Single-cell Tree-based Model for Genomic-Disease Association.

ABSTRACT: The rapid maturation of single-cell multi-omics technologies has enabled unprecedented resolution for mapping disease states and identifying disease-associated biomarkers. In practice, biomarkers are often discovered through differential detection that treat genomic features as independent contributors to phenotypes, while the combinatorial interactions that drive clinical outcomes remain a practical challenge. We present scanCT (single-cell analysis of Clinical Tree), a tree-based framework that identifies groups of genomic features associated with distinct disease phenotypes in a highly interpretable manner. scanCT uses an unbiased, model-based variable-selection procedure for data-driven split selection, which is important for handling the diverse distributional properties of single-cel

SUBMITTER: Liu Z 

PROVIDER: S-EPMC12776419 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature