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

• Fully automated, data-driven clustering pipeline for scRNA-seq analysis. • Addresses the limitation of fixed principal component defaults in DR. • Data-driven pipeline improves clustering performance by 10-14%. • Performance gains are most pronounced on high-sparsity, high-skewness data. 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.