Pappalardo Vincent Y, Azarang Leyla, Zaura Egija, Brandt Bernd W, de Menezes Renée X
Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam, University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Biostatistics Centre, Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
BMC Bioinformatics. 2024 Feb 5;25(1):58. doi: 10.1186/s12859-023-05575-8.
Data from microbiomes from multiple niches is often collected, but methods to analyse these often ignore associations between niches. One interesting case is that of the oral microbiome. Its composition is receiving increasing attention due to reports on its associations with general health. While the oral cavity includes different niches, multi-niche microbiome data analysis is conducted using a single niche at a time and, therefore, ignores other niches that could act as confounding variables. Understanding the interaction between niches would assist interpretation of the results, and help improve our understanding of multi-niche microbiomes.
In this study, we used a machine learning technique called latent Dirichlet allocation (LDA) on two microbiome datasets consisting of several niches. LDA was used on both individual niches and all niches simultaneously. On individual niches, LDA was used to decompose each niche into bacterial sub-communities unveiling their taxonomic structure. These sub-communities were then used to assess the relationship between microbial niches using the global test. On all niches simultaneously, LDA allowed us to extract meaningful microbial patterns. Sets of co-occurring operational taxonomic units (OTUs) comprising those patterns were then used to predict the original location of each sample.
Our approach showed that the per-niche sub-communities displayed a strong association between supragingival plaque and saliva, as well as between the anterior and posterior tongue. In addition, the LDA-derived microbial signatures were able to predict the original sample niche illustrating the meaningfulness of our sub-communities. For the multi-niche oral microbiome dataset we had an overall accuracy of 76%, and per-niche sensitivity of up to 83%. Finally, for a second multi-niche microbiome dataset from the entire body, microbial niches from the oral cavity displayed stronger associations to each other than with those from other parts of the body, such as niches within the vagina and the skin.
Our LDA-based approach produces sets of co-occurring taxa that can describe niche composition. LDA-derived microbial signatures can also be instrumental in summarizing microbiome data, for both descriptions as well as prediction.
通常会收集来自多个生态位的微生物组数据，但分析这些数据的方法往往忽略了不同生态位之间的关联。一个有趣的例子是口腔微生物组。由于有报道称其与总体健康状况有关联，其组成受到越来越多的关注。虽然口腔包含不同的生态位，但多生态位微生物组数据分析是一次使用一个生态位进行的，因此忽略了可能作为混杂变量的其他生态位。了解不同生态位之间的相互作用将有助于解释结果，并有助于增进我们对多生态位微生物组的理解。
在本研究中，我们对由几个生态位组成的两个微生物组数据集使用了一种称为潜在狄利克雷分配（LDA）的机器学习技术。LDA既用于各个生态位，也同时用于所有生态位。在各个生态位上，LDA用于将每个生态位分解为细菌亚群落，揭示其分类结构。然后使用全局检验，利用这些亚群落来评估微生物生态位之间的关系。在同时处理所有生态位时，LDA使我们能够提取有意义的微生物模式。然后，包含这些模式的共现操作分类单元（OTU）集被用于预测每个样本的原始位置。
我们的方法表明，每个生态位的亚群落显示出龈上菌斑与唾液之间以及舌前部和舌后部之间存在很强的关联。此外，LDA衍生的微生物特征能够预测原始样本生态位，说明了我们亚群落的意义。对于多生态位口腔微生物组数据集，我们的总体准确率为76%，每个生态位的灵敏度高达83%。最后，对于来自全身的第二个多生态位微生物组数据集，口腔的微生物生态位之间的相互关联比与身体其他部位（如阴道和皮肤内的生态位）的关联更强。
我们基于LDA的方法产生了可描述生态位组成的共现分类单元集。LDA衍生的微生物特征在总结微生物组数据方面也很有用，无论是用于描述还是预测。
