Sprod for de-noising spatially resolved transcriptomics data based on position and image information.
Sprod for de-noising spatially resolved transcriptomics data based on position and image information.
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DOI：
10.1038/s41592-022-01560-w
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发表时间：
2022-08
期刊：
影响因子：
48
通讯作者：
Wang, Tao
中科院分区：
文献类型：
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作者：
Wang, Yunguan;Song, Bing;Wang, Shidan;Chen, Mingyi;Xie, Yang;Xiao, Guanghua;Wang, Li;Wang, Tao
空间分辨转录组学（SRT）提供接近或甚至上级于单细胞分辨率的基因表达，同时保留测序的物理位置并且通常还提供匹配的病理学图像。然而，由于每个测序单元中的浅覆盖和保留测序位置所需的额外实验步骤，SRT表达数据遭受高噪声水平。幸运的是，这种噪声可以通过利用来自测序的物理位置的信息以及反映在相应病理图像中的组织组织来去除。在这项工作中，我们开发了Sprod，基于匹配位置和成像数据的潜图学习，以估算准确的SRT基因表达。我们全面验证了Sprod，并证明了其优于以前的方法，可以消除单细胞RNA测序数据中的缺失。我们发现，Sprod插补后，差异表达分析，途径富集和细胞间相互作用的推断更准确。总的来说，我们设想Sprod的去噪将成为为生物医学发现提供SRT技术的关键第一步。
Spatially resolved transcriptomics (SRT) provide gene expression close to, or even superior to, single-cell resolution while retaining the physical locations of sequencing and often also providing matched pathology images. However, SRT expression data suffer from high noise levels, due to the shallow coverage in each sequencing unit and the extra experimental steps required to preserve the locations of sequencing. Fortunately, such noise can be removed by leveraging information from the physical locations of sequencing, and the tissue organization reflected in corresponding pathology images. In this work, we developed Sprod, based on latent graph learning of matched location and imaging data, to impute accurate SRT gene expression. We validated Sprod comprehensively and demonstrated its advantages over previous methods for removing drop-outs in single-cell RNA-sequencing data. We showed that, after imputation by Sprod, differential expression analyses, pathway enrichment and cell-to-cell interaction inferences are more accurate. Overall, we envision de-noising by Sprod to become a key first step towards empowering SRT technologies for biomedical discoveries.
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DOI：
10.1016/j.patter.2021.100211
发表时间：
2021-03-12
期刊：
Patterns (New York, N.Y.)
影响因子：
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作者：
Zhang R;Atwal GS;Lim WK
通讯作者：
Lim WK
影响因子：
3.3
作者：
Agbay, Rose Lou Marie C.;Medeiros, L. Jeffrey;Loghavi, Sanam
通讯作者：
Loghavi, Sanam
影响因子：
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作者：
Andrews, Tallulah S;Hemberg, Martin
通讯作者：
Hemberg, Martin
影响因子：
4.7
作者：
Wang, Hao;Wu, Xiaosheng;Chen, Yiming
通讯作者：
Chen, Yiming
影响因子：
2.4
作者：
通讯作者：
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