Lense: optimizing data preprocessing in single-cell omics using large language models
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.
Fields of Study & Subjects
Single-cell and spatial transcriptomics
Bioinformatics and Genomic Networks
Cell Image Analysis Techniques
Publication details
- Journal / source
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Volume
- 27
- Issue
- 3
- Publication date
- 1 May 2026
- Language
- EN