Additional file 1 of scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics
Creators: name: Li, Qian
Publisher: name: figshare
Year: 2023
Subjects: subject: Biochemistry
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Biotechnology
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: Computational Biology

description: Additional file 1: Fig. S1. Inferred vector field of cells during granule cell differentiation in the mouse dentate gyrus using scTour and scVelo. Fig. S2. Application of scTour to the developing mouse hippocampus dataset. Fig. S3. scTour’s inference is robust to cell subsampling. Fig. S4. scTour captures the developmental cellular dynamics in embryonic organoids. Fig. S5. scTour captures the developmental cellular dynamics in the human thymic epithelial cells. Fig. S6. scTour captures the developmental cellular dynamics in human gastrulation. Fig. S7. scTour captures the developmental cellular dynamics in human preimplantation. Fig. S8. scTour captures the cellular dynamics during iPSC reprogramming in mice. Fig. S9. scTour captures the cellular dynamics during iPSC reprogramming in humans. Fig. S10. scTour captures the developmental cellular dynamics during hematopoiesis. Fig. S11. scTour captures the anatomical topography of brain endothelial cells. Fig. S12. Superiority of scTour’s pseudotime over scVelo’s latent time in characterizing a discontinued process. Fig. S13. Comparisons of scTour’s vector field with velocities from other methods. Fig. S14. scTour predicts the cellular dynamics of unseen cellular states regardless of their positions along the developmental process. Fig. S15. scTour reconstructs the transcriptomic space at different developmental stages. Fig. S16. scTour predicts the latent space of unseen datasets. Fig. S17. Benchmarking scTour against existing methods using the dataset profiling mouse gastrulation and early organogenesis. Fig. S18. Application of scTour to scATAC-seq data. Fig. S19. Delineation of the cycling process by scTour’s vector field and RNA velocity. Fig. S20. Application of scTour to terminally differentiated cells. Fig. S21. Assessment of the parameter alpha in the objective function in a dataset of granule cell differentiation. Fig. S22. Assessment of the parameter alpha in the objective function in a dataset of pyramidal neuron and granule cell development. Fig. S23. Assessment of contributions of z and zt to scTour’s latent representation in a dataset of granule cell development. Fig. S24. Assessment of contributions of z and zt to scTour’s latent representation in a dataset of excitatory neuron development. Fig. S25. Assessment of contributions of z and zt to scTour’s latent representation in a dataset of granule cell and pyramidal neuron development. Fig. S26. Assessment of contributions of z and zt to scTour’s latent representation in a dataset of pancreatic endocrinogenesis.