一种用于预测生物网络未观测节点特征的图特征自动编码器
A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networks
摘要：
动机：分子相互作用网络将复杂的生物学过程概括为图形，其结构在多个尺度上提供生物学功能的信息。同时，组学技术测量个体或实验条件下基因、蛋白质或代谢物的变异或活性。整合生物网络和组学数据的互补观点是生物信息学中的一项重要任务，但现有方法将网络视为离散结构，本质上难以与连续节点特征或活动度量整合。图神经网络将图节点映射到低维向量空间表示中，并且可以被训练为保持局部图结构和节点特征之间的相似性。结果：研究了转录、蛋白质-蛋白质和遗传相互作用网络在大肠杆菌中的表达。利用图神经网络对大肠杆菌和小鼠进行了研究。我们发现，这种表示解释了基因表达数据中的大部分变化，并且使用基因表达数据作为节点特征改进了从嵌入的图的重建。我们进一步提出了一种新的端到端图特征自动编码器，它在特征重建任务上进行了训练，并表明它在预测未观察到的节点特征方面比在学习预测节点特征之前在图重建任务上进行训练的自动编码器表现得更好。当应用于在单细胞RNAseq数据中输入缺失数据的问题时，我们的图形特征自动编码器优于不使用蛋白质相互作用信息的最先进的输入方法，显示了使用图形表示学习整合生物网络和组学数据的好处。
英文摘要：
Motivation: Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features. Results: We studied the representation of transcriptional, protein-protein and genetic interaction networks in E. Coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end graph feature auto-encoder which is trained on the feature reconstruction task, and showed that it performs better at predicting unobserved node features than auto-encoders that are trained on the graph reconstruction task before learning to predict node features. When applied to the problem of imputing missing data in single-cell RNAseq data, our graph feature auto-encoder outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data using graph representation learning.
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