stImage: a versatile framework for optimizing spatial transcriptomic analysis through customizable deep histology and location informed integration
- bgtaylor1
- Nov 11, 2025
- 1 min read

Date: | August 31, 2025 |
PMID: | |
Category: | N/A |
Authors: | Yu Wang, Haichun Yang, Ruining Deng, Yuankai Huo, Qi Liu, Yu Shyr, Shilin Zhao |
Abstract: | 40905789 |
Spatial transcriptomics (ST) integrates gene expression data with the spatial organization of cells and their associated histology, offering unprecedented insights into tissue biology. While existing methods incorporate either location-based or histology-informed information, none fully synergize gene expression, histological features, and precise spatial coordinates within a unified framework. Moreover, these methods often exhibit inconsistent performance across diverse datasets and conditions. Here, we introduce stImage, an open-source R package that provides a comprehensive and flexible solution for ST analysis. By generating deep learning-derived histology features and offering 54 integrative strategies, stImage seamlessly combines transcriptional profiles, histology images, and spatial information. We demonstrate stImage's effectiveness across multiple datasets, underscoring its ability to guide users toward the most suitable integration strategy using diagnostic graph. Our results highlight how stImage can optimize ST, consistently improving biological insights and advancing our understanding of tissue architecture. stImage is freely available at https://github.com/YuWang-VUMC/stImage.
Acknowledgements:
The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute, or the National Institute of Health.
The Translational and Basic Science Research in Early Lesions (TBEL) Research Consortia is supported and funded by grants from the National Cancer Institute and the National Institutes of Health under the following award numbers:
Project Number: | Awardee Organization |
U54CA274374 | Fred Hutchinson Cancer Center |
U54CA274375 | Houston Methodist Research Institute |
U54CA274370 | Johns Hopkins University |
U54CA274371 | UT MD Anderson Cancer Center |
U54CA274367 | Vanderbilt University Medical Center |



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