Published 2026 | Version v1
Dataset Open

Supplementary Material for "stCAMBL: Biased Multi-view Contrastive Learning with Attentive Masking for Spatial Transcriptomic Analysis"

Files

10X.zip

Files (2.7 GB)

Name Size Download all
md5:fb4b92b0cdfa7a11b832d5feb0ae535c
202.8 MB Preview Download
md5:5c5a0cb8858e0dad982441b91745de63
343.6 MB Download
md5:ed8b9e5942608770ef276fef8be058d7
463.4 MB Download
md5:c394302573b5a90e0b8396790705fd87
269.9 MB Preview Download
md5:85a5f949e03d43f3e17afe50fa079bc2
95.6 MB Preview Download
md5:5bd684f793b00c1bcf0b879b6d7b2b4a
364.4 MB Preview Download
md5:34c65e5a042a17b83b8d86bcf85fc177
255.7 MB Download
md5:491c15ce99f7908fb739d310071276e3
243.5 MB Download
md5:7e497941ef616d5e0ca5356ff73af07d
245.0 MB Download
md5:876208d30de7f8cb856a4e2c46f8f225
189.5 MB Preview Download

Additional details

Dates

Submitted
2025-11-20

References

  • Jain S, Eadon M T. Spatial transcriptomics in health and disease. Nature Reviews Disease Primers 2024;10:1-15. https://doi.org/10.1038/s41581-024-00841-1
  • Yang J et al. Advances in spatial transcriptomics and its applications in cancer research. Molecular Cancer 2024;23:129. https://doi.org/10.1186/s12943-024-02040-9
  • Svensson V, Teichmann S A, Stegle O. SpatialDE: identification of spatially variable genes. Nature Methods 2018;15(5):343-346. https://doi.org/10.1038/nmeth.4636
  • Edsg¨ard D, Johnsson P, Sandberg R. Identification of spatial expression trends in single-cell gene expression data. Nature Methods 2018;15(5):339-342. https://doi.org/10. 1038/nmeth.4634
  • Sun S, Zhu J, Zhou X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nature Methods 2020;17(2):193-200. https://doi. org/10.1038/s41592-019-0701-7
  • Zhao E et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nature Biotechnology 2021;39(11):1375 1384. https://doi.org/10.1038/s41587-021-00935-2
  • Hu J et al. SpaGCN: Integrating gene expression, spatial location and histology for spatial transcriptomics analysis. Nature Methods 2021;18:1343-1351. https://doi.org/10. 1038/s41592-021-01255-8
  • PhamDTetal. stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell–cell interactions and spatial trajectories within undissociated tissues. bioRxiv 2020. https://doi.org/10.1101/2020.05. 31.125658
  • Long Y et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nature Communications 2023;14(1):1155. https://doi.org/ 10.1038/s41467-023-36796-3
  • Xiong Z, Luo J, Shi W et al. scGCL: an imputation method for scRNA-seq data based on Graph Contrastive Learning. Bioinformatics 2023;39(3). https://doi.org/10. 1093/bioinformatics/btad098
  • Zong Y et al. conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics. bioRxiv 2022. https://doi.org/10.1101/2022.01.14.476408
  • Ma A et al. DeepMAPS: Single-cell biological network inference using a heterogeneous graph transformer. Nature Communications 2023;14. https://doi.org/10. 1038/s41467-023-36559-0
  • Khan M et al. A comprehensive review of spatial transcriptomics data: methods, challenges and opportunities. Nucleic Acids Research 2025;53(12). https: //academic.oup.com/nar/article/53/12/gkaf536/8174767
  • Kang L et al. Benchmarking computational methods for detecting spatial domains and spatially variable genes. Nucleic Acids Research 2025;53(7). https://academic.oup. com/nar/article/53/7/gkaf303/8114322
  • Du M R M et al. Benchmarking spatial transcriptomics technologies with the SpatialBenchVisium reference. Genome Biology 2025. https://genomebiology. biomedcentral.com/articles/10.1186/s13059-025-03543-4
  • Ren P et al. Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors. Nature Communications 2025. https://www. nature.com/articles/s41467-025-64292-3
  • Li S, Zhang L, Wang Z, Wu D, Wu L, Liu Z. Masked Modeling for Self-supervised Representation Learning: A Survey. arXiv 2023. https://arxiv.org/abs/2401.00897
  • Hondru V, Croitoru F A, Minaee S, Ionescu R T, Sebe N. Masked Image Modeling: A Survey. arXiv / Int. J. Computer Vision 2024. https://arxiv.org/abs/2408. 06687v1
  • Xu X, Lian J et al. Negative Sampling in Recommendation: A Survey and Lessons. arXiv 2024. https://arxiv.org/ html/2409.07237v1
  • Lin Y et al. A contrastive learning approach to integrate spatial transcriptomics and histology for tissue architecture identification (ConGcR). Computational & Structural Biotechnology Journal 2024. https://www.sciencedirect. com/science/article/pii/S200103702400120X
  • Risso D, et al. A general and flexible method for signal extraction from single-cell RNA-seq data. Nature Communications. 2018;9:284. https://doi.org/10.1038/ s41467-017-02554-5
  • Yu J, Luo X. Identification of cell-type-specific spatially variable genes accounting for multiple cell types. Bioinformatics. 2022;38(17):4135–4143. https://doi.org/10.1093/bioinformatics/btac457
  • Svensson V. Droplet scRNA-seq is not zero-inflated. Nature Biotechnology. 2020;38(2):147–150. https://doi.org/10. 1038/s41587-019-0379-5
  • hao P, Zhu J, Ma Y, Zhou X. Modeling zero inflation is not necessary for spatial transcriptomics. Genome Biology. 2022;23:1–19. https://doi.org/10.1186/ s13059-022-02684-0
  • Zhao Q, Zhang Q. High-dimensional Bayesian model for disease-specific gene detection in spatial transcriptomics: incorporating spatial correlation among tissue spots. arXiv preprint. 2024. Available at: https://arxiv.org/abs/2409. 02397
  • Veliˇckovi´c P, Fedus W, Hamilton WL, Li`o P, Bengio Y, Hjelm RD. Deep Graph Infomax. In: Proceedings of the 6th International Conference on Learning Representations (ICLR 2018). Available at: https://arxiv.org/abs/1809. 10341
  • Zhang L, et al. MuCoST: A multi-view graph contrastive learning framework for deciphering spatially resolved transcriptomics data. Briefings in Bioinformatics. 2024. Available at: https://academic.oup.com/bib/article/25/4/ bbae255/7683165
  • Zhao J, Min W. SpaICL: image-guided curriculum strategy-based graph contrastive learning for spatial transcriptomics clustering. Briefings in Bioinformatics 2025;26(4). https://academic.oup.com/bib/article/doi/10. 1093/bib/bbaf433/8239088
  • He D, Zhang Y, Zhang Y, Dong Y, Chen E. A New Mechanism for Eliminating Implicit Conflict in Graph Contrastive Learning. In: AAAI Conf. Artificial Intelligence 2024;38(11):12340-12348. https://ojs.aaai.org/index.php/ AAAI/article/view/29125
  • Skuntz S, Mankoo B, Nguyen M-T T et al. Lack of the mesodermal homeodomain protein MEOX1 disrupts sclerotome polarity and leads to a remodeling of the craniocervical joints of the axial skeleton. Developmental Biology 2009;332(2):383-395. https://doi.org/10.1016/j. ydbio.2009.06.006
  • Linehan W M, Ricketts C J. The Cancer Genome Atlas of renal cell carcinoma: findings and clinical implications. Nature Reviews Urology 2019;16(9):539-552. https://doi. org/10.1038/s41585-019-0211-5
  • Pek J, Zhou X. CELINA: Cell type-aware spatial domain identification and integration in spatial transcriptomics. 2025. Available at: https://doi.org/10.5281/zenodo. 14212866. doi:10.5281/zenodo.14212866
  • amouille S, Xu J, Derynck R. Molecular mechanisms of epithelial–mesenchymal transition. Nature Reviews Molecular Cell Biology 2014;15(3):178-196. https://doi. org/10.1038/nrm3758
  • Turajlic S et al. Deterministic evolutionary trajectories influence primary tumour growth: TRACERx Renal. Cell 2018;173(3):595-610.e11. https://doi.org/10.1016/j.cell. 2018.03.043
  • Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology. 2014;32(4):381–386. https://doi. org/10.1038/nbt.2859
  • Liu L et al. S100A11 regulates renal carcinoma cell proliferation, invasion, and migration via EGFR/Akt. Tumor Biology 2017;39(5):https://doi.org/10.1177/ 1010428317705337
  • Sugiyama T et al. Expression Profile of S100A2 and its Clinicopathological Significance in Renal Cell Carcinoma. Anticancer Research. 2020;40(11):6337-6343. https://doi. org/10.21873/anticanres.14654
  • Xie X et al. Acyl-CoA Thioesterase 7 is Transcriptionally Activated by Kr¨uppel-Like Factor 13 and Promotes the Progression of Hepatocellular Carcinoma. Journal of Hepatocellular Carcinoma 2021;8. https://doi.org/10. 2147/JHC.S338353
  • Liberzon A, Birger C, Thorvaldsd´ottir H et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Systems 2015;1(6):417-425. https://doi. org/10.1016/j.cels.2015.12.004
  • Subramanian A et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences 2005;102(43):15545-15550. https://doi.org/10. 1073/pnas.0506580102
  • Semenza G L. Hypoxia-inducible factors in physiology and medicine. Cell 2012;148(3):399-408. https://doi.org/10. 1016/j.cell.2012.01.021
  • Mallach A, Zielonka M, van Lieshout V et al. Microglia- astrocyte crosstalk in the amyloid plaque niche of an Alzheimer's disease mouse model, as revealed by spatial transcriptomics. Cell Reports 2024;43(6):114216. https: //doi.org/10.1016/j.celrep.2024.114216
  • Chen W T et al. Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer's Disease. Cell 2020;182(4):976-991.e19. https://doi.org/10.1016/j.cell. 2020.06.038
  • Ardura-Fabregat A et al. Response of spatially defined microglia states with distinct transcriptomic profiles to amyloid- plaques in Alzheimer's disease. Nature Neuroscience 2025;28(5):639-650. https: //doi.org/10.1038/s41593-025-02006-0
  • Hemonnot-Girard A L et al. Comparative analysis of transcriptome remodeling in plaque-associated and plaque-free microglia in Alzheimer's disease. Journal of Neuroinflammation 2022;19(1):1-16. https://doi.org/10. 1186/s12974-022-02581-0
  • Keren-Shaul H, Spinrad A, Weiner A, et al. A unique microglia type associated with restricting development of Alzheimer's disease. Cell 2017;169(7):1276–1290.e17. https: //doi.org/10.1016/j.cell.2017.05.018
  • Lui H, Zhang J, Makinson S R, et al. Progranulin deficiency promotes circuit-specific synaptic pruning by microglia via complement activation. Cell 2016;165(4):921–935. https:// doi.org/10.1016/j.cell.2016.04.001
  • Zhao Z, Sagare A P, Ma Q, et al. Central role for PICALM in amyloid- blood–brain barrier transcytosis and clearance. Nature Communications 2015;6:8969. https://doi.org/10. 1038/nn.4025
  • Hemonnot-Girard A-L, Carpentier R, Bourgin C, et al. Comparative analysis of transcriptome remodeling in plaque-associated and plaque-free microglia in Alzheimer's disease. Journal of Neuroinflammation 2022;19(1):168. https://doi.org/10.1186/s12974-022-02581-0
  • Kristen R. Maynard et al. "Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex". In: Nature Neuroscience 24.3 (2021), pp. 425–436. doi: 10.1038/s41593-020-00787-0.
  • 10xGenomics. Visium HD Spatial Gene Expression Library — Human Lung Cancer (Visium dataset). https://www.10xgenomics.com/datasets/ visium-hd-cytassist-gene-expression-libraries-of-human-lung cancer-if. 10x Genomics dataset page; download citation instructions on 10x website. 2024.
  • R. Li and et al. Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer (Spatial transcriptomics data). Mendeley Data. Mendeley dataset with samples from tumour core, tumour normal interface, etc.; see dataset page. 2022.
  • QianJingyang. FowardYang/scSpace datasets: v1.0.1. Version v1.0.1. Apr. 15, 2023. doi: 10.5281/zenodo.7830764. url: https://doi.org/10.5281/ zenodo.7830764.
  • H. Shi, X. Wang, and et al. "Spatial atlas of the mouse central nervous system at molecular resolution". In: Nature (2023). STARmap / STARmap PLUSmousebrain datasets (used as mouse brain / hippocampus reference). doi: 10.1038/s41586-023-06569-5.
  • A. Author and B. Author. "The NanoString GeoMx® Digital Spatial Pro f iler: platform and applications". In: Frontiers in Oncology (2022). Ge oMx platform descriptive article — cite when referencing GeoMx technol ogy/datasets. doi: 10.3389/fonc.2022.890410.
  • A. Chen et al. "Spatiotemporal transcriptomic atlas of mouse organogenesis using Stereo-seq". In: Cell (2022). Stereo-seq whole-embryo datasets (E9.5,E10.5) — see Cell article and associated data repositories. doi: 10.1016/ j.cell.2022.04.003.
  • J. Pek and X. Zhou. GSE270392: Spatially resolved multi-omic single-cell atlas of soybean development. https://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?acc=GSE270392. 2025.
  • Joonwu Pek and Xiang Zhou. "A spatially resolved multi-omic single-cell atlas of soybean development". In: Nature / preprint (2025). doi: 10.5281/ zenodo.14212866.