Generalizing RNA velocity to transient cell states through dynamical modeling

By Volker Bergen, Marius Lange, Stefan Peidli, F. Alexander Wolf, Fabian J Theis. Nature Biotechnology, 2019

2,552 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4634699f1eb2d853afe63b6965fc558db23756e5

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In Molecular Biology

Citations in the corpus

The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans (2021, background): “Latent time analysis, which provides an estimate of each cell’s internal clock using RNA velocity trajectories (28), correctly identified the direction of differentiation (Fig.”
Mapping single-cell data to reference atlases by transfer learning (2021, methodology): “Cell type labels were obtained from an adata object provided by the authors of scVelo 46 .”
Mapping Transcriptomic Vector Fields of Single Cells (2022, positive): “18 Cell 185, 1–22, February 17, 2022 from (Bergen et al., 2020), butwithout assuming a constant transcription rate, to further improve estimates for absolute kinetic parameters and velocity.”
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics (2020, methodology): “While RNA-velocity provides some estimate of relative speed, the vector length is considered not as informative, as it is unclear how to normalize these vectors in a system speciﬁc way (La Manno et al., 2018; Bergen et al., 2019).”
Spatial components of molecular tissue biology (2022, positive): “A key innovation in the field of trajectory inference from single-cell profiles has been the discovery of molecular states that relate to velocities in the state space (RNA velocity 67,82 ).”
Evolutionary velocity with protein language models (2021, positive): “embedding space (assuming that embeddings are available for all sequences) as done previously 462 [51].”
CellRank for directed single-cell fate mapping (2020, neutral): “Choosing Schur vectors with real eigenvalues close to one, thus recovering metastability, maximizing Equation (9) can be interpreted as maximizing the metastability of the macrostates in the system.”
Pan-cancer single-cell landscape of tumor-infiltrating T cells (2021, background): “First, on a global scale, the diffusionmap (25) and RNA velocity (26) showed that CD8 T cells could develop from naïve T cells to either Temra or Tex cells (fig.”