Bayesian Integrative Region Segmentation in Spatially Resolved Transcriptomic Studies
Author:
Affiliation:
1. Institute of Statistics and Big Data, Renmin University of China
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/01621459.2024.2308323
Reference42 articles.
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2. Chang Y. F. He J. Wang S. Chen J. Li J. Liu Y. Yu L. Su A. Ma C. Allen et al. (2022). Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning. Computational and Structural Biotechnology Journal 20 4600–4617.
3. Chen W.-T. A. Lu K. Craessaerts B. Pavie C. S. Frigerio N. Corthout X. Qian J. Laláková M. Kühnemund I. Voytyuk et al. (2020). Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell 182 (4) 976–991.
4. Chidester B. T. Zhou S. Alam and J. Ma (2023). SpiceMix enables integrative single-cell spatial modeling of cell identity. Nature Genetics 1–11.
5. Close J. L. B. R. Long and H. Zeng (2021). Spatially resolved transcriptomics in neuroscience. Nature Methods 18 (1) 23–25.
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