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

Liu, J.; Ji, Z.

doi:10.64898/2026.05.07.723465

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bioRxiv · 10.64898/2026.05.07.723465

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

Liu, J.·Ji, Z.

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Abstract

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 NoteJingyun Liu is a Masters 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.

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Liu, J., Ji, Z.. 2026-05-11. Lense: Optimizing data preprocessing in single-cell omics using LLMs. https://doi.org/10.64898/2026.05.07.723465

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