<p>Single-cell RNA sequencing generates high-dimensional gene expression profiles for individual cells. A key step in its analysis is dimensionality reduction, which transforms these data into lower-dimensional representations for clustering and cell type identification. Current approaches often fail to balance technical noise removal with the preservation of biologically meaningful cell-type signals. Here, we present CellMentor, a fully supervised dimensionality reduction method based on non-negative matrix factorization that integrates cell type labels directly into its optimization objective. CellMentor minimizes variation within known populations while maximizing distinctions between types, producing low-dimensional embeddings optimized for cell type identification. Across diverse simulated and experimental datasets, CellMentor shows superior cell type separation, robust batch correction, and effective detection of rare cell populations, offering a valuable tool for integrative single-cell analyses across experiments.</p>