LineageVAE: reconstructing historical cell states and transcriptomes toward unobserved progenitors
The LineageVAE model was implemented in Python using the PyTorch deep learning library. The code is available on GitHub at https://github.com/LzrRacer/LineageVAE/.
Authors
- Kodai Minoura (ORCID: https://orcid.org/0000-0003-3221-329X)
- Yasuhiro Kojima (ORCID: https://orcid.org/0000-0002-1363-7153)
- Haruka Hirose (ORCID: https://orcid.org/0009-0007-3827-257X)
- Koichiro Majima (ORCID: https://orcid.org/0009-0006-5099-7680)
- Ko Abe (ORCID: https://orcid.org/0000-0002-0489-8677)
- Teppei Shimamura (ORCID: https://orcid.org/0009-0009-7713-2531)
Institutions
- Tokyo Medical and Dental University (JP)
- National Cancer Centre Japan (JP)
- Japanese Red Cross Nagoya Daiichi Hospital (JP)
- National Cancer Research Institute (GB)
- Nagoya University (JP)
Publication Details
- Journal
- Bioinformatics
- Published
- 2024-08-20
- DOI
- https://doi.org/10.1093/bioinformatics/btae520
- Citations
- 6
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 1.40
Funders
- Japan Society for the Promotion of Science