寻找信息丰富的空间关联路径的稳健统计方法。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Leqi Tian, Jiashun Xiao, Tianwei Yu
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引用次数: 0

摘要

空间转录组学通过将基因表达映射到空间位置来深入了解细胞功能定位和交流。传统方法侧重于选择空间可变基因,往往忽视了生物通路的复杂性和基因之间的相互作用。在这里,我们引入了一个新颖的框架,通过以创新的方式调整布朗距离协方差检验来探索生物功能在空间上的异质性,从而将重点转向直接识别与空间变异性相关的功能通路。与大多数其他方法不同的是,这种统计检验方法不受基因选择和参数选择的影响,允许非线性和复杂的依赖关系。它能让人们更深入地了解细胞如何通过生物通路协调它们在不同空间领域的活动。通过分析真实的人类和小鼠数据集,该方法发现了与空间变化相关的重要通路,以及内部和边缘癌症区域的不同通路模式。这一创新框架为分析空间转录组数据提供了一个新视角,有助于我们了解组织结构和疾病病理。该框架的实现可在 https://github.com/tianlq-prog/STpathway 上公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A robust statistical approach for finding informative spatially associated pathways.

Spatial transcriptomics offers deep insights into cellular functional localization and communication by mapping gene expression to spatial locations. Traditional approaches that focus on selecting spatially variable genes often overlook the complexity of biological pathways and the interactions among genes. Here, we introduce a novel framework that shifts the focus towards directly identifying functional pathways associated with spatial variability by adapting the Brownian distance covariance test in an innovative manner to explore the heterogeneity of biological functions over space. Unlike most other methods, this statistical testing approach is free of gene selection and parameter selection and allows nonlinear and complex dependencies. It allows for a deeper understanding of how cells coordinate their activities across different spatial domains through biological pathways. By analyzing real human and mouse datasets, the method found significant pathways that were associated with spatial variation, as well as different pathway patterns among inner- and edge-cancer regions. This innovative framework offers a new perspective on analyzing spatial transcriptomic data, contributing to our understanding of tissue architecture and disease pathology. The implementation is publicly available at https://github.com/tianlq-prog/STpathway.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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