Identifying key cell types and genes in Alzheimer’s Disease (AD) is crucial for understanding its pathogenesis and discovering therapeutic targets. Single cell RNA sequencing technology (scRNAseq) has provided unprecedented opportunities to study the molecular mechanisms that underlie AD at the cellular level. scAGG is a sample-level classification model which uses a sample-level pooling mechanism to aggregate single-cell embeddings. The method predicts the disease status of entire samples from the gene expression profiles of their cells, which are not necessarily all affected by the disease.
For details and the software, please visit the scAGG GitHub and read the publication by Verlaan et al. (2025).
