Abstract
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder with significant impacts on patients and their families. Therefore, accurate and early diagnosis of AD is crucial for improving patient outcomes and developing effective treatments. However, despite advancements in machine learning for AD diagnosis, current methods lack molecular-level insights and completely ignore the heterogeneity in complex human brains, thus potentially masking crucial disease mechanisms. Here, we present ExAD-GNN, an Explainable Graph Neural Network for predicting AD status from single-cell sequencing data. Leveraging K Nearest Neighbours (KNN) graphs derived from the expression profiles of individual cells, ExAD-GNN achieves two primary goals: predicting AD pathology at a cellular level and identifying cell-type-specific marker genes for AD diagnosis through a unique learnable gene importance metric. Extensive benchmarking on large-scale scRNA-seq data with state-of-the-art methods demonstrates ExAD-GNN's noticeably improved AD prediction accuracy and robustness across various cell types and samples. Furthermore, an extensive ablation study and literature search confirm the majority of top AD risk genes highlighted by our method, demonstrating the effectiveness of ExAD-GNN's model interpretation scheme. In summary, we develop ExAD-GNN as a publicly available software for the scientific community to gain molecular insights into AD pathology from scRNA-seq data.