Motivation: It is known that the structure of transcription and protein interaction networks is informative of its biological function at multiple scales. However, thus far it has not been possible to systematically connect network topology to gene expression in a quantitative way. Results: We investigated whether there is a relationship between interaction networks and gene expression values by using a graph convolutional auto-encoder and two end-to-end learning approaches for three interaction networks and hundreds of experimental conditions in the URLanismE.\ coli . Graph neural networks use a message passing framework to learn an embedding of a graph in a continuous space , either using network topology alone, or including additional node features. We found that graph embeddings trained on transcription and PPI networks can explain more than 50 and 40 percent, respectively, of the variance in gene expression data, thus confirming the relationship between network structure and gene expression value. Additionally, for the task of predicting gene expression values using GNNs, with and without additional expression training data, we found that the message passing scheme of GNNs is able to obtain the lowest mean squared error between the tested models both in prediction of unseen test values, and in an auto-encoder scheme for reconstruction of the feature matrix of expression values .
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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It is known that the structure of transcription and protein interaction networks
Molecular interaction networks summarize complex biological processes as graphs, whose structure
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its
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However, thus far it has not been possible to systematically connect network topology to gene expression in a quantitative way. Results: We investigated whether there is a relationship between interaction networks and gene expression values by using a graph convolutional auto-encoder and two end-to-end learning approaches for three interaction networks and hundreds of experimental conditions in the URLanism
R
E.\ coli
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
R
use a message passing framework to learn an embedding of a graph in a continuous space , either using network topology alone, or including additional
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
R
We found that graph embeddings trained on transcription and PPI networks can explain more than 50 and 40 percent, respectively, of the variance
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
R
thus confirming the relationship between network structure and gene expression value. Additionally, for the task of predicting gene expression values using GNNs, with and without additional expression training data, we found that the message passing scheme of GNNs is able to obtain the lowest mean squared error between the tested models both in prediction of unseen test values, and in an
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
R
scheme for reconstruction of the feature matrix of expression values
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