Computer Methods and Programs in Biomedicine Update · Published 2026-01-26 · DOI 10.1016/j.cmpbup.2026.100232
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.
Abstract from DOAJ. Public domain (CC0 1.0).
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