A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics
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DOI: 10.1038/s41467-023-37168-7
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- Patrick Danaher & Youngmi Kim & Brenn Nelson & Maddy Griswold & Zhi Yang & Erin Piazza & Joseph M. Beechem, 2022. "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
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- Jingyang Qian & Hudong Bao & Xin Shao & Yin Fang & Jie Liao & Zhuo Chen & Chengyu Li & Wenbo Guo & Yining Hu & Anyao Li & Yue Yao & Xiaohui Fan & Yiyu Cheng, 2024. "Simulating multiple variability in spatially resolved transcriptomics with scCube," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
- Tianci Song & Charles Broadbent & Rui Kuang, 2023. "GNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
- Haoyang Li & Yingxin Lin & Wenjia He & Wenkai Han & Xiaopeng Xu & Chencheng Xu & Elva Gao & Hongyu Zhao & Xin Gao, 2024. "SANTO: a coarse-to-fine alignment and stitching method for spatial omics," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Brian S. White & Aurélien Reyniès & Aaron M. Newman & Joshua J. Waterfall & Andrew Lamb & Florent Petitprez & Yating Lin & Rongshan Yu & Martin E. Guerrero-Gimenez & Sergii Domanskyi & Gianni Monaco &, 2024. "Community assessment of methods to deconvolve cellular composition from bulk gene expression," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
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