MoRE-GNN: Multi-omics Data Integration with a Heterogeneous Graph Autoencoder
This work addresses a domain-specific problem in computational biology for researchers integrating multi-omics data, offering an adaptive and scalable framework, though it appears incremental as it builds on existing graph neural network techniques.
The paper tackled the challenge of integrating high-dimensional multi-omics single-cell data by introducing MoRE-GNN, a heterogeneous graph autoencoder that dynamically constructs relational graphs, and it outperformed existing methods on six datasets, particularly in settings with strong inter-modality correlations.
The integration of multi-omics single-cell data remains challenging due to high-dimensionality and complex inter-modality relationships. To address this, we introduce MoRE-GNN (Multi-omics Relational Edge Graph Neural Network), a heterogeneous graph autoencoder that combines graph convolution and attention mechanisms to dynamically construct relational graphs directly from data. Evaluations on six publicly available datasets demonstrate that MoRE-GNN captures biologically meaningful relationships and outperforms existing methods, particularly in settings with strong inter-modality correlations. Furthermore, the learned representations allow for accurate downstream cross-modal predictions. While performance may vary with dataset complexity, MoRE-GNN offers an adaptive, scalable and interpretable framework for advancing multi-omics integration.