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Updated: Jun 25, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

计算建模用于从空间解析的转录组学解读组织微环境异质性.

Chuanchao Zhang¹, Lequn Wang^2,3, Qianqian Shi^4,5

¹Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, Hangzhou 310024; University of Chinese Academy of Sciences, China.

Computational and structural biotechnology journal

|May 27, 2024

概括

此摘要是机器生成的。

空间转录学通过分析基因表达与位置来揭示组织结构和疾病进展. 本综述对分析这些复杂的omics数据的计算方法进行了分类,帮助研究人员选择合适的工具.

关键词:

空间解卷的空间解卷.空间域检测空间域检测空间转录组空间转录组

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Last Updated: Jun 25, 2025

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科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

空间转录学 (ST) 测量基因表达,同时保留空间信息,这对于研究组织结构和病理学至关重要.
由ST生成的大量数据需要先进的计算方法来理解组织微环境异质性.

研究的目的:

分类和审查空间转录学数据分析的计算方法.
引导研究人员根据他们的具体研究需求选择合适的计算工具.

主要方法:

将计算方法分为基于机器学习,基于概率模型和基于深度学习的方法.
讨论代表算法,它们的优点和缺点.
用于评估ST计算方法的数据集和评估指标的描述.

主要成果:

提供了现有的空间转录学计算方法的结构化概述.
突出了不同算法方法的优缺点.
便于明智选择用于空间域检测和解卷的计算工具.

结论:

当前的计算方法为分析空间转录组学数据提供了多种策略.
基于技术进步和算法限制,建议未来的发展方向.
有效的计算分析是释放空间转录学在生物研究中的全部潜力的关键.