne, B. C., Bailey, A. M., & Peirce, S. M. (2007) · Briefings in Bioinformatics · doi:10.1093/bib/bbm024 · Cited by 144 · see citing works
False signals induced by single-cell imputation
Andrews, T. S., & Hemberg, M. (2018) · F1000Research · doi:10.12688/f1000research.16613.1 · Cited by 142 · see citing works
Demystifying “drop-outs” in single-cell UMI data
Kim, T. H., Zhou, X., & Chen, M. (2020) · Genome biology · doi:10.1186/s13059-020-02096-y · Cited by 130 · see citing works
Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells
Cannoodt, R., Saelens, W., Deconinck, L., & Saeys, Y. (2021) · Nature Communications · doi:10.1038/s41467-021-24152-2 · Cited by 124 · see citing works
Steady-state fluctuations of a genetic feedback loop: An exact solution
Grima, R., Schmidt, D. R., & Newman, T. J. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4736721 · Cited by 123 · see citing works
Cell segmentation-free inference of cell types from in situ transcriptomics data
Park, J., Choi, W., Tiesmeyer, S., Long, B., Borm, L. E., Garren, E. J., Nguyen, T. N., Tasic, B., Codeluppi, S., Graf, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23807-4 · Cited by 116 · see citing works
A DNA repair pathway can regulate transcriptional noise to promote cell fate transitions
Desai, R. V., Chen, X., Martin, B., Chaturvedi, S., Hwang, D.-W., Li, W., Chen, Y., Ding, S., Thomson, M. W., Singer, R. H., et al. (2021) · Science · doi:10.1126/science.abc6506 · Cited by 116 · see citing works
de la Mata, M., Lafaille, C., & Kornblihtt, A. R. (2010) · RNA · doi:10.1261/rna.1993510 · Cited by 115 · see citing works
Tedesco, M., Giannese, F., Lazarević, D., Giansanti, V., Rosano, D., Monzani, S., Catalano, I., Grassi, E., Zanella, E. R., Botrugno, O. A., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-01031-1 · Cited by 112 · see citing works
Delmans, M., & Hemberg, M. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0944-6 · Cited by 111 · see citing works
Delmans, M., & Hemberg, M. (2015) · doi:10.1101/020735 · Cited by 111 · see citing works
Computational approaches for interpreting sc<scp>RNA</scp>‐seq data
Rostom, R., Svensson, V., Teichmann, S. A., & Kar, G. (2017) · FEBS Letters · doi:10.1002/1873-3468.12684 · Cited by 107 · see citing works
What shapes eukaryotic transcriptional bursting?
Nicolas, D., Phillips, N. E., & Naef, F. (2017) · Molecular BioSystems · doi:10.1039/c7mb00154a · Cited by 107 · see citing works
Consequences of mRNA Transport on Stochastic Variability in Protein Levels
Singh, A., & Bokes, P. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2012.07.015 · Cited by 106 · see citing works
Kessler, O., Jiang, Y., & Chasin, L. A. (1993) · Molecular and Cellular Biology · doi:10.1128/mcb.13.10.6211 · Cited by 105 · see citing works
Distribution shapes govern the discovery of predictive models for gene regulation
Munsky, B. E., Li, G., Fox, Z., Shepherd, D. P., & Neuert, G. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804060115 · Cited by 98 · see citing works
Stochastic Kinetics of Nascent RNA
Xu, H., Skinner, S. O., Sokac, A. M., & Golding, I. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.128101 · Cited by 93 · see citing works
Munsky, B., Fox, Z., & Neuert, G. (2015) · Methods · doi:10.1016/j.ymeth.2015.06.009 · Cited by 89 · see citing works
Bayesian inference of gene expression states from single-cell RNA-seq data
Breda, J., Zavolan, M., & van Nimwegen, E. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00875-x · Cited by 87 · see citing works
Protein velocity and acceleration from single-cell multiomics experiments
Gorin, G., Svensson, V., & Pachter, L. S. (2020) · Genome biology · doi:10.1186/s13059-020-1945-3 · Cited by 85 · see citing works
Self-citationIsoform cell-type specificity in the mouse primary motor cortex
Booeshaghi, A. S., Yao, Z., van Velthoven, C. T. J., Smith, K. A., Tasic, B., Zeng, H., & Pachter, L. S. (2021) · Nature · doi:10.1038/s41586-021-03969-3 · Cited by 81 · see citing works
Self-citationStochastic approaches in systems biology
Ullah, M., & Wolkenhauer, O. (2009) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.78 · Cited by 79 · see citing works
Mixture Models: Theory, Geometry and Applications
Lindsay, B. G. (1995) · Project Euclid (Cornell University) · Cited by 79 · see citing works
The transcriptome dynamics of single cells during the cell cycle
Schwabe, D., Formichetti, S., Junker, J. P., Falcke, M., & Rajewsky, N. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209946 · Cited by 76 · see citing works
Extrinsic Noise and Heavy-Tailed Laws in Gene Expression
Ham, L., Brackston, R. D., & Stumpf, M. P. H. (2020) · Physical Review Letters · doi:10.1103/physrevlett.124.108101 · Cited by 74 · see citing works
Single-Cell Transcriptomics Reveals Chemotaxis-Mediated Intraorgan Crosstalk During Cardiogenesis
Xiong, H., Luo, Y., Yue, Y., Zhang, J., Ai, S., Li, X., Wang, X., Zhang, Y.-L., Wei, Y., Li, H., et al. (2019) · Circulation Research · doi:10.1161/circresaha.119.315243 · Cited by 71 · see citing works
Dattani, J., & Barahona, M. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2016.0833 · Cited by 69 · see citing works
Campbell, K. R., & Yau, C. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005212 · Cited by 66 · see citing works
Recent advances in trajectory inference from single-cell omics data
Deconinck, L., Cannoodt, R., Saelens, W., Deplancke, B., & Saeys, Y. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.05.005 · Cited by 66 · see citing works
RNA Velocity: Molecular Kinetics from Single-Cell RNA-Seq
Svensson, V., & Pachter, L. S. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.09.026 · Cited by 65 · see citing works
Self-citationA computational framework to study sub-cellular RNA localization
Samacoïts, A., Chouaib, R., Safieddine, A., Traboulsi, A.-M., Ouyang, W., Zimmer, C., Peter, M., Bertrand, É., Walter, T., & Mueller, F. (2018) · Nature Communications · doi:10.1038/s41467-018-06868-w · Cited by 63 · see citing works
Pumping the brakes on RNA velocity by understanding and interpreting RNA velocity estimates
Zheng, S., Stein-O’Brien, G., Boukas, L., Goff, L. A., & Hansen, K. D. (2023) · Genome biology · doi:10.1186/s13059-023-03065-x · Cited by 63 · see citing works
Preprocessing choices affect RNA velocity results for droplet scRNA-seq data
Soneson, C., Srivastava, A., Patro, R., & Stadler, M. B. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008585 · Cited by 62 · see citing works
Occupation measures for Markov chains
Pitman, J. (1977) · Advances in Applied Probability · doi:10.2307/1425817 · Cited by 59 · see citing works
WASABI: a dynamic iterative framework for gene regulatory network inference
Bonnaffoux, A., Herbach, U., Richard, A., Guillemin, A., Gonin-Giraud, S., Gros, P.-A., & Gandrillon, O. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2798-1 · Cited by 59 · see citing works
GrandPrix: scaling up the Bayesian GPLVM for single-cell data
Ahmed, S., Rattray, M., & Boukouvalas, A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty533 · Cited by 56 · see citing works
Schiebinger, G. R., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Liu, S., Lin, S. L., Berube, P., Lee, L., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/191056 · Cited by 54 · see citing works
Stochastic Approaches for Systems Biology
Ullah, M., & Wolkenhauer, O. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0478-1 · Cited by 53 · see citing works
Evaluation of STAR and Kallisto on Single Cell RNA-Seq Data Alignment
Du, Y., Huang, Q., Arisdakessian, C. G., & Garmire, L. X. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.120.401160 · Cited by 52 · see citing works
Order Statistics from Discrete Distributions
Nagaraja, H. N. (1992) · Statistics · doi:10.1080/02331889208802365 · Cited by 51 · see citing works
A novel metric reveals previously unrecognized distortion in the analysis of scRNA-seq dataPreprint
Hamilton, T., Sparta, B., Cooley, S. M., Aragones, S. D., Ray, J. C. J., & Deeds, E. J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/689851 · Cited by 49 · see citing works
Gorin, G., Vastola, J. J., Fang, M., & Pachter, L. S. (2022) · Nature Communications · doi:10.1038/s41467-022-34857-7 · Cited by 48 · see citing works
Self-citationA descriptive marker gene approach to single-cell pseudotime inference
Campbell, K. R., & Yau, C. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty498 · Cited by 47 · see citing works
Inference of high-resolution trajectories in single-cell RNA-seq data by using RNA velocity
Zhang, Z., & Zhang, X. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100095 · Cited by 42 · see citing works
Exploiting Natural Fluctuations to Identify Kinetic Mechanisms in Sparsely Characterized Systems
Hilfinger, A., Norman, T. M., & Paulsson, J. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.002 · Cited by 41 · see citing works
Distributions of order statistics for discrete case
Khatri, C. G. (1962) · Annals of the Institute of Statistical Mathematics · doi:10.1007/bf02868637 · Cited by 39 · see citing works
Intracellular RNA-tracking methods
George, L., Indig, F. E., Abdelmohsen, K., & Gorospe, M. (2018) · Open Biology · doi:10.1098/rsob.180104 · Cited by 39 · see citing works
Modeling bursty transcription and splicing with the chemical master equation
Gorin, G., & Pachter, L. S. (2022) · Biophysical Journal · doi:10.1016/j.bpj.2022.02.004 · Cited by 38 · see citing works
Self-citationKessler, O., Jiang, Y., & Chasin, L. A. (1993) · Molecular and Cellular Biology · doi:10.1128/mcb.13.10.6211-6222.1993 · Cited by 38 · see citing works
Optimal transport analysis reveals trajectories in steady-state systems
Zhang, S., Afanassiev, A., Greenstreet, L., Matsumoto, T., & Schiebinger, G. R. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009466 · Cited by 37 · see citing works
Mapping Transcriptomic Vector Fields of Single CellsPreprint
Qiu, X., Zhang, Y., Hosseinzadeh, S., Yang, D., Pogson, A. N., Wang, L., Shurtleff, M., Yuan, R., Song, X., Ma, Y.-A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/696724 · Cited by 35 · see citing works
On the Mathematics of RNA Velocity I: Theoretical Analysis
Li, T., Shi, J., Wu, Y., & Zhou, P. (2021) · CSIAM Transactions on Applied Mathematics · doi:10.4208/csiam-am.so-2020-0001 · Cited by 35 · see citing works
Tian, L., Jabbari, J. S., Thijssen, R., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.10.243543 · Cited by 33 · see citing works
Nascent RNA kinetics: Transient and steady state behavior of models of transcription
Choubey, S. (2018) · Physical review. E · doi:10.1103/physreve.97.022402 · Cited by 30 · see citing works
A mechanistic model for the negative binomial distribution of single-cell mRNA countsPreprint
Amrhein, L., Harsha, K., & Fuchs, C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/657619 · Cited by 29 · see citing works
Length biases in single-cell RNA sequencing of pre-mRNA
Gorin, G., & Pachter, L. S. (2022) · Biophysical Reports · doi:10.1016/j.bpr.2022.100097 · Cited by 28 · see citing works
Self-citationVeTra: a tool for trajectory inference based on RNA velocity
Weng, G., Kim, J., & Won, K. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab364 · Cited by 27 · see citing works
On distributionally robust extreme value analysis
Blanchet, J., He, F., & Murthy, K. (2020) · Extremes · doi:10.1007/s10687-019-00371-1 · Cited by 26 · see citing works
Limi, S., Senecal, A., Coleman, R., Lopez-Jones, M., Guo, P., Polumbo, C., Singer, R. H., Skoultchi, A. I., & Cvekl, A. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ra118.001927 · Cited by 24 · see citing works
Revealing Dynamic Mechanisms of Cell Fate Decisions From Single-Cell Transcriptomic Data
Zhang, J., Nie, Q. H., & Zhou, T. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01280 · Cited by 24 · see citing works
Simulation-based inference of differentiation trajectories from RNA velocity fields
Gupta, R., Cerletti, D., Gut, G., Oxenius, A., & Claassen, M. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100359 · Cited by 24 · see citing works
VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories
Atta, L., Sahoo, A., & Fan, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab653 · Cited by 22 · see citing works
Special function methods for bursty models of transcription
Gorin, G., & Pachter, L. S. (2020) · Physical review. E · doi:10.1103/physreve.102.022409 · Cited by 21 · see citing works
Self-citationOptimal Transport for Parameter Identification of Chaotic Dynamics via Invariant Measures
Yang, Y., Nurbekyan, L., Negrini, E., Martin, R., & Pasha, M. (2023) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/21m1421337 · Cited by 20 · see citing works
Gorin, G., Wang, M., Golding, I., & Xu, H. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230736 · Cited by 19 · see citing works
Self-citationKuntz, J., Thomas, P., Stan, G.-B. V., & Barahona, M. (2019) · SIAM Journal on Scientific Computing · doi:10.1137/18m1168261 · Cited by 18 · see citing works
Keizer, E. M., Bastian, B., Smith, R. W., Grima, R., & Fleck, C. C. (2019) · Physical review. E · doi:10.1103/physreve.99.052417 · Cited by 18 · see citing works
Compositional Data Analysis is necessary for simulating and analyzing RNA-Seq dataPreprint
McGee, W. A., Pimentel, H., Pachter, L. S., & Wu, J. Y. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/564955 · Cited by 17 · see citing works
Self-citationSingle molecule analysis of combinatorial splicing
Conze, T., Göransson, J., Razzaghian, H. R., Ericsson, O., Öberg, D., Akusjärvi, G., Landegren, U. D., & Nilsson, M. F. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq581 · Cited by 16 · see citing works
Towards a mathematical theory of trajectory inferencePreprint
Lavenant, H., Zhang, S. X., Kim, Y., & Schiebinger, G. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.09204 · Cited by 16 · see citing works
Extremal quantile regression: an overview
Chernozhukov, V., Kaji, T., & Fernández‐Val, I. (2017) · doi:10.1920/wp.cem.2017.6517 · Cited by 15 · see citing works
Single-cell multi-omic velocity infers dynamic and decoupled gene regulationPreprint
Li, C., Virgilio, M., Collins, K. L., & Welch, J. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.13.472472 · Cited by 15 · see citing works
VeloSim: Simulating single cell gene-expression and RNA velocityPreprint
Zhang, Z., & Zhang, X. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.11.426277 · Cited by 13 · see citing works
A note on the distribution of the maximum of a set of Poisson random variablesPreprint
Briggs, K., Song, L., & Prellberg, T. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0903.4373 · Cited by 11 · see citing works
Trajectory inference and parameter estimation in stochastic models with temporally aggregated data
Folia, M. M., & Rattray, M. (2017) · Statistics and Computing · doi:10.1007/s11222-017-9779-x · Cited by 11 · see citing works
A comparison of alternative mRNA splicing in the CD4 and CD8 T cell lineages
Liu, X., Andrews, M. V., Skinner, J. P. J., Johanson, T. M., & Chong, M. M. W. (2021) · Molecular Immunology · doi:10.1016/j.molimm.2021.02.009 · Cited by 11 · see citing works
Gorin, G., Vastola, J. J., Fang, M., & Pachter, L. S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.06.459173 · Cited by 11 · see citing works
Self-citationEvolutionary velocity with protein language modelsPreprint
Hie, B., Yang, K., & Kim, P. S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.07.447389 · Cited by 10 · see citing works
Understanding the molecular mechanisms of transcriptional bursting
Klindziuk, A., & Kolomeisky, A. B. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp03665c · Cited by 10 · see citing works
Untangling biological factors influencing trajectory inference from single cell data
Charrout, M., Reinders, M. J. T., & Mahfouz, A. E. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa053 · Cited by 10 · see citing works
Erratum: Untangling biological factors influencing trajectory inference from single cell data.
Charrout, M., Reinders, M. J. T., & Mahfouz, A. E. (2020) · PubMed Central · doi:10.1093/nargab/lqaa102 · Cited by 8 · see citing works
High-Performance Whole-Cell Simulation Exploiting Modular Cell Biology Principles
Das, B., & Mitra, P. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01282 · Cited by 8 · see citing works
Single-Cell Multi-omic Velocity Infers Dynamic and Decoupled Gene Regulation
Li, C., Virgilio, M., Collins, K. L., & Welch, J. D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04749-7_18 · Cited by 7 · see citing works
Inference of high-resolution trajectories in single cell RNA-Seq data from RNA velocityPreprint
Zhang, Z., & Zhang, X. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.30.321125 · Cited by 6 · see citing works
Optimal Transport for Parameter Identification of Chaotic Dynamics via Invariant MeasuresPreprint
Yang, Y., Nurbekyan, L., Negrini, E., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.15138 · Cited by 6 · see citing works
Effects of elongation delay in transcription dynamics
Zhang, X., Jin, H., Yang, Z., & Lei, J. (2014) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2014.11.1431 · Cited by 5 · see citing works
VeTra: a tool for trajectory inference based on RNA velocityPreprint
Weng, G., Kim, J., & Won, K. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.01.277095 · Cited by 5 · see citing works
Velo-Predictor: an ensemble learning pipeline for RNA velocity prediction
Wang, X., & Zheng, J. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04330-1 · Cited by 5 · see citing works
Cytopath: Simulation-based inference of differentiation trajectories from RNA velocity fieldsPreprint
Gupta, R., Cerletti, D., Gut, G., Oxenius, A., & Claassen, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.21.423801 · Cited by 3 · see citing works
scRegulocity: Detection of local RNA velocity patterns in embeddings of single cell RNA-Seq dataPreprint
Harmancı, A. S., Harmanci, A. O., Zhou, X., Deneen, B., Rao, G., Klisch, T. J., & Patel, A. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.01.446674 · Cited by 3 · see citing works
Extremal Quantile Regression: An OverviewPreprint
Chernozhukov, V., Fernández‐Val, I., & Kaji, T. (2016) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1612.06850
On distributionally robust extreme value analysis
Blanchet, J., He, F., & Murthy, K. R. A. (2016) · arXiv · doi:10.48550/arxiv.1601.06858
Flagged first, then by citations
Research works printed but not linked: 6
- [83]Dattani J. Exact solutions of master equations for the analysis of gene transcription models [PhD Dissertation]. Imperial College London; 2015.
- [107]Ahlmann-Eltze C, Huber W. Transformation and Preprocessing of Single-Cell RNA-Seq Data. bioRxiv: 2021.06.24.449781; 2021.
- [148]Gorin G, Pachter L. Monod: mechanistic analysis of single-cell RNA sequencing count data. bioRxiv: 2022.06.11.495771; 2022.
- [149]Davis ME, Davis RJ. Fundamentals of chemical reaction engineering. International ed ed. McGraw-Hill chemical engineering series. Boston: McGraw-Hill; 2003.
- [156]Jia C, Grima R. Accuracy and limitations of extrinsic noise models to describe gene expression in growing cells. bioRxiv: 2022.06.15.496247; 2022.
- [161]Coté A, Coté C, Bayatpour S, Drexler HL, Alexander KA, Chen F, et al. pre-mRNA spatial distributions suggest that splicing can occur post-transcriptionally. bioRxiv: 2020.04.06.028092; 2021.
Cited by
Sorted by citations
Best 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
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
Self-citationCellRank 2: unified fate mapping in multiview single-cell data
Weiler, P., Lange, M., Klein, M., Pe’er, D., & Theis, F. J. (2024) · Nature Methods · doi:10.1038/s41592-024-02303-9 · Cited by 260 · see citing works
He, P., Lim, K., Sun, D., Pett, J. P., Jeng, Q., Polański, K., Dong, Z., Bolt, L., Richardson, L., Mamanova, L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.11.005 · Cited by 221 · see citing works
Time-resolved single-cell transcriptomics defines immune trajectories in glioblastoma
Kirschenbaum, D. S., Xie, K., Ingelfinger, F., Katzenelenbogen, Y., Abadie, K., Look, T., Sheban, F., Phan, T. S., Li, B., Zwicky, P., et al. (2023) · Cell · doi:10.1016/j.cell.2023.11.032 · Cited by 217 · see citing works
Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells
Gayoso, A., Weiler, P., Lotfollahi, M., Klein, D., Hong, J., Streets, A., Theis, F. J., & Yosef, N. (2023) · Nature Methods · doi:10.1038/s41592-023-01994-w · Cited by 140 · see citing works
Lineage-specific intolerance to oncogenic drivers restricts histological transformation
Gardner, E. E., Earlie, E. M., Li, K., Thomas, J., Hubisz, M. J., Stein, B. D., Zhang, C., Cantley, L. C., Laughney, A. M., & VARMUS, H. E. (2024) · Science · doi:10.1126/science.adj1415 · Cited by 115 · see citing works
A relay velocity model infers cell-dependent RNA velocity
Li, S., Zhang, P., Chen, W., Ye, L., Brannan, K. W., Le, N.-T., Abe, J., Cooke, J. P., & Wang, G. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01728-5 · Cited by 106 · see citing works
Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA
Kobayashi-Kirschvink, K. J., Comiter, C. S., Gaddam, S., Joren, T., Grody, E. I., Ounadjela, J. R., Zhang, K., Ge, B., Kang, J. W., Xavier, R. J., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02082-2 · Cited by 93 · see citing works
Steele, L., Olabi, B., Roberts, K., Mazin, P., Koplev, S., Tudor, C. L., Rumney, B. M., Admane, C., Jiang, T., Correa‐Gallegos, D., et al. (2025) · Nature Immunology · doi:10.1038/s41590-025-02267-8 · Cited by 88 · see citing works
kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq
Sullivan, D. K., Min, K. H., Hjörleifsson, K. E., Luebbert, L., Holley, G., Moses, L., Gustafsson, J., Bray, N., Pimentel, H., Booeshaghi, A. S., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01057-0 · Cited by 77 · see citing works
Self-citationDeepVelo: deep learning extends RNA velocity to multi-lineage systems with cell-specific kinetics
Cui, H., Maan, H., Vladoiu, M. C., Zhang, J., Taylor, M. D., & Wang, B. (2024) · Genome biology · doi:10.1186/s13059-023-03148-9 · Cited by 64 · see citing works
Pumping the brakes on RNA velocity by understanding and interpreting RNA velocity estimates
Zheng, S., Stein-O’Brien, G., Boukas, L., Goff, L. A., & Hansen, K. D. (2023) · Genome biology · doi:10.1186/s13059-023-03065-x · Cited by 63 · see citing works
Mapping the topography of spatial gene expression with interpretable deep learning
Chitra, U., Arnold, B. J., Sarkar, H., Sanno, K., Ma, C., Lopez-Darwin, S. L., & Raphael, B. J. (2025) · Nature Methods · doi:10.1038/s41592-024-02503-3 · Cited by 63 · see citing works
Li, H., Zhang, Z., Squires, M., Chen, X. X., & Zhang, X. (2025) · Nature Methods · doi:10.1038/s41592-025-02651-0 · Cited by 61 · see citing works
Technology meets TILs: Deciphering T cell function in the -omics era
Hudson, W. H., & Wieland, A. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.011 · Cited by 53 · see citing works
Tóth, A., Kannan, P. S., Snowball, J. M., Kofron, M. J., Wayman, J. A., Bridges, J. P., Miraldi, E. R., Swarr, D. T., & Zacharias, W. J. (2023) · Nature Communications · doi:10.1038/s41467-023-44184-0 · Cited by 53 · see citing works
spliceJAC: transition genes and state‐specific gene regulation from single‐cell transcriptome data
Bocci, F., Zhou, P., & Nie, Q. H. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211176 · Cited by 48 · see citing works
Gorin, G., Vastola, J. J., Fang, M., & Pachter, L. S. (2022) · Nature Communications · doi:10.1038/s41467-022-34857-7 · Cited by 48 · see citing works
Self-citationCell2fate infers RNA velocity modules to improve cell fate prediction
Aivazidis, A., Memi, F., Kleshchevnikov, V., Er, S., Clarke, B., Stegle, O., & Bayraktar, O. A. (2025) · Nature Methods · doi:10.1038/s41592-025-02608-3 · Cited by 48 · see citing works
Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics
Farrell, S., Mani, M., & Goyal, S. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100581 · Cited by 47 · see citing works
TFvelo: gene regulation inspired RNA velocity estimation
Li, J., Pan, X., Yuan, Y., & Shen, H. (2024) · Nature Communications · doi:10.1038/s41467-024-45661-w · Cited by 46 · see citing works
Lost in space: what single-cell RNA sequencing cannot tell you
Adema, K., Schon, M. A., Nodine, M. D., & Kohlen, W. (2024) · Trends in Plant Science · doi:10.1016/j.tplants.2024.03.010 · Cited by 42 · see citing works
SIRV: spatial inference of RNA velocity at the single-cell resolution
Abdelaal, T., Grossouw, L. M., Pasterkamp, R. J., Lelieveldt, B. P. F., Reinders, M. J. T., & Mahfouz, A. E. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae100 · Cited by 39 · see citing works
Studying stochastic systems biology of the cell with single-cell genomics data
Gorin, G., Vastola, J. J., & Pachter, L. S. (2023) · Cell Systems · doi:10.1016/j.cels.2023.08.004 · Cited by 38 · see citing works
Self-citationLuo, S., Zhang, Z., Wang, Z., Yang, X., Chen, X., Zhou, T., & Zhang, J. (2023) · Royal Society Open Science · doi:10.1098/rsos.221057 · Cited by 35 · see citing works
Maizels, R. J., Snell, D. M., & Briscoe, J. (2024) · Cell Systems · doi:10.1016/j.cels.2024.04.004 · Cited by 35 · see citing works
Spatial transition tensor of single cells
Zhou, P., Bocci, F., Li, T., & Nie, Q. H. (2024) · Nature Methods · doi:10.1038/s41592-024-02266-x · Cited by 35 · see citing works
The impact of package selection and versioning on single-cell RNA-seq analysis
Rich, J. M., Moses, L., Einarsson, P. H., Jackson, K. C., Luebbert, L., Booeshaghi, A. S., Antonsson, S. E., Sullivan, D. K., Bray, N., Melsted, P., et al. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101560 · Cited by 35 · see citing works
Self-citationSingle-cell multi-omics and lineage tracing to dissect cell fate decision-making
Haghverdi, L., & Ludwig, L. S. (2023) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.12.003 · Cited by 35 · see citing works
Inferring cell trajectories of spatial transcriptomics via optimal transport analysis
Shen, X., Zuo, L., Ye, Z., Yuan, Z., Huang, K., Li, Z., Yu, Q., Zou, X., Wei, X., Xu, P., et al. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101194 · Cited by 34 · see citing works
Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data
Carilli, M., Gorin, G., Choi, Y., Chari, T., & Pachter, L. S. (2024) · Nature Methods · doi:10.1038/s41592-024-02365-9 · Cited by 33 · see citing works
Self-citationLoers, J. U., & Vermeirssen, V. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae382 · Cited by 33 · see citing works
Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools
Pillai, M., Hojel, E., Jolly, M. K., & Goyal, Y. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00427-0 · Cited by 33 · see citing works
Lederer, A. R., Leonardi, M., Talamanca, L., Bobrovskiy, D. M., Herrera, A., Droin, C., Khven, I. M., Carvalho, H. J. F., Valente, A., Mantes, A. D., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02471-8 · Cited by 32 · see citing works
Towards reliable quantification of cell state velocities
Marot-Lassauzaie, V., Bouman, B. J., Donaghy, F. D., Demerdash, Y., Essers, M. A. G., & Haghverdi, L. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010031 · Cited by 30 · see citing works
Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination
Wu, S., & Schmitz, U. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.03.023 · Cited by 30 · see citing works
Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq
Stokes, T., Cen, H. H., Kapranov, P., Gallagher, I. J., Pitsillides, A. A., Volmar, C., Kraus, W. E., Johnson, J. D., Phillips, S. M., Wahlestedt, C., et al. (2023) · Advanced Genetics · doi:10.1002/ggn2.202200024 · Cited by 26 · see citing works
A cell atlas of the human fallopian tube throughout the menstrual cycle and menopause
Weigert, M., Li, Y., Zhu, L., Eckart, H., Bajwa, P., Krishnan, R., Ackroyd, S. A., Lastra, R. R., Bilecz, A. J., Basu, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55440-2 · Cited by 26 · see citing works
Single-cell transcriptomics in tissue engineering and regenerative medicine
Ruta, A., Krishnan, K., & Elisseeff, J. H. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00132-7 · Cited by 25 · see citing works
Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells
Park, J. H., Hothi, P., de Lomana, A. L. G., Pan, M., Calder, R., Turkarslan, S., Wu, W., Lee, H., Patel, A. P., Cobbs, C., et al. (2024) · Science Advances · doi:10.1126/sciadv.adj7706 · Cited by 25 · see citing works
Transcriptomic forecasting with neural ordinary differential equations
Erbe, R., Stein-O’Brien, G., & Fertig, E. J. (2023) · Patterns · doi:10.1016/j.patter.2023.100793 · Cited by 25 · see citing works
Single-cell dissection of tumor microenvironmental response and resistance to cancer therapy
Luo, Y., & Liang, H. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.07.005 · Cited by 25 · see citing works
Innate immunity champions: The diverse functions of macrophages
Biscu, F., Zouzaf, A., Cicia, D., Pridans, C., & Matteoli, G. (2024) · European Journal of Immunology · doi:10.1002/eji.202451139 · Cited by 24 · see citing works
Pyro-Velocity: Probabilistic RNA Velocity inference from single-cell dataPreprint
Qin, Q., Bingham, E., Manno, G. L., Langenau, D. M., & Pinello, L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.12.507691 · Cited by 24 · see citing works
Sukys, A., & Grima, R. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf295 · Cited by 21 · see citing works
Accurate quantification of nascent and mature RNAs from single-cell and single-nucleus RNA-seq
Sullivan, D. K., Hjörleifsson, K. E., Swarna, N. P., Oakes, C. G., Holley, G., Melsted, P., & Pachter, L. S. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1137 · Cited by 21 · see citing works
Self-citationGorin, G., Chari, T., Carilli, M., Vastola, J. J., & Pachter, L. S. (2025) · Nature Methods · doi:10.1038/s41592-025-02832-x · Cited by 21 · see citing works
Self-citationBiophysically interpretable inference of cell types from multimodal sequencing data
Chari, T., Gorin, G., & Pachter, L. S. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00689-2 · Cited by 20 · see citing works
Self-citationTrajectory inference from single-cell genomics data with a process time model
Fang, M., Gorin, G., & Pachter, L. S. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012752 · Cited by 20 · see citing works
Self-citationReversing pathological cell states: the road less travelled can extend the therapeutic horizon
Kholodenko, B. Ν., Kölch, W., & Rukhlenko, O. S. (2023) · Trends in Cell Biology · doi:10.1016/j.tcb.2023.04.004 · Cited by 19 · see citing works
Volteras, D., Shahrezaei, V., & Thomas, P. (2024) · Cell Systems · doi:10.1016/j.cels.2024.07.002 · Cited by 15 · see citing works
Liao, X., Kang, L., Peng, Y., Chai, X., Xie, P., Lin, C., Ji, H., Jiao, Y., & Liu, J. (2024) · Nature Communications · doi:10.1038/s41467-024-55146-5 · Cited by 14 · see citing works
spVelo: RNA velocity inference for multi-batch spatial transcriptomics data
Long, W., Liu, T., Xue, L., & Zhao, H. (2025) · Genome biology · doi:10.1186/s13059-025-03701-8 · Cited by 14 · see citing works
Craniofacial developmental biology in the single-cell era
Tseng, K.-C., & Crump, J. G. (2023) · Development · doi:10.1242/dev.202077 · Cited by 14 · see citing works
Theoretical and computational tools to model multistable gene regulatory networks
Bocci, F., Jia, D., Nie, Q. H., Jolly, M. K., & Onuchic, J. N. (2023) · Reports on Progress in Physics · doi:10.1088/1361-6633/acec88 · Cited by 14 · see citing works
Elucidating the coordination of RNA processing using short-read and long-read RNA-sequencing methods
Alfonso-Gonzalez, C., & Hilgers, V. (2025) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-025-00895-4 · Cited by 13 · see citing works
Learning cell dynamics with neural differential equations
Vinyard, M. E., Rasmussen, A. W., Li, R., Klein, A. M., Getz, G. A., & Pinello, L. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01150-3 · Cited by 13 · see citing works
Improving cellular fitness of human stem cell-derived islets under hypoxia
Wang, X., Brielle, S., Kenty, J. H.-R., Korover, N., Bavli, D., Pop, R., & Melton, D. A. (2025) · Nature Communications · doi:10.1038/s41467-025-59924-7 · Cited by 13 · see citing works
TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference
Ge, M., Miao, J., Qi, J., Zhou, X., & Lin, Z. (2025) · Nature Communications · doi:10.1038/s41467-025-61628-x · Cited by 13 · see citing works
Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics
Yang, X., Huang, M., Chen, H., Dai, J., Chen, J., Chen, K., Zhou, J., Li, A., & Li, P. (2025) · Bioactive Materials · doi:10.1016/j.bioactmat.2025.07.047 · Cited by 13 · see citing works
Dissecting the spatiotemporal diversity of adult neural stem cells
Mitic, N., Neuschulz, A., Spanjaard, B., Schneider, J. R., Fresmann, N., Novoselc, K. T., Strunk, T., Münster, L., Olivares‐Chauvet, P., Ninkovic, J., et al. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00022-z · Cited by 13 · see citing works
What Single Cell RNA Sequencing Has Taught Us about Chronic Obstructive Pulmonary Disease
Sin, D. D. (2024) · Tuberculosis & respiratory diseases · doi:10.4046/trd.2024.0001 · Cited by 12 · see citing works
Ranek, J. S., Stanley, N., & Purvis, J. E. (2022) · Genome biology · doi:10.1186/s13059-022-02749-0 · Cited by 12 · see citing works
Learning cell-specific networks from dynamics and geometry of single cells
Zhang, S. Y., & Stumpf, M. P. H. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101399 · Cited by 12 · see citing works
Systems immunology spanning tumors, lymph nodes, and periphery
Sidiropoulos, D. N., Ho, W. J., Jaffee, E. M., Kagohara, L. T., & Fertig, E. J. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100670 · Cited by 11 · see citing works
A new atlas to study embryonic cell types in Xenopus
Petrova, K., Tretiakov, M., Kotov, A., Monsoro-Burq, A. H., & Peshkin, L. (2024) · Developmental Biology · doi:10.1016/j.ydbio.2024.04.003 · Cited by 11 · see citing works
FateNet: an integration of dynamical systems and deep learning for cell fate prediction
Sadria, M., & Bury, T. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae525 · Cited by 11 · see citing works
Seq-Scope-eXpanded: spatial omics beyond optical resolution
Anacleto, A., Cheng, W., Feng, Q., Park, A., Cho, C., Hwang, Y., Kim, Y., Si, Y., Hsu, J.-E., Zhao, Q., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69346-8 · Cited by 11 · see citing works
Dissection and integration of bursty transcriptional dynamics for complex systems
Gao, C., Vaikuntanathan, S., & Riesenfeld, S. J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2306901121 · Cited by 11 · see citing works
Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions
Miles, C. E., McKinley, S. A., Ding, F., & Lehoucq, R. B. (2024) · Bulletin of Mathematical Biology · doi:10.1007/s11538-024-01301-4 · Cited by 10 · see citing works
DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates
Mizukoshi, C., Kojima, Y., Nomura, S., Hayashi, S., Abe, K., & Shimamura, T. (2024) · Genome biology · doi:10.1186/s13059-024-03367-8 · Cited by 10 · see citing works
Time-resolved single-cell transcriptomic sequencing
Xu, X., Wen, Q. J., Lan, T., Zeng, L., Zeng, Y., Lin, S., Qiu, M., Na, X., & Yang, C. J. (2024) · Chemical Science · doi:10.1039/d4sc05700g · Cited by 10 · see citing works
Graph-Dynamo: Learning stochastic cellular state transition dynamics from single cell dataPreprint
Zhang, Y., Qiu, X., Ni, K., Weissman, J. S., Bahar, İ., & Xing, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.24.559170 · Cited by 10 · see citing works
Resolving cellular dynamics using single-cell temporal transcriptomics
Liu, Y., Huang, K., & Chen, W. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2023.103060 · Cited by 9 · see citing works
Challenges and progress in RNA velocity: Comparative analysis across multiple biological contexts
Ancheta, S., Dorman, L. C., Treut, G. L., Gurung, A., Huber, G., Royer, L. A., Granados, A. A., & Lange, M. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014303 · Cited by 9 · see citing works
Navigating Nongenetic Plasticity in Cancer Drug Resistance
Sun, H., Kumari, N., Melzer, M. E., Backman, V., & Goyal, Y. (2024) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-071924-084617 · Cited by 9 · see citing works
Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review
Wang, Y., Li, J., Zha, H., Liu, S., Huang, D., Fu, L., & Liu, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf339 · Cited by 9 · see citing works
Li, X., Poire, A., Jeong, K. J., Zhang, D., Chen, G., Sun, C., & Mills, G. B. (2023) · Cell Death Discovery · doi:10.1038/s41420-023-01586-9 · Cited by 9 · see citing works
Zhang, Y., Khalilitousi, M., & Park, Y. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100388 · Cited by 9 · see citing works
Mathematics of neural stem cells: Linking data and processes
Danciu, D.-P., Hooli, J., Martín-Villalba, A., & Marciniak-Czochra, A. K. (2023) · Cells and Development · doi:10.1016/j.cdev.2023.203849 · Cited by 8 · see citing works
RNA velocity and beyond: Current advances in modeling single-cell transcriptional dynamics
Shimamura, T. (2025) · Allergology International · doi:10.1016/j.alit.2025.08.005 · Cited by 7 · see citing works
Decoding cell state transitions driven by dynamic cell–cell communication in spatial transcriptomics
Yan, L., Zhang, D., & Sun, X. (2026) · Nature Computational Science · doi:10.1038/s43588-025-00934-2 · Cited by 7 · see citing works
Vo, D.-N., Yuan, O., Kanaya, M., Dushime, G. T., Li, H., Kotova, O., Caglar, E., de Lichtenberg, K. H., Rahman, S. H., Soneji, S., et al. (2024) · Blood Advances · doi:10.1182/bloodadvances.2023011909 · Cited by 7 · see citing works
Mapping, Modeling, and Reprogramming Cell-Fate Decision-Making Systems
Ham, L., Woodward, T. E., Coomer, M. A., & Stumpf, M. P. H. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-101424-121439 · Cited by 6 · see citing works
Peng, Q., Qiu, X., & Li, T. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012606 · Cited by 6 · see citing works
On the Mathematics of RNA Velocity II: Algorithmic Aspects
Li, T., Wang, Y. W. Y., Yang, G., & Zhou, P. (2024) · CSIAM Transactions on Applied Mathematics · doi:10.4208/csiam-am.so-2023-0026 · Cited by 6 · see citing works
Zhu, L., & Wang, J. (2025) · JACS Au · doi:10.1021/jacsau.5c00620 · Cited by 5 · see citing works
Interpretable learning of temporal cellular dynamics from single-cell data
Boudjelthia, I. K., Milite, S., Kazwini, N. E., Huang, Y., Sottoriva, A., & Sanguinetti, G. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101342 · Cited by 5 · see citing works
Li, C., Gu, Y., Virgilio, M., Lee, K. H., Collins, K. L., & Welch, J. D. (2025) · Nature Communications · doi:10.1038/s41467-025-66287-6 · Cited by 4 · see citing works
Ngassam, B. T., Niu, H., Pang, S., Shydlouskaya, V., & Andrews, T. S. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1715821 · Cited by 4 · see citing works
Benchmarking RNA velocity methods across 17 independent studies
Luo, Y. H., Ren, J., Yang, Q., You, Z., Zhou, Y., Qin, Q., & Li, Q. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101367 · Cited by 4 · see citing works
Molecularly stratified hypothalamic astrocytes are cellular foci for obesityPreprint
Harkany, T., Tretiakov, E. O., Varela, L., Jarc, J., Rebernik, P., Newbold, S. A., Keimpema, E., Verkhratsky, A., Horváth, T. L., & Romanov, R. A. (2024) · Research Square · doi:10.21203/rs.3.rs-3748581/v1 · Cited by 4 · see citing works
CytoSimplex: visualizing single-cell fates and transitions on a simplex
Liu, J., Wang, Y., Li, C., Gu, Y., Ono, N., & Welch, J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf119 · Cited by 3 · see citing works
Gandrillon, O. (2025) · F1000Research · doi:10.12688/f1000research.164530.2 · Cited by 3 · see citing works
FatePredictor: Cell fate decision-making prediction with an ensemble deep learning model
Shen, J., Chen, N., Niu, B., Zhong, J., & Liu, R. (2025) · The Innovation · doi:10.1016/j.xinn.2025.101010 · Cited by 3 · see citing works
VESNA: an open-source tool for automated 3D vessel segmentation and network analysis
Schüttler, M., Doğan, L., Kirchner, J., Ergün, S., Wörsdörfer, P., & Fischer, S. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06270-6 · Cited by 3 · see citing works
Verma, P. K., Lekkala, S., Usman, B., Lankireddy, S. V., Khadgi, A., d'Agostino, L., Balne, P. K., Patil, G., Sripathi, V. R., & Janga, M. R. (2025) · Plant Biology · doi:10.1111/plb.70131 · Cited by 3 · see citing works
A novel paradigm for single-cell annotation in stem cell research
Atkeson, T., Wilson, S. B., Ruiz-Morales, E. R., Little, M. H., Vento‐Tormo, R., Neavin, D. R., & Powell, J. E. (2025) · Stem Cell Reports · doi:10.1016/j.stemcr.2025.102707 · Cited by 3 · see citing works
Loss of Chromosome Y Associates With Altered Immune Cell Trajectories and X‐Inactivation Features
Dawoud, A. A. Z., Green, L., & Rackham, O. J. L. (2026) · Aging Cell · doi:10.1111/acel.70528 · Cited by 3 · see citing works
- Authors
- Gorin, G., Fang, M., Chari, T., & Pachter, L.
- Affiliations
- California Institute of Technology (US)
- Corresponding author
- Pachter, Lior
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-09-12
- Volume
- 18
- Issue
- 9
- Pages
- e1010492
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC9499228
- OpenAlex
- W4295880122
- Cited by
- 186
- References
- 187
- Citations by year
- 2026: 39 · 2025: 49 · 2024: 45 · 2023: 36 · 2022: 16 · 2021: 1
- FWCI
- 11.92
- Type
- journal article
- Language
- en
- Update notices
- new_version (2022-09-22)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of Mental Health (U19MH114830)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- RNA velocity, single-cell transcriptomics, Markov chain model, parameter sensitivity, method comparison
- MeSH terms
- RNA, Workflow
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review