Lense: Optimizing data preprocessing in single-cell omics using LLMs

Data preprocessing is critical for single-cell omics analyses, but default pipelines often underperform on diverse datasets, especially from emerging platforms like spatial transcriptomics. We introduce Lense, a language-model-guided method that automatically selects optimal preprocessing by comparing plots that visualize low-dimensional representations across pipeline variants. Integrated with Seurat, Lense streamlines analysis and improves preprocessing robustness without requiring manual tuning. Biographical Note: Jingyun Liu is a Master's student in the Department of Biostatistics and Bioinformatics at Duke University. Dr. Zhicheng Ji is a tenure-track Assistant Professor in the Department of Biostatistics and Bioinformatics at Duke University. His research focuses on artificial intelligence and statistical modeling for single-cell genomics, spatial genomics, and biomedical imaging.