scEGOT: single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport
These findings shed light on the mechanism that segregates PGCLC from somatic lineages. Notably, not limited to scRNA-seq, scEGOT's versatility can extend to general single-cell data like scATAC-seq, and hence has the potential to revolutionize our understanding of such datasets and, thereby also, developmental biology.
Authors
- Mitinori Saitou (ORCID: https://orcid.org/0000-0002-2895-6798)
- Momoko Yoshida
- Toshiaki Yachimura (ORCID: https://orcid.org/0009-0009-6372-1212)
- Yukihiro Yabuta (ORCID: https://orcid.org/0000-0002-6437-1201)
- Yoji Kojima (ORCID: https://orcid.org/0000-0002-4694-1477)
- Yasuaki Hiraoka (ORCID: https://orcid.org/0000-0002-1023-2687)
- Hanbo Wang
- Sohei Tasaki (ORCID: https://orcid.org/0000-0002-4259-0075)
- Yusuke Imoto (ORCID: https://orcid.org/0000-0003-2574-4471)
Institutions
- Tohoku University (JP)
- Hokkaido University (JP)
- Kyoto University (JP)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-12-23
- DOI
- https://doi.org/10.1186/s12859-024-05988-z
- Citations
- 14
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 3.27
Funders
- Japan Agency for Medical Research and Development
- Open Philanthropy Project
- Japan Society for the Promotion of Science
- Core Research for Evolutional Science and Technology
- Precursory Research for Embryonic Science and Technology
- JST-Mirai Program