Annotation-free phenotype prediction using knowledge-augmented clustering from single-cell RNA sequencing data.

Single-cell RNA sequencing has emerged as a transformative tool, enabling precise phenotype prediction and the detailed identification of disease-associated cell subpopulations. However, many existing computational approaches still rely on predefined cell-type annotations during model training. This dependence makes their predictive performance highly sensitive to subjective annotation quality, labeling inconsistencies, and dataset-specific biases, ultimately hindering their generalizability across diverse patient cohorts. To address these challenges, we propose scCap, an annotation-free framework that leverages knowledge-augmented clustering for robust phenotype prediction. Specifically, the framework first constructs initial clusters from raw gene expression profiles and subsequently refines them within the embedding space of a pretrained single-cell foundation model, allowing the clusters to better reflect broader biological organization while preserving fine-grained cellular heterogeneity. The resulting knowledge-augmented clusters are then integrated into a hierarchical multiple instance learning framework with dual-level attention, enabling interpretable predictions at both the cell and cluster levels. Evaluated across three public scRNA-seq datasets, scCap consistently outperforms baseline models in predictive accuracy. Furthermore, scCap identifies disease-associated subpopulations previously reported in the literature without relying on predefined cell-type annotations. These results demonstrate that scCap provides a robust and interpretable framework for annotation-free phenotype prediction.