Liu Ruoqian, Wang Yue, Cheng Dan
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85251, United States.
Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, United States.
Bioinformatics. 2024 Nov 28;40(12). doi: 10.1093/bioinformatics/btae667.
Extensive research has uncovered the critical role of the human gut microbiome in various aspects of health, including metabolism, nutrition, physiology, and immune function. Fecal microbiota is often used as a proxy for understanding the gut microbiome, but it represents an aggregate view, overlooking spatial variations across different gastrointestinal (GI) locations. Emerging studies with spatial microbiome data collected from specific GI regions offer a unique opportunity to better understand the spatial composition of the stool microbiome.
We introduce Micro-DeMix, a mixture beta-multinomial model that deconvolutes the fecal microbiome at the compositional level by integrating stool samples with spatial microbiome data. Micro-DeMix facilitates the comparison of microbial compositions across different GI regions within the stool microbiome through a hypothesis-testing framework. We demonstrate the effectiveness and efficiency of Micro-DeMix using multiple simulated datasets and the inflammatory bowel disease data from the NIH Integrative Human Microbiome Project.
The R package is available at https://github.com/liuruoqian/MicroDemix.
广泛的研究揭示了人类肠道微生物群在健康的各个方面所起的关键作用，包括新陈代谢、营养、生理学和免疫功能。粪便微生物群通常被用作了解肠道微生物群的替代指标，但它呈现的是一种总体观点，忽略了不同胃肠道（GI）位置的空间差异。从特定GI区域收集空间微生物组数据的新兴研究为更好地理解粪便微生物组的空间组成提供了独特的机会。
我们引入了Micro-DeMix，这是一种混合β-多项分布模型，通过将粪便样本与空间微生物组数据相结合，在组成水平上对粪便微生物组进行反卷积。Micro-DeMix通过假设检验框架促进了粪便微生物组内不同GI区域微生物组成的比较。我们使用多个模拟数据集和来自美国国立卫生研究院综合人类微生物组计划的炎症性肠病数据证明了Micro-DeMix的有效性和效率。
