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Updated: Jan 10, 2026

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
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Can LLMs Bridge Domain and Visualization? A Case Study on High-Dimension Data Visualization in Single-Cell

Qianwen Wang, Xinyi Liu, Nils Gehlenborg

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    Summary
    This summary is machine-generated.

    Large Language Models (LLMs) can analyze scientific papers to understand how high-dimension (HD) data visualizations are used in sing-cell transcriptomics research. This approach reveals key visualization patterns like trajectories and inter-cluster relationships.

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    Area of Science:

    • Data Visualization
    • Bioinformatics
    • Scientific Literature Analysis

    Background:

    • Understanding real-world visualization usage is challenging, often relying on limited user interviews or visualization-focused papers.
    • Existing methods lack comprehensive insight into how domain experts utilize visualizations 'in the wild'.
    • High-dimension (HD) data visualization in sing-cell transcriptomics involves complex, specialized terminology.

    Purpose of the Study:

    • To investigate the potential of Large Language Models (LLMs) for analyzing visualization usage within domain-specific literature.
    • To bridge the gap between visualization design and actual domain application by studying visualization usage in sing-cell transcriptomics.
    • To develop a robust methodology for analyzing large-scale scientific literature concerning visualization practices.

    Main Methods:

    • Developed a human-in-the-loop LLM workflow to analyze 1,203 papers describing 2,056 HD visualizations.
    • Integrated image processing and traditional NLP techniques to prepare data for LLM analysis.
    • Employed three targeted LLM subtasks: domain terminology translation, analysis task summarization, and categorization, with human validation checkpoints.

    Main Results:

    • Successfully analyzed a large corpus of sing-cell transcriptomics papers, identifying 2,056 HD visualizations.
    • Revealed three underappreciated aspects of HD visualization usage: trajectories in HD spaces, inter-cluster relationships, and dimension clustering.
    • Validated analysis findings through expert interviews and a dedicated test set.

    Conclusions:

    • LLMs, augmented by human oversight and complementary methods, offer a powerful tool for analyzing visualization usage in specialized scientific domains.
    • The developed workflow provides a scalable approach to understanding visualization practices 'in the wild'.
    • This research lays groundwork for future studies using LLMs to connect visualization design with domain-specific application and needs.