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Abstract
Results: In this study, we introduce COME, a COntrastive Mapping lEarning approach that learns mapping between ST and scRNA-seq data to recover the spatial information of scRNA-seq data. Extensive experiments demonstrate that the proposed COME method effectively captures precise cell-spot relationships and outperforms previous methods in recovering spatial location for scRNA-seq data. More importantly, our method is capable of precisely identifying biologically meaningful information within the data, such as the spatial structure of missing genes, spatial hierarchical patterns, and the cell-type compositions for each spot. These results indicate that the proposed COME method can help to understand the heterogeneity and activities among cells within tissue environments.
Availability and implementation: The COME is freely available in GitHub (https://github.com/cindyway/COME).
© The Author(s) 2025. Published by Oxford University Press.
Original language | English |
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Article number | btaf083 |
Journal | Bioinformatics |
Volume | 41 |
Issue number | 3 |
Online published | 24 Feb 2025 |
DOIs | |
Publication status | Published - Mar 2025 |
Funding
This work was supported in part by the Research Grants Council of the Hong Kong Special Administration Region (Project No. CityU 11206622), in part by Guangdong Basic and Applied Basic Research Foundation (Project No. 2023A1515030154), in part by the Fundamental Research Funds for the Central Universities (Project No. 2024ZYGXZR077), in part by the GuangDong Basic and Applied Basic Research Foundation (Project No. 2024A1515011437), and in part by TCL Science and Technology Innovation Fund (Project No. 20231752).
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
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GRF: Beyond Data Augmentation: Generative Modeling of Close-to-real Training Examples in Machine Learning through Domain Knowledge Injection
WONG, H. S. (Principal Investigator / Project Coordinator)
1/01/23 → …
Project: Research