A multimodal deep learning model to infer cell-type-specific functional gene networks

• Published in: BMC bioinformatics Vol. 24; no. 1; p. 47
• Main Authors: Afshar, Shiva, Braun, Patricia R, Han, Shizhong, Lin, Ying
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 14.02.2023; BioMed Central; BMC
• Subjects: Alzheimer Disease - genetics; Alzheimer's disease; Artificial neural networks; Autism; Biomarkers; Brain; Brain cells; Cell-type-specific functional gene networks; Comparative analysis; Context; Deep Learning; Gene expression; Gene Regulatory Networks; Genes; Genetic aspects; Global protein interaction networks; Humans; Machine learning; Microglia; Model accuracy; Modularity; Multimodal deep learning; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Neurological research; Performance evaluation; Proteins; Risk; Risk factors; Single-nuclei gene expression; Topology; United States; Multimodal deep learning; Single-nuclei gene expression; Cell-type-specific functional gene networks; Global protein interaction networks
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05146-x

Functional gene networks (FGNs) capture functional relationships among genes that vary across tissues and cell types. Construction of cell-type-specific FGNs enables the understanding of cell-type-specific functional gene relationships and insights into genetic mechanisms of human diseases in disease-relevant cell types. However, most existing FGNs were developed without consideration of specific cell types within tissues. In this study, we created a multimodal deep learning model (MDLCN) to predict cell-type-specific FGNs in the human brain by integrating single-nuclei gene expression data with global protein interaction networks. We systematically evaluated the prediction performance of the MDLCN and showed its superior performance compared to two baseline models (boosting tree and convolutional neural network). Based on the predicted cell-type-specific FGNs, we observed that cell-type marker genes had a higher level of hubness than non-marker genes in their corresponding cell type. Furthermore, we showed that risk genes underlying autism and Alzheimer's disease were more strongly connected in disease-relevant cell types, supporting the cellular context of predicted cell-type-specific FGNs. Our study proposes a powerful deep learning approach (MDLCN) to predict FGNs underlying a diverse set of cell types in human brain. The MDLCN model enhances prediction accuracy of cell-type-specific FGNs compared to single modality convolutional neural network (CNN) and boosting tree models, as shown by higher areas under both receiver operating characteristic (ROC) and precision-recall curves for different levels of independent test datasets. The predicted FGNs also show evidence for the cellular context and distinct topological features (i.e. higher hubness and topological score) of cell-type marker genes. Moreover, we observed stronger modularity among disease-associated risk genes in FGNs of disease-relevant cell types. For example, the strength of connectivity among autism risk genes was stronger in neurons, but risk genes underlying Alzheimer's disease were more connected in microglia.