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Add FlashDeconv to Spot Deconvolution section #1

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3 changes: 2 additions & 1 deletion README.md
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Expand Up @@ -41,7 +41,8 @@ To provide a distinction between Deep Learning and Machine Learning/Statistical
| ***2. Spot Deconvolution*** | [RTCD](https://github.com/dmcable/RCTD) | Robust decomposition of cell type mixtures in spatial tran- scriptomics | R | 2021 | D. M. Cable, E. Murray, L. S. Zou, A. Goeva, E. Z. Macosko, F. Chen, and R. A. Irizarry, “Robust decomposition of cell type mixtures in spatial transcriptomics,” Nature Biotechnology (2021), 10.1038/s41587-021-00830- w. | |
| ***2. Spot Deconvolution*** | [SpatialDWLS](https://github.com/RubD/Giotto) | SpatialDWLS: Accurate deconvolution of spatial transcriptomic data | Python (PyTorch) | 2021 | R. Dong and G.-C. Yuan, “Spatialdwls: accurate deconvolution of spatial transcriptomic data,” Genome Biology 22, 145 (2021). | |
| ***2. Spot Deconvolution*** | [Cell2Location](https://github.com/BayraktarLab/cell2location) | Cell2Location maps fine-grained cell types in spatial transcriptomics | Python | 2022 | V. Kleshchevnikov, A. Shmatko, E. Dann, A. Aivazidis, H. W. King, T. Li, R. Elmentaite, A. Lomakin, V. Kedlian, A. Gayoso, M. S. Jain, J. S. Park, L. Ramona, E. Tuck, A. Arutyunyan, R. Vento-Tormo, M. Ger- stung, L. James, O. Stegle, and O. A. Bayraktar, “Cell2location maps fine-grained cell types in spatial transcriptomics,” Nature Biotechnology (2022), 10.1038/s41587-021-01139-4. | |
| ***2. Spot Deconvolution*** | [CARD](https://github.com/YingMa0107/CARD) | Spatially informed cell-type deconvolution for spatial transcriptomic | R | 2022 | Y. Ma and X. Zhou, “Spatially informed cell-type deconvolution for spatial transcriptomics,” Nature Biotechnology 40, 1349–1359 (2022). | |
| ***2. Spot Deconvolution*** | [CARD](https://github.com/YingMa0107/CARD) | Spatially informed cell-type deconvolution for spatial transcriptomic | R | 2022 | Y. Ma and X. Zhou, "Spatially informed cell-type deconvolution for spatial transcriptomics," Nature Biotechnology 40, 1349–1359 (2022). | |
| ***2. Spot Deconvolution*** | [FlashDeconv](https://github.com/cafferychen777/flashdeconv) | High-performance deconvolution using randomized sketching with linear O(N) scaling | Python | 2025 | C. Yang et al., "FlashDeconv: Ultra-fast cell type deconvolution for high-resolution spatial transcriptomics," bioRxiv (2025). https://doi.org/10.64898/2025.12.22.696108 | Designed for Visium HD |
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| ***3. Spatially Variable Genes ID*** | [Trendsceek](https://github.com/edsgard/trendsceek) | Identification of spatial expression trends in single-cell gene expression data | R | 2018 | D. Edsgärd, P. Johnsson, and R. Sandberg, “Identification of spatial expression trends in single-cell gene expression data,” Nature Methods 15, 339–342 (2018). | |
| ***3. Spatially Variable Genes ID*** | [SpatialDE](https://github.com/Teichlab/SpatialDE) | SpatialDE: Identification of spatially variable genes | Python | 2018 | V. Svensson, S. A. Teichmann, and O. Stegle, “SpatialDE: Identification of spatially variable genes,” Nature Methods 15, 343–346 (2018). | |
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