scSurvival: Single-Cell Survival Analysis of Clinical Cancer Cohort Data at Cellular Resolution.

UNLABELLED: 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 alongside 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 tumor sample as an ensemble 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 single-cell RNA sequencing (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.

SIGNIFICANCE: Survival analysis is central to clinical oncology, yet no effective tools currently model survival outcomes directly from single-cell data. scSurvival bridges this gap by predicting patient outcomes and identifying key subpopulations from scRNA-seq with survival information, enabling scalable analyses and promoting broader adoption of cohort-level single-cell profiling in cancer research.