Bioinformatics Research Group (BioRG), Florida International University, Miami, Florida, USA.
Florida International University, Miami, Florida, USA.
mSystems. 2024 Oct 22;9(10):e0130323. doi: 10.1128/msystems.01303-23. Epub 2024 Sep 6.
A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.
We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.
在微生物组数据分析中，一个关键挑战是整合多组学数据集，并发现微生物类群、它们表达的基因以及它们消耗和/或产生的代谢物之间的相互作用。为了提高从纵向多组学微生物组数据集中推断具有生物学意义的多组学相互作用的水平，我们试图解决从纵向多组学微生物组数据集中进行因果推断的一些最基本问题。我们开发了 METALICA，这是一套可以推断微生物组实体之间相互作用的工具和技术。METALICA 引入了新颖的展开和去混淆技术，用于揭示被认为是某些可能使用标准因果推断工具推断的关系的混淆因素的多组学实体。结果支持了关于生物模型和过程的预测，即微生物类群如何在微生物组中相互作用。展开过程有助于识别可能的中介物（基因和/或代谢物）来解释微生物之间的相互作用；去混淆过程确定可能导致推断出虚假关系的共同原因。METALICA 被应用于现有因果发现推断的网络，并且网络推断算法被应用于来自 IBD 微生物组的纵向研究的多组学数据集。使用现有的文献和数据库手动验证了最重要的展开和去混淆。
我们开发了一套能够推断微生物组实体之间相互作用的工具和技术。METALICA 引入了称为展开和去混淆的新技术，用于揭示被认为是某些可能使用标准因果推断工具推断的关系的混淆因素的多组学实体。为了评估我们的方法，我们在来自 iHMP 纵向研究的炎症性肠病 (IBD) 数据集上进行了测试，我们按照我们之前的工作对其进行了预处理。从这个数据集，我们生成了各种子集，包含不同组合的宏基因组学、代谢组学和宏转录组学数据集。使用这些多组学数据集，我们展示了展开过程如何帮助识别可能的中介物（基因和/或代谢物）来解释微生物之间的相互作用。此外，去混淆过程确定了可能导致推断出虚假关系的潜在共同原因。使用现有的文献和数据库手动验证了最重要的展开和去混淆。
