Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics , Karolinska Institute , SE-171 77 Stockholm , Sweden.
Centre for Integrative Metabolomics and Computational Biology, School of Science , Edith Cowan University , Perth 6027 , Australia.
Anal Chem. 2018 Nov 20;90(22):13400-13408. doi: 10.1021/acs.analchem.8b03205. Epub 2018 Nov 2.
Integration of multiomics data remains a key challenge in fulfilling the potential of comprehensive systems biology. Multiple-block orthogonal projections to latent structures (OnPLS) is a projection method that simultaneously models multiple data matrices, reducing feature space without relying on a priori biological knowledge. In order to improve the interpretability of OnPLS models, the associated multi-block variable influence on orthogonal projections (MB-VIOP) method is used to identify variables with the highest contribution to the model. This study combined OnPLS and MB-VIOP with interactive visualization methods to interrogate an exemplar multiomics study, using a subset of 22 individuals from an asthma cohort. Joint data structure in six data blocks was assessed: transcriptomics; metabolomics; targeted assays for sphingolipids, oxylipins, and fatty acids; and a clinical block including lung function, immune cell differentials, and cytokines. The model identified seven components, two of which had contributions from all blocks (globally joint structure) and five that had contributions from two to five blocks (locally joint structure). Components 1 and 2 were the most informative, identifying differences between healthy controls and asthmatics and a disease-sex interaction, respectively. The interactions between features selected by MB-VIOP were visualized using chord plots, yielding putative novel insights into asthma disease pathogenesis, the effects of asthma treatment, and biological roles of uncharacterized genes. For example, the gene ATP6 V1G1, which has been implicated in osteoporosis, correlated with metabolites that are dysregulated by inhaled corticoid steroids (ICS), providing insight into the mechanisms underlying bone density loss in asthma patients taking ICS. These results show the potential for OnPLS, combined with MB-VIOP variable selection and interaction visualization techniques, to generate hypotheses from multiomics studies and inform biology.
多组学数据的整合仍然是实现全面系统生物学潜力的关键挑战。多块正交投影到潜在结构（OnPLS）是一种投影方法，它可以同时对多个数据矩阵进行建模，在不依赖先验生物学知识的情况下减少特征空间。为了提高 OnPLS 模型的可解释性，使用相关的多块变量对正交投影的影响（MB-VIOP）方法来识别对模型贡献最大的变量。本研究结合 OnPLS 和 MB-VIOP 与交互式可视化方法，使用哮喘队列中 22 名个体的子集，对一个多组学研究范例进行了分析。评估了六个数据块的联合数据结构：转录组学；代谢组学；鞘脂、氧化脂和脂肪酸的靶向测定；以及包括肺功能、免疫细胞差异和细胞因子的临床块。该模型确定了七个成分，其中两个成分来自所有块（全局联合结构），五个成分来自两个到五个块（局部联合结构）。成分 1 和 2 是最具信息量的，分别识别了健康对照者和哮喘患者之间的差异以及疾病性别相互作用。使用和弦图可视化 MB-VIOP 选择的特征之间的相互作用，为哮喘发病机制、哮喘治疗的影响以及未表征基因的生物学作用提供了新的见解。例如，与吸入皮质类固醇（ICS）调节的代谢物相关的基因 ATP6 V1G1 已被牵连到骨质疏松症中，这为了解接受 ICS 的哮喘患者骨密度下降的机制提供了线索。这些结果表明，OnPLS 结合 MB-VIOP 变量选择和相互作用可视化技术，有可能从多组学研究中生成假设，并为生物学提供信息。
