Published June 25, 2024
| Version v1
Dataset
Open
SpatialMETA: A Novel Framework for Integrating Spatial Transcriptomics and Metabolomics Data
Authors/Creators
Contributors
Data collector:
Description
Multimodal analysis of spatial transcriptomics (ST) and spatial metabolomics (SM) has rapidly advanced for characterizing tissue microenvironments. However, integrating ST and SM data remains challenging due to differing morphologies, resolutions, and batch effects. We developed SpatialMETA (Spatial Metabolomics and Transcriptomics Analysis), a novel method for integrating spatial multi-omics data, which aligns ST and SM to a unified resolution, enables both cross-modal and cross-sample integration to identify ST-SM associated spatial patterns, and provides extensive visualization and analysis capabilities. The datasets for SpatialMETA is avaiable.
Files
ccRCC_SM_merge_data.zip
Additional details
Dates
- Submitted
-
2024-06-25
Software
- Repository URL
- https://github.com/WanluLiuLab/SpatialMETA
References
- Sun, C. et al. Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer. Nat. Commun. 14, 2692 (2023).
- Hu, J. et al. Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression. Nat. Genet. 56, 442–457 (2024).
- Ravi, V. M. et al. Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma. Cancer Cell 40, 639-655.e13 (2022).
- Zheng, P. et al. Integrated spatial transcriptome and metabolism study reveals metabolic heterogeneity in human injured brain. Cell Rep. Med. 4, 101057 (2023).
- Vicari, M. et al. Spatial multimodal analysis of transcriptomes and metabolomes in tissues. Nat. Biotechnol. (2023) doi:10.1038/s41587-023-01937-y.