Formulating a method to analyse the differential expression of co-occurrence networks for small-sampled microbiome data
Formulating a method to analyse the differential expression of co-occurrence networks for small-sampled microbiome data
复制标题
制定一种分析小样本微生物组数据共现网络差异表达的方法
DOI：
10.1145/3584371.3612969
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Gadhia N
中科院分区：
文献类型：
--
作者：
Gadhia N
可以使用基于图的机器学习方法来探索遗传数据中的变异的识别和预测。特别是，在细胞水平上的表型效应的理解是一个加速药物基因组学的研究领域。了解药物或疾病对底层生物网络的影响可以促进未来的药物开发和精准医疗的改进。在这篇文章中，提出了一种新的图论方法，从16S微生物组数据中推断共生网络，专门用于处理小样本数据集。小数据集加剧了生物数据所面临的重大挑战，这些数据表现出稀疏性，组合性和相互作用的复杂性等特性。还提出了统计丰富和过滤推断网络的方法。该方法专门用于丰富的小数据集，但它通常可以应用于任何16S数据集，也可以扩展到与其他多个组学数据。所提出的方法进行了测试的数据集上的鸡接种疫苗，并由原生动物寄生虫Eimeria tenella的挑战。在疾病进展的三个不同阶段下的网络功能的表达分析生物学推导，从纯粹的统计方法得出的直观结论。的分布揭示集群的物种相互作用和寄生，正如预期的那样。此外，发现一个特定的子网络在所有实验条件下都能持续存在，代表了“核心微生物组”。
The identification and prediction of variation in genetic data can be explored using graph-based machine learning methods. In particular, the comprehension of phenotypic effects at the cellular level is an accelerating research area in pharmacogenomics. Understanding the effect of drugs or disease on the underlying bionetwork could facilitate future drug development and improvement of precision medicine.In this article, a novel graph theoretic approach is proposed to infer a co-occurrence network from 16S microbiome data, specialised to handle small sampled data sets. Small data sets exacerbate the significant challenges faced by biological data, which exhibit properties such as sparsity, compositionality, and complexity of interactions. Methodologies are also proposed to statistically enrich and filter the inferred networks. The method is specialised for small data sets, which are abundant, but it can be generally applied to any 16S data set, and can also be extended to be integrated with other multi-omics data.The proposed methodology is tested on a data set of chickens vaccinated against and challenged by the protozoan parasiteEimeria tenella.Analysis of the expression of network features under three different stages of disease progression derive biologically intuitive conclusions from purely statistical methods. The distributions reveal clusters of species interacting mutualistically and parasitically, as expected. Moreover, a specific subnetwork is found to persist through all experimental conditions, representative of a 'core microbiome'.
定义合成生态学中的高阶相互作用：物理学和定量遗传学的教训。
影响因子：
9.3
作者：
Sanchez,Alvaro
通讯作者：
Sanchez,Alvaro