A fully automated, data-driven approach for dimensionality reduction and clustering in single-cell RNA-seq analysis

Single-cell RNA sequencing (scRNA-seq) provides deep insights into cellular heterogeneity but demands robust dimensionality reduction (DR) and clustering to handle high-dimensional, noisy data. Many DR and clustering approaches rely on user-defined parameters, undermining reliability. Even automated clustering methods like ChooseR and MultiK still employ fixed principal component defaults, limiting their full automation. To overcome this limitation, we propose a fully automated clustering approach by integrating scLENS—a method for optimal PC selection—with these tools. Our fully automated approach improves clustering performance by ∼14 % for ChooseR and ∼10 % for MultiK and identifies additional cell subtypes, highlighting the advantages of adaptive, data-driven DR.