STAD-CoAtt: Integration of Evolving Gene Graphs in the Assessment of Neuropathological Stages Using Spatiotemporal Representations of Brain Transcriptomics Data.

For neurological disorders, single-nucleus RNA sequencing(snRNA-seq) data from human brain samples have revealed valuable insights about regulatory mechanisms that are associated with disease progression. During data mining of RNA-seq data that are associated with Alzheimer's disease(AD) and dementia, conventional deep learning methods generally focus on changes in gene transcript levels, while ignoring graph features of dementia-specific gene networks to a certain degree. It is noted that graph features underlying transcriptomics data have the potential to enhance model performance by analyzing structural information of AD-specific regulatory networks namely AD-GRN. To sufficiently exploit graph features, spatiotemporal graph learning technique has been employed to recognize meaningful patterns that govern AD progression. Using brain snRNA-seq data, this study has developed an ST-GCN architecture, which has embedded a co-attention network and a nonlinear manifold alignment(NMA) fusion block, to explore abnormal regulatory mechanisms about neurological disorders. The co-attention network aims to obtain compact graph representations by compressing evolving AD-GRNs. The proposed STAD-CoAtt method integrates temporal and graph features, thus constructing joint latent representations of snRNA-seq data. Experiments about two benchmark RNA-seq datasets from ROSMAP and GSE platforms have demonstrated the effectiveness and superiority of the STAD-CoAtt method in assessing neuropathology stages and cognitive dysfunction. By investigating cross-view interactions, the proposed STAD-CoAtt method has obtained superior performance over established SOTA approaches in AD classification tasks.