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@ARTICLE{Aivazidis:299517,
      author       = {A. Aivazidis and F. Memi and V. Kleshchevnikov and S. Er
                      and B. Clarke$^*$ and O. Stegle$^*$ and O. A. Bayraktar},
      title        = {{C}ell2fate infers {RNA} velocity modules to improve cell
                      fate prediction.},
      journal      = {Nature methods},
      volume       = {22},
      number       = {4},
      issn         = {1548-7091},
      address      = {London [u.a.]},
      publisher    = {Nature Publishing Group},
      reportid     = {DKFZ-2025-00475},
      pages        = {698-707},
      year         = {2025},
      note         = {2025 Apr;22(4):698-707},
      abstract     = {RNA velocity exploits the temporal information contained in
                      spliced and unspliced RNA counts to infer transcriptional
                      dynamics. Existing velocity models often rely on coarse
                      biophysical simplifications or numerical approximations to
                      solve the underlying ordinary differential equations (ODEs),
                      which can compromise accuracy in challenging settings, such
                      as complex or weak transcription rate changes across
                      cellular trajectories. Here we present cell2fate, a
                      formulation of RNA velocity based on a linearization of the
                      velocity ODE, which allows solving a biophysically more
                      accurate model in a fully Bayesian fashion. As a result,
                      cell2fate decomposes the RNA velocity solutions into
                      modules, providing a biophysical connection between RNA
                      velocity and statistical dimensionality reduction. We
                      comprehensively benchmark cell2fate in real-world settings,
                      demonstrating enhanced interpretability and power to
                      reconstruct complex dynamics and weak dynamical signals in
                      rare and mature cell types. Finally, we apply cell2fate to
                      the developing human brain, where we spatially map RNA
                      velocity modules onto the tissue architecture, connecting
                      the spatial organization of tissues with temporal dynamics
                      of transcription.},
      cin          = {B260},
      ddc          = {610},
      cid          = {I:(DE-He78)B260-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40032996},
      doi          = {10.1038/s41592-025-02608-3},
      url          = {https://inrepo02.dkfz.de/record/299517},
}