Work

Lense: optimizing data preprocessing in single-cell omics using large language models

Year: 2026

Type: article

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.

Source: Briefings in Bioinformatics

DOI: 10.1093/bib/bbag288

Authors: Jingyun Liu, Zhicheng Ji

Institutions: Duke University, Duke Medical Center

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,885

Funder: National Institutes of Health