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CORTADO: hill climbing optimization for cell-type specific marker gene discovery and clustering accuracy improvement
Musaddiq K Lodi1, Leiliani Clark2, Satyaki Roy3
1Virginia Commonwealth University Integrative Life Sciences, Richmond, VA, 23220, United States.
Bioinformatics Advances
|June 19, 2026
Summary
CORTADO, a new computational framework, improves cell subpopulation identification using single-cell RNA sequencing data. It enhances marker discovery and refines cell clustering for better biological insights.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Single-cell RNA sequencing (scRNA-seq) enables detailed study of cellular heterogeneity.
- Accurate identification of cell subpopulations and their markers is crucial for understanding tissue diversity.
Purpose of the Study:
- Introduce CORTADO, a novel hill-climbing optimization framework.
- Enhance marker discovery and refine clustering for scRNA-seq data analysis.
Main Methods:
- CORTADO framework maximizes differential gene expression.
- Minimizes marker redundancy using cosine similarity and enforces sparsity.
- Iterative refinement approach using CORTADO-selected markers.
Main Results:
- Significant increase in Adjusted Rand Index (ARI) for cell-type identification.
- Demonstrated superior performance in marker discovery and clustering accuracy across diverse datasets (brain, immune, spatial, cancer).
- Identified biologically relevant markers.
Conclusions:
- CORTADO offers a robust and effective method for analyzing scRNA-seq data.
- Improves the accuracy of cell subpopulation identification and marker discovery.
- Outperforms existing state-of-the-art methods in computational analysis of single-cell data.
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