@inproceedings{wei-etal-2025-identifying,
    title = "Identifying Cellular Niches in Spatial Transcriptomics: An Investigation into the Capabilities of Large Language Models",
    author = "Wei, Huanhuan  and
      Luo, Xiao  and
      Yu, Hongyi  and
      Liang, Jinping  and
      Yang, Luning  and
      Lin, Lixing  and
      Popa, Alexandra  and
      Yan, Xiting",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.455/",
    pages = "9275--9289",
    ISBN = "979-8-89176-251-0",
    abstract = "Spatial transcriptomic technologies enable measuring gene expression profile and spatial information of cells in tissues simultaneously. Clustering of captured cells/spots in the spatial transcriptomic data is crucial for understanding tissue niches and uncovering disease-related changes.Current methods to cluster spatial transcriptomic data encounter obstacles, including inefficiency in handling multi-replicate data, lack of prior knowledge incorporation, and producing uninterpretable cluster labels.We introduce a novel approach, LLMiniST, to identify spatial niche using a zero-shot large language models (LLMs) by transforming spatial transcriptomic data into spatial context prompts, leveraging gene expression of neighboring cells/spots, cell type composition, tissue information, and external knowledge. The model was further enhanced using a two-stage fine-tuning strategy for improved generalizability. We also develop a user-friendly annotation tool to accelerate the creation of well-annotated spatial dataset for fine-tuning.Comprehensive method performance evaluations showed that both zero-shot and fine-tunned LLMiniST had superior performance than current non-LLM methods in many circumstances. Notably, the two-stage fine-tuning strategy facilitated substantial cross-subject generalizability. The results demonstrate the feasibility of LLMs for tissue niche identification using spatial transcriptomic data and the potential of LLMs as a scalable solution to efficiently integrate minimal human guidance for improved performance in large-scale datasets."
}