scSurvival: Single-cell survival analysis of clinical cancer cohort data at cellular resolution

Survival analysis is fundamental to cancer research. Advances in technology have enabled an increasing number of cohort-level cancer studies to incorporate single-cell sequencing while collecting clinical survival data. However, no effective strategy currently exists for directly modeling survival outcomes from single-cell data. To address this gap, we present scSurvival, an attention-based multiple-instance Cox regression framework that models each patient as a bag of cells to predict survival outcomes at both the patient and single-cell levels. To handle high dimensionality, sparsity, and batch effects, scSurvival integrates a variational autoencoder-based feature extraction module with generative modeling to enhance feature robustness and cross-batch generalizability. Comprehensive simulations demonstrate scSurvival's superior performance and scalability. In melanoma and liver cancer scRNA-seq cohorts, scSurvival accurately predicts patient outcomes and identifies the cell subpopulations most critical to survival. Overall, scSurvival enables robust prediction of patient survival while uncovering survival-associated cell subpopulations, advancing single-cell survival analysis in cancer research.