scUmaper: An automated framework for doublet removal and cell-type annotation in single-cell transcriptomics

Single-cell RNA sequencing can resolve cellular heterogeneity but is sensitive to heterotypic doublets and often requires expert-driven preprocessing and annotation. We developed single cell utility matrices processing engine (scUmaper), an R/Seurat-native workflow that integrates quality control, biologically grounded doublet filtering, and marker-library-based cell-type annotation. scUmaper codifies lineage-marker incompatibility rules and applies global clustering followed by within-lineage re-clustering to reveal anomalous subclusters with implausible cross-lineage co-expression. Across six public human organ datasets, scUmaper removed additional high-confidence heterotypic doublets that were retained by simulation-based approaches and achieved annotation agreement comparable to or higher than commonly used R-based baselines, with competitive runtime. Stress tests with simulated ambient RNA contamination and reduced sequencing depth showed stable outputs under moderate degradation. scUmaper provides an interpretable and extensible framework that lowers barriers for reproducible single-cell RNA sequencing (scRNA-seq) analysis.