MoReL: Multi-omics Relational Learning
MoReL: Multi-omics Relational Learning
复制标题
DOI：
10.48550/arxiv.2203.08149
复制
发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Arman Hasanzadeh;Ehsan Hajiramezanali;N. Duffield;Xiaoning Qian
中科院分区：
文献类型：
--
作者：
Arman Hasanzadeh;Ehsan Hajiramezanali;N. Duffield;Xiaoning Qian
多组学数据分析具有发现隐藏的分子相互作用的潜力，在研究生命和疾病系统时揭示感兴趣的细胞过程的潜在调控和/或信号转导途径。当处理真实世界的多组学数据时的关键挑战之一是，它们可能表现出异构结构和数据质量，因为对于每种类型的组学数据，通常现有的数据可能在不同的条件下从不同的受试者收集。我们提出了一种新的深度贝叶斯生成模型，可以有效地推断出编码这种异质视图之间分子相互作用的多部图，在相应视图的潜在表示之间使用融合的Gromov-Wasserstein（FGW）正则化进行综合分析。通过深度贝叶斯生成模型中的这种最佳传输正则化，它不仅允许将视图特定的边信息与不同视图中的图形结构化或非结构化数据结合起来，而且还通过基于分布的正则化增加了模型的灵活性。与现有的基于点的图嵌入方法相比，这允许异构潜变量分布的有效对齐以获得可靠的交互预测。我们在几个真实世界数据集上的实验表明，与现有基线相比，MoReL在推断有意义的相互作用方面的性能有所增强。
Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems. One of critical challenges when dealing with real-world multi-omics data is that they may manifest heterogeneous structures and data quality as often existing data may be collected from different subjects under different conditions for each type of omics data. We propose a novel deep Bayesian generative model to efficiently infer a multi-partite graph encoding molecular interactions across such heterogeneous views, using a fused Gromov-Wasserstein (FGW) regularization between latent representations of corresponding views for integrative analysis. With such an optimal transport regularization in the deep Bayesian generative model, it not only allows incorporating view-specific side information, either with graph-structured or unstructured data in different views, but also increases the model flexibility with the distribution-based regularization. This allows efficient alignment of heterogeneous latent variable distributions to derive reliable interaction predictions compared to the existing point-based graph embedding methods. Our experiments on several real-world datasets demonstrate enhanced performance of MoReL in inferring meaningful interactions compared to existing baselines.