计算机科学
计算生物学
人工智能
模式识别(心理学)
转录组
噪音(视频)
数据挖掘
计算机视觉
基因表达
基因
生物
图像(数学)
遗传学
作者
Yunguan Wang,Bing Song,Shidan Wang,Mingyi Chen,Yang Xie,Guanghua Xiao,Li Wang,Tao Wang
出处
期刊:Nature Methods
[Springer Nature]
日期:2022-08-01
卷期号:19 (8): 950-958
被引量:28
标识
DOI:10.1038/s41592-022-01560-w
摘要
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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