PDF(Pubmed)
Abstract：
To address the limitation of overlooking crucial ecological interactions due to relying on single time point samples, we developed a computational approach that analyzes individual samples based on the interspecific microbial relationships. We verify, using both numerical simulations as well as real and shuffled microbial profiles from the human oral cavity, that the method can classify single samples based on their interspecific interactions. By analyzing the gut microbiome of people with autistic spectrum disorder, we found that our interaction-based method can improve the classification of individual subjects based on a single microbial sample. These results demonstrate that the underlying ecological interactions can be practically utilized to facilitate microbiome-based diagnosis and precision medicine.
摘要：
为了解决由于依赖单个时间点样本而忽略关键生态相互作用的局限性，我们开发了一种计算方法，基于种间微生物关系分析单个样品。我们核实,使用数值模拟以及来自人类口腔的真实和混合微生物谱，该方法可以根据单个样本的种间相互作用对它们进行分类。通过分析自闭症谱系障碍患者的肠道微生物组，我们发现我们的基于相互作用的方法可以改善基于单个微生物样本的个体分类。这些结果表明，潜在的生态相互作用可以实际用于促进基于微生物组的诊断和精准医学。
