Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA.
Nat Methods. 2019 Dec;16(12):1306-1314. doi: 10.1038/s41592-019-0616-3. Epub 2019 Nov 4.
Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.
整合多组学数据集对于微生物组研究至关重要；然而，推断组学数据集之间的相互作用存在多个统计挑战。我们通过使用神经网络（https://github.com/biocore/mmvec）来估计给定特定微生物存在时每个分子存在的条件概率来解决这个问题。我们通过已知的环境（沙漠土壤生物结皮润湿）和临床（囊性纤维化肺）示例展示了我们恢复微生物 - 代谢物关系的能力，并演示了该方法如何发现微生物产生的代谢物与炎症性肠病之间的关系。
