The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Unsupervised spatially embedded deep representation of spatial transcriptomics
Open accessAbstract
Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR’s ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/).
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Integrated analysis of multimodal single-cell data
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Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays
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Self-citationTranscriptome-scale super-resolved imaging in tissues by RNA seqFISH
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Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2
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Cited by
Dong, K., & Zhang, S. (2022) · Nature Communications · doi:10.1038/s41467-022-29439-6 · Cited by 761
Long, Y., Ang, K. S., Li, M., Chong, K. L. K., Sethi, R., Zhong, C., Xu, H., Ong, Z., Sachaphibulkij, K., Chen, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36796-3 · Cited by 700
Self-citationComputational approaches and challenges in spatial transcriptomics
Fang, S., Chen, B., Zhang, Y., Sun, H., Liu, L., Liu, S., Li, Y., & Xu, X. (2023) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.10.001 · Cited by 159
Self-citationMultimodal contrastive learning for spatial gene expression prediction using histology images
Min, W., Shi, Z., Zhang, J., Wan, J., & Wang, C. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae551 · Cited by 90
Graph neural networks for single-cell omics data: a review of approaches and applications
Li, S., Hua, H., & Chen, S. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf109 · Cited by 43
Metadata
- Authors
- Xu, H., Fu, H., Long, Y., Ang, K. S., Sethi, R., Chong, K., Li, M., Uddamvathanak, R., Lee, H. K., Ling, J., Chen, A., Shao, L., Liu, L., & Chen, J.
- Venue
- Genome Medicine
- Published
- 2024-01-12
- Volume
- 16
- Issue
- 1
- Pages
- 12-12
- ISSN
- 1756-994X (ISSN-L)
- OpenAlex
- W4390814844
- Cited by
- 391
- References
- 55
- FWCI
- 43.11
- Type
- journal article
- Language
- en
- Open access
- gold
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- Xu, H., Fu, H., Long, Y., Ang, K. S., Sethi, R., Chong, K., Li, M., Uddamvathanak, R., Lee, H. K., Ling, J., Chen, A., Shao, L., Liu, L., & Chen, J. (2024). Unsupervised spatially embedded deep representation of spatial transcriptomics. Genome Medicine. https://doi.org/10.1186/s13073-024-01283-x
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