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Updated: Jun 13, 2025

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波帕里:在空间转录组学中模拟多样本变异

Shahul Alam¹, Tianming Zhou¹, Ellie Haber²

¹Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

bioRxiv : the preprint server for biology

|June 4, 2025

概括

波帕里是一个新的计算工具,可以分析来自多个样本的空间转录组学数据. 它揭示了基因表达模式如何在各种组织和疾病中发生变化,为细胞相互作用和疾病提供了洞察力.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
空间转录组学空间转录组学

背景情况:

在生物样本中整合空间解析转录组学 (SRT) 对于理解in situ组织架构和细胞与细胞相互作用至关重要.
现有的计算工具对于多样本SRT分析是有限的,这阻碍了对特定条件空间变化的研究.

研究的目的:

介绍Popari,一种新的概率图形模型,用于基于多样本SRT数据的因子分解.
在多个样本中捕获和分析空间组织和基因表达程序中的特定条件变化.

主要方法:

波帕里共同学习空间转基因 (基因表达程序) 和它们在样本中的空间亲和关系.
关键的创新包括空间一致规范化的差异优先级和多分辨率分析的空间下采样.
该模型通过模拟验证并应用于现实数据集 (老鼠大脑,胸腺,卵巢癌).

主要成果:

波帕里在多样本和多分辨率空间指标方面表现优于现有方法.
该工具成功地在多种生物样本中识别了空间转基因动态,空间一致性和细胞身份.
具体的应用揭示了老鼠大脑中与AD相关的转基因,胸腺中V(D) J重组和T细胞增殖,以及卵巢癌中的恶性免疫相互作用.

结论:

波帕里提供了一个通用和可解释的框架,用于分析多样本空间解析的转录学数据的变化.
该方法增强了对复杂生物系统中的空间组织和细胞-细胞相互作用的理解.

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