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贝叶斯TME:一个端到端的方法,用于组织微环境的多尺度空间转录分析

Haoran Zhang¹, Miranda V Hunter², Jacqueline Chou³

¹Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA.

|July 20, 2023

概括

贝叶斯TME是一种新的计算方法,它分析空间转录组学数据,以了解疾病中的细胞变异. 它可以识别空间转录程序,而不需要单细胞RNA测序,从而改善癌症研究.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
免疫学免疫学免疫学

背景情况:

细胞表型的空间变异有助于疾病异质性,影响免疫识别和治疗反应.
空间转录学提供了一种方法来测量这种变化,但目前的方法仅限于特定的任务,如点位解卷.
分析空间转录学数据对于理解复杂的生物系统至关重要.

研究的目的:

介绍BayesTME,一个端到端的贝叶斯方法,用于全面的空间转录学数据分析.
将不同的分析目标统一为一个整体方法的单一生成模型.
为了使细胞表型解卷,并揭示空间基因表达模式没有配对单细胞RNA-seq.

主要方法:

开发了贝叶斯TME,一个统一的贝叶斯生成模型用于空间转录学数据.
应用贝叶斯TME将斑点解成细胞表型.
在细胞表型中确定了空间转录程序.

主要成果:

贝叶斯TME在多个基准上实现了最先进的性能.
在没有单细胞RNA-seq.的情况下,成功地从空间转录组学数据中解构了细胞表型.
在人类和斑马鱼黑色素瘤中确定了生物学相关的空间转录程序,包括双边对称性和免疫细胞重编程.

结论:

贝叶斯TME提供了一个统一而强大的框架来分析空间转录学数据.
该方法促进了在疾病背景下对细胞异质性和空间组织的理解.
贝叶斯TME有助于发现新的空间生物现象,并作为开源软件提供.

关键词:

贝叶斯的方法贝叶斯的方法机器学习是机器学习.空间基因表达方式空间建模空间建模空间转录学空间转录学瘤微环境是一个微环境.

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