Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data.
Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data.
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DOI：
10.1186/s13059-023-03138-x
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发表时间：
2023-12-18
期刊：
影响因子：
12.3
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中科院分区：
文献类型：
--
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空间组学技术可以帮助识别空间组织的生物过程，但现有的计算方法往往忽略了数据中的结构依赖性。在这里，我们介绍Smoother，一个统一的框架，通过模块化的先验和损失将位置信息集成到非空间模型中。在模拟和真实的数据集中，Smoother可以实现精确的数据插补、单元类型反卷积和降维，效率非常高。在结直肠癌中，平滑引导的去卷积揭示了与肿瘤微环境重建相关的浆细胞和成纤维细胞亚型定位。此外，使用Smoother对空间和单细胞人类前列腺数据进行联合建模，可以显著降低模糊性，实现参考人群的空间映射。在线版本包含补充材料，可通过10.1186/s13059-023-03138-x获得。
Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses. In simulated and real datasets, Smoother enables accurate data imputation, cell-type deconvolution, and dimensionality reduction with remarkable efficiency. In colorectal cancer, Smoother-guided deconvolution reveals plasma cell and fibroblast subtype localizations linked to tumor microenvironment restructuring. Additionally, joint modeling of spatial and single-cell human prostate data with Smoother allows for spatial mapping of reference populations with significantly reduced ambiguity. The online version contains supplementary material available at 10.1186/s13059-023-03138-x.
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影响因子：
64.8
作者：
Cao, Junyue;Spielmann, Malte;Shendure, Jay
通讯作者：
Shendure, Jay
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Andersson A;Bergenstråhle J;Asp M;Bergenstråhle L;Jurek A;Fernández Navarro J;Lundeberg J
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Izar, Benjamin
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64.8
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Deng, Yanxiang;Bartosovic, Marek;Ma, Sai;Zhang, Di;Kukanja, Petra;Xiao, Yang;Su, Graham;Liu, Yang;Qin, Xiaoyu;Rosoklija, Gorazd B.;Dwork, Andrew J.;Mann, J. John;Xu, Mina L.;Halene, Stephanie;Craft, Joseph E.;Leong, Kam W.;Boldrini, Maura;Castelo-Branco, Goncalo;Fan, Rong
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Fan, Rong
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Fritz, Jorg H.