| 项目编号 |
PRJCA030105 |
| 项目标题 |
Interpretable high-resolution dimension reduction of spatial transcriptomics data by DeepFuseNMF |
| 涉及领域 |
Medical |
| 数据类型 |
Transcriptome or Gene expression
Spatial transcriptomics
|
| 物种名称 |
Homo sapiens
|
| 描述信息 |
Spatial transcriptomics (ST) technologies have revolutionized tissue architecture studies by capturing gene expression profiles along with spatial context. However, the high-dimensional ST data often have limited spatial resolution and exhibit considerable noise and sparsity, posing significant challenges for deciphering subtle spatial patterns. To address these challenges, we introduce DeepFuseNMF, a novel multi-modal dimensionality reduction framework that enhances spatial resolution by integrating low-resolution ST data with high-resolution histology images. DeepFuseNMF incorporates nonnegative matrix factorization into a neural network architecture for interpretable high-resolution embedding learning and spatial domain detection. Furthermore, DeepFuseNMF can simultaneously handle multiple samples and is compatible with various types of histology images. Extensive evaluations using synthetic and real ST datasets from various technologies and tissues demonstrate DeepFuseNMF's ability to produce highly interpretable, high-resolution embeddings and detect refined spatial structures. DeepFuseNMF represents a powerful multi-modality integration approach for ST data and histology images, paving the way for understanding complex tissue structures and functions. |
| 样品范围 |
Single cell |
| 发布日期 |
2025-10-20 |
| 出版信息 |
| PubMed ID |
文章标题 |
杂志名称 |
Doi |
发表年份 |
| 41495202
|
The interpretable multimodal dimension reduction framework SpaHDmap enhances resolution in spatial transcriptomics
|
Nature Cell Biology
|
10.1038/s41556-025-01838-z
|
2026
|
|
|
| 实验材料提供者 |
Tianjin Medical University Cancer Institute & Hospital |
| 项目资金来源 |
| 机构 |
项目类型 |
授权项目ID |
授权项目名称 |
| National Natural Science Foundation of China (NSFC)
|
|
12425110
|
|
| National Natural Science Foundation of China (NSFC)
|
|
12371286
|
|
| National Natural Science Foundation of China (NSFC)
|
|
11971039
|
|
| Ministry of Science and Technology of the People's Republic of China (MOST)
|
National Key Technologies R&D Program
|
2020YFE0204200
|
|
| Science and Technology Project of Tianjin Binhai New Area Health Commission
|
|
2022BWKY016
|
|
|
|
| 提交者 |
Ruibin
Xi (ruibinxi@math.pku.edu.cn)
|
| 提交单位 |
Peking University |
| 提交日期 |
2024-09-12 |
