Single-cell colocalization analysis using a deep generative model.

Yasuhiro Kojima, Shinji Mii, Shuto Hayashi, Haruka Hirose, Masato Ishikawa, Masashi Akiyama, Atsushi Enomoto, Teppei Shimamura
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Abstract

Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, and cellular responses to external stimuli and their biological functions in diseases and tissues. However, existing computational methodologies identified the colocalization patterns between predefined cell populations, which can obscure the molecular signatures arising from intercellular communication. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers intercellular colocalization networks with single-cell resolution by the integration of single-cell and spatial transcriptomes. Along with colocalized population detection accuracy that is superior to existing methods in simulated dataset, DeepCOLOR identified plausible cell-cell interaction candidates between colocalized single cells and segregated cell populations defined by the colocalization relationships in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2. DeepCOLOR is applicable to studying cell-cell interactions behind various spatial niches. A record of this paper's transparent peer review process is included in the supplemental information.

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利用深度生成模型进行单细胞共定位分析
分析具有异质分子表型的单细胞的共定位对于了解细胞-细胞相互作用、细胞对外界刺激的反应及其在疾病和组织中的生物功能至关重要。然而,现有的计算方法识别的是预定义细胞群之间的共聚焦模式,这可能会掩盖细胞间通信产生的分子特征。在这里,我们介绍了 DeepCOLOR,这是一种基于深度生成模型的计算框架,通过整合单细胞和空间转录组,以单细胞分辨率恢复细胞间的共定位网络。在模拟数据集中,DeepCOLOR 的共定位群体检测准确率优于现有方法,同时还在小鼠脑组织、人类鳞状细胞癌样本和感染 SARS-CoV-2 的人类肺组织中发现了共定位单细胞与由共定位关系定义的分离细胞群体之间似是而非的细胞-细胞相互作用候选者。DeepCOLOR 适用于研究各种空间龛位背后的细胞-细胞相互作用。本论文的同行评审过程透明,记录见补充信息。
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