CORTADO: Hill Climbing Optimization for Cell-Type Specific Marker Gene Discovery and Clustering Accuracy Improvement

The advent of single-cell RNA sequencing (scRNA-seq) has enhanced our ability to study cellular heterogeneity. Accurately identifying distinct subpopulations and their defining markers is critical for understanding tissue diversity. We introduce CORTADO, a hill-climbing optimization framework for marker discovery and clustering refinement. CORTADO maximizes differential expression, minimizes redundancy via cosine similarity, and enforces sparsity for interpretability. By using CORTADO-selected markers to inform the cell-type identification process, an iterative refinement approach markedly increases the Adjusted Rand Index (ARI), a metric that quantifies how well the clustering assignments align with gold-standard cell-type annotations. Benchmarking across brain, immune, spatial, and cancer datasets confirms that CORTADO delivers biologically relevant markers and consistently outperforms state-of-the-art methods in both marker discovery and clustering accuracy.