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References

This page lists the core literature and software references used throughout Cafe's documentation. The bibliography is rendered directly from docs/references.bib.

  1. Zhaoyang Huang, Haonan Ma, Yuchuan Peng, Chenguang Zhao, and Liang Yu. Cafe: an integrated platform for exploring cell fate. bioRxiv, pages 2025.02.04.636565, 2026. doi:10.1101/2025.02.04.636565. ↩

  2. Isaac Virshup, Sergei Rybakov, Fabian J. Theis, Philipp Angerer, and F. Alexander Wolf. Anndata: access and store annotated data matrices. Journal of Open Source Software, 9(101):4371, 2024. doi:10.21105/joss.04371. ↩

  3. F. Alexander Wolf, Philipp Angerer, and Fabian J. Theis. SCANPY: large-scale single-cell gene expression data analysis. Genome Biology, 19(1):15, 2018. doi:10.1186/s13059-017-1382-0. ↩

  4. Wouter Saelens, Robrecht Cannoodt, Helena Todorov, and Yvan Saeys. A comparison of single-cell trajectory inference methods. Nature Biotechnology, 37(5):547–554, 2019. doi:10.1038/s41587-019-0071-9. ↩

  5. F. Alexander Wolf, Fiona K. Hamey, Mireya Plass, Jordi Solana, Joakim S. Dahlin, Berthold Gottgens, Nikolaus Rajewsky, Lukas Simon, and Fabian J. Theis. Paga: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biology, 20(1):59, 2019. doi:10.1186/s13059-019-1663-x. ↩

  6. Manu Setty, Vaidotas Kiseliovas, Jacob Levine, Adam Gayoso, Linas Mazutis, and Dana Pe'er. Characterization of cell fate probabilities in single-cell data with palantir. Nature Biotechnology, 37(4):451–460, 2019. doi:10.1038/s41587-019-0068-4. ↩

  7. Gunsagar S. Gulati, Sharmila S. Sikandar, David J. Wesche, Anoop Manjunath, Aditya Bharadwaj, Michael J. Berger, Francisco Ilagan, Angera H. Kuo, Robert W. Hsieh, Stanley Cai, Maria Zabala, F. Patrick Scheeren, Neil A. Lobo, David Qian, Frank B. Yu, Frederick M. Dirbas, Michael F. Clarke, and Aaron M. Newman. Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome. Science, 367(6476):405–411, 2020. doi:10.1126/science.aax0249. ↩

  8. Anthony Gitter. Single-cell rna-seq pseudotime estimation algorithms. \url https://github.com/agitter/single-cell-pseudotime, 2018. doi:10.5281/zenodo.1297422. ↩

  9. Gioele La Manno, Ruslan Soldatov, Amit Zeisel, Emelie Braun, Hannah Hochgerner, Viktor Petukhov, Katja Lidschreiber, Maria E. Kastriti, Peter Lonnerberg, Alessandro Furlan, Jean Fan, Lars E. Borm, Zehua Liu, David van Bruggen, Jimin Guo, Xiaoling He, Roger Barker, Erik Sundstrom, Goncalo Castelo-Branco, Patrick Cramer, Igor Adameyko, Sten Linnarsson, and Peter V. Kharchenko. Rna velocity of single cells. Nature, 560(7719):494–498, 2018. doi:10.1038/s41586-018-0414-6. ↩

  10. Volker Bergen, Marius Lange, Stefan Peidli, F. Alexander Wolf, and Fabian J. Theis. Generalizing rna velocity to transient cell states through dynamical modeling. Nature Biotechnology, 38(12):1408–1414, 2020. doi:10.1038/s41587-020-0591-3. ↩

  11. Marius Lange, Volker Bergen, Michal Klein, Manu Setty, Bernhard Reuter, Mostafa Bakhti, Heiko Lickert, Meshal Ansari, Jorg Schniering, Herbert B. Schiller, Dana Pe'er, and Fabian J. Theis. Cellrank for directed single-cell fate mapping. Nature Methods, 19(2):159–170, 2022. doi:10.1038/s41592-021-01346-6. ↩

  12. Xiaojie Qiu, Yan Zhang, Jose D. Martin-Rufino, Chen Weng, Sam Hosseinzadeh, Dian Yang, Alexandra N. Pogson, Marco Y. Hein, Kyung Hoi Min, Li Wang, Eric I. Grody, Marc J. Shurtleff, Rui Yuan, Shou-Wen Xu, Yuhan Ma, Joseph M. Replogle, Eric S. Lander, Spyros Darmanis, Ivet Bahar, Jianhua Xing, Jonathan S. Weissman, Peter V. Kharchenko, Aviv Regev, Joan Massague, Nir Hacohen, Rapolas Zilionis, Itai Yanai, Nikolaus Rajewsky, Allon M. Klein, and Cole Trapnell. Mapping single-cell transcriptomic vector fields of cellular state transition. Cell, 185(4):690–711.e45, 2022. doi:10.1016/j.cell.2021.12.045. ↩

  13. Mingze Gao, Chen Qiao, and Yuanhua Huang. Unitvelo: temporally unified rna velocity reinforces single-cell trajectory inference. Nature Communications, 13(1):6586, 2022. doi:10.1038/s41467-022-34188-7. ↩

  14. Zhanlin Chen, William C. King, Aheyon Hwang, Mark Gerstein, and Jing Zhang. Deepvelo: single-cell transcriptomic deep velocity field learning with neural ordinary differential equations. Science Advances, 8(48):eabq3745, 2022. doi:10.1126/sciadv.abq3745. ↩

  15. Shengyu Li, Pengzhi Zhang, Weiqing Chen, Lingqun Ye, Kristopher W. Brannan, and others. A relay velocity model infers cell-dependent rna velocity. Nature Biotechnology, 41:1175–1185, 2023. doi:10.1038/s41587-023-01728-5. ↩

  16. Sara Aibar, Carmen Bravo Gonzalez-Blas, Thomas Moerman, Vân Anh Huynh-Thu, Hana Imrichova, Gert Hulselmans, Florian Rambow, Jean-Christophe Marine, Pierre Geurts, Jan Aerts, Jeroen van den Oord, Zeynep Kalender Atak, Jasper Wouters, and Stein Aerts. SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14(11):1083–1086, 2017. doi:10.1038/nmeth.4463. ↩

  17. Kenji Kamimoto, Benedetta Stringa, Christoph M. Hoffmann, Krishan Jindal, Lilianna Solnica-Krezel, and Samantha A. Morris. Dissecting cell identity via network inference and in silico gene perturbation. Nature, 614(7949):742–751, 2023. doi:10.1038/s41586-022-05688-9. ↩