MuSe-GNN: Learning Unified Gene Representation From Multimodal
  Biological Graph Data
Tianyu Liu Yuge Wang Rex Ying Hongyu Zhao
Discovering genes with similar functions across diverse biomedical contexts
poses a significant challenge in gene representation learning due to data
heterogeneity. In this study, we resolve this problem by introducing a novel
model called Multimodal Similarity Learning Graph Neural Network, which
combines Multimodal Machine Learning and Deep Graph Neural Networks to learn
gene representations from single-cell sequencing and spatial transcriptomic
data. Leveraging 82 training datasets from 10 tissues, three sequencing
techniques, and three species, we create informative graph structures for model
training and gene representations generation, while incorporating
regularization with weighted similarity learning and contrastive learning to
learn cross-data gene-gene relationships. This novel design ensures that we can
offer gene representations containing functional similarity across different
contexts in a joint space. Comprehensive benchmarking analysis shows our
model's capacity to effectively capture gene function similarity across
multiple modalities, outperforming state-of-the-art methods in gene
representation learning by up to 97.5%. Moreover, we employ bioinformatics
tools in conjunction with gene representations to uncover pathway enrichment,
regulation causal networks, and functions of disease-associated or
dosage-sensitive genes. Therefore, our model efficiently produces unified gene
representations for the analysis of gene functions, tissue functions, diseases,
and species evolution.