1. |
Blondel V D, Guillaume J L, Lambiotte R, et al. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008: P10008.
|
2. |
Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell, 2019, 177(7): 1888-1902.
|
3. |
Zhao E, Stone M R, Ren X, et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nature Biotechnology, 2021, 39(11): 1375-1384.
|
4. |
Xu Y, McCord R P. CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis. BMC Bioinformatics, 2021, 22(1): 397.
|
5. |
Reynolds D. Gaussian mixture models. Encyclopedia of Biometrics, 2018: 659-663.
|
6. |
Yuan Y, Bar-Joseph Z. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biology, 2020, 21(1): 300.
|
7. |
Hu J, Li X, Coleman K, et al. SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nature Methods, 2021, 18(11): 1342-1351.
|
8. |
Pham D, Tan X, Xu J, et al. stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues. BioRxiv, 2020, DOI: 10.1101/2020.05.31.125658.
|
9. |
Xu H, Fu H, Long Y, et al. Unsupervised spatially embedded deep representation of spatial transcriptomics. Genome Med, 2024, 16(1): 12.
|
10. |
Teng H, Yuan Y, Bar-Joseph Z. Clustering spatial transcriptomics data. Bioinformatics, 2022, 38(4): 997-1004.
|
11. |
Velickovic P, Cucurull G, Casanova A, et al. Graph attention networks. arXiv preprint, 2018, DOI: 10.48550/arXiv.1710.10903.
|
12. |
Traag V A, Waltman L, van Eck N J. From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 2019, 9(1): 5233.
|
13. |
Wolf F A, Angerer P, Theis F J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biology, 2018, 19(1): 15.
|
14. |
Paszke A, Gross S, Massa F, et al. Pytorch: an imperative style, high-performance deep learning library. arXiv preprint, 2019, DOI: 10.48550/arXiv.1912.01703.
|
15. |
Lewis M, Liu Y, Goyal N, et al. Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint, 2019, DOI: 10.48550/arXiv.1910.13461.
|
16. |
Fey M, Lenssen J E. Fast graph representation learning with PyTorch Geometric. arXiv preprint, 2019, DOI: 10.48550/arXiv.1903.02428.
|
17. |
McInnes L, Healy J, Melville J. Umap: uniform manifold approximation and projection for dimension reduction. arXiv preprint, 2018, DOI: 10.48550/arXiv.1802.03426.
|
18. |
Hubert L, Arabie P. Comparing partitions. Journal of Classification, 1985, 2: 193-218.
|
19. |
Xu C, Jin X, Wei S, et al. DeepST: identifying spatial domains in spatial transcriptomics by deep learning. Nucleic Acids Research, 2022, 50(22): e131.
|
20. |
Dong K, Zhang S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nature Communications, 2022, 13(1): 1739.
|
21. |
Maynard K R, Collado-Torres L, Weber L M, et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nature Neuroscience, 2021, 24(3): 425-436.
|
22. |
Ma S, Skarica M, Li Q, et al. Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. Science, 2022, 377(6614): eabo7257.
|