单细胞论文记录(part11)--ClusterMap for multi-scale clustering analysis of spatial gene expression

2024-04-20 18:30:20

学习笔记，仅供参考，有错必纠

阅读状态：略读
Journal：Nature Communications
Year：2021
Authors：He Yichun，Tang Xin

ClusterMap for multi-scale clustering analysis of spatial gene expression

abstract

在空间背景下量化RNA是了解复杂组织中基因表达和调节的关键. In situ transcriptomic methods在完整的组织中产生空间分辨率的RNA谱. 然而，目前还缺乏一个统一的计算框架来综合分析in situ transcriptomic data. 在这里，我们引入了一个无监督框架，称为ClusterMap，它结合了RNA的physical location 和 gene identity of RNAs，将任务制定为一个point模式分析问题，并通过密度峰聚类（DPC）识别有生物学意义的结构. 具体来说，ClusterMap在二维和三维空间中将RNA精确地聚类到亚细胞结构、细胞体和组织区域，并在不同的组织类型中表现稳定，包括小鼠大脑、胎盘、肠道和人类心脏器官. 我们证明ClusterMap广泛适用于各种in situ transcriptomic measurements，从具有高维转录组图谱的图像中发现gene expression patterns, cell niche, 和 tissue organization principles.

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