DANST enables cell-type deconvolution in spatial transcriptomics using deep domain adversarial neural networks

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DANST enables cell-type deconvolution in spatial transcriptomics using deep domain adversarial neural networks

References

  1. Smith, K. D., Prince, D. K., MacDonald, J. W., Bammler, T. K. & Akilesh, S. Challenges and opportunities for the clinical translation of spatial transcriptomics technologies. Glomerular Dis. 4, 49–63 (2024).

    Google Scholar 

  2. Asp, M., Bergenstråhle, J. & Lundeberg, J. Spatially resolved transcriptomes—next generation tools for tissue exploration. Bioessays 42, e1900221 (2020).

    Google Scholar 

  3. 10x Genomics. https://www.10xgenomics.com/resources/datasets/ (2023).

  4. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Google Scholar 

  5. Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681 (2020).

    Google Scholar 

  6. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792 (2022).

    Google Scholar 

  7. Fu, X. et al. Polony gels enable amplifiable DNA stamping and spatial transcriptomics of chronic pain. Cell 185, 4621–4633 (2022).

    Google Scholar 

  8. Cho, C.-S. et al. Microscopic examination of spatial transcriptome using seq-scope. Cell 184, 3559–3572.e22 (2021).

    Google Scholar 

  9. Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).

    Google Scholar 

  10. Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022).

    Google Scholar 

  11. Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021).

    Google Scholar 

  12. Andersson, A. et al. Spatial mapping of cell types by integration of transcriptomics data. Commun. Biol. 3, 77 (2020).

    Google Scholar 

  13. Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).

    Google Scholar 

  14. Garmire, L. X. et al. Challenges and perspectives in computational deconvolution of genomics data. Nat. Methods 21, 391–400 (2024).

    Google Scholar 

  15. Xu, H. et al. SPACEL: deep learning-based characterization of spatial transcriptome architectures. Nat. Commun. 14, 7603 (2023).

    Google Scholar 

  16. Li, H., Li, H., Zhou, J. & Gao, X. SD2: spatially resolved transcriptomics deconvolution through integration of dropout and spatial information. Bioinformatics. 38, 4878–4884 (2022).

    Google Scholar 

  17. Long, Y. et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat. Commun. 14, 1155 (2023).

    Google Scholar 

  18. Wang, Y., Wan, Y. & Zhou, Y. SpatialcoGCN: deconvolution and spatial information–aware simulation of spatial transcriptomics data via deep graph co-embedding. Brief. Bioinform. 25, bbae130 (2024).

    Google Scholar 

  19. Coleman, K. et al. SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning. Commun. Biol. 6, 378 (2023).

    Google Scholar 

  20. Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods 18, 1352–1362 (2021).

    Google Scholar 

  21. Bae, S. et al. CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data. Nucleic Acids Res. 50, e57 (2022).

    Google Scholar 

  22. Bae, S., Choi, H. & Lee, D. S. spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data. Genome Med. 15, 19 (2023).

    Google Scholar 

  23. Liu, Z. et al. SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics. Nat. Commun. 14, 4727 (2023).

    Google Scholar 

  24. Davis, J. & Goadrich, M. An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006).

    Google Scholar 

  25. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Google Scholar 

  26. Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature. 563, 72–78 (2018).

    Google Scholar 

  27. Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 53, 1334–1347 (2021).

    Google Scholar 

  28. Yang, W. et al. Single-cell RNA reveals a tumorigenic microenvironment in the interface zone of human breast tumors. Breast Cancer Res. 25, 100 (2023).

    Google Scholar 

  29. Kumar, T. et al. A spatially resolved single-cell genomic atlas of the adult human breast. Nature 620, 181–191 (2023).

    Google Scholar 

  30. Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387 (2018).

    Google Scholar 

  31. Carron, E. C. et al. Macrophages promote the progression of premalignant mammary lesions to invasive cancer. Oncotarget 8, 50731–50746 (2017).

    Google Scholar 

  32. Hu, Q. et al. Atlas of breast cancer infiltrated B-lymphocytes revealed by paired single-cell RNA-sequencing and antigen receptor profiling. Nat. Commun. 12, 2186 (2021).

    Google Scholar 

  33. Roy, M., Fowler, A. M., Ulaner, G. A. & Mahajan, A. Molecular Classification of Breast Cancer. PET Clin. 18, 441–458 (2023).

    Google Scholar 

  34. Song, Y. et al. DDHD2 is involved in the malignant progression of early luminal A breast cancer by changing cell membrane proteins and immune responses functionality. Oncol. Transl. Med. 10, 231–244 (2024).

    Google Scholar 

  35. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Google Scholar 

  36. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Google Scholar 

  37. McCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

    Google Scholar 

  38. Fraley, C., Raftery, A. E., Murphy, T. B. & Scrucca, L. mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Report No. 597 (University of Washington, 2012).

  39. Ganin, Y. et al. Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17, 1–35 (2016).

    Google Scholar 

  40. Fey, M. & Lenssen, J. E. Fast graph representation learning with PyTorch geometric. ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds. (ICLR, 2019).

  41. Bock, S., & Weiß, M. A proof of local convergence for the Adam optimizer. IEEE International Joint Conference on Neural Networks (IEEE, 2019).

  42. Kleshchevnikov V., et al. Single-nucleus RNA-seq from adult mouse brain sections paired to 10X Visium spatial RNA-seq. E-MTAB-11115, (EMBL-EBI, 2022).

  43. Li, M. et al. DISCO: a database of deeply integrated human singlecell omics data. Nucleic Acids Res. 50, D596–D602 (2022).

    Google Scholar 

  44. Wu, Z. Processed datasets for DANST enables cell-type deconvolution in spatial transcriptomics using deep domain adversarial neural networks. Zenodo https://doi.org/10.5281/zenodo.18213061 (2026).

  45. Wu, Z. ZhichaoWu7/DANST: First version for publication (v1.0.0). Zenodo https://doi.org/10.5281/zenodo.18212150 (2026).

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