ReSort通过区域信息整合增强了基于参考的空间转录组细胞类型反卷积分析。
ReSort enhances reference-based cell type deconvolution for spatial transcriptomics through regional information integration.
作者信息
Wang Linhua, Wu Ling, Qi Guantong, Liu Chaozhong, Wang Wanli, Zhang Xiang H-F, Liu Zhandong
机构信息
Graduate School of Biomedical Sciences, Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, United States.
Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, United States.
出版信息
Bioinform Adv. 2025 May 27;5(1):vbaf091. doi: 10.1093/bioadv/vbaf091. eCollection 2025.
MOTIVATION
Spatial transcriptomics (ST) captures positional gene expression within tissues but lacks single-cell resolution. Reference-based cell type deconvolution methods were developed to understand cell type distributions for ST. However, batch/platform discrepancies between references and ST impact their accuracy.
RESULTS
We present Region-based Cell Sorting (ReSort), which utilizes ST's region-level data to lessen reliance on reference data and alleviate these technical issues. In simulation studies, ReSort enhances reference-based deconvolution methods. Applying ReSort to a mouse breast cancer model highlights macrophages M0 and M2 enrichment in the epithelial clone, revealing insights into epithelial-mesenchymal transition and immune infiltration.
AVAILABILITY AND IMPLEMENTATION
Source codes for ReSort are publicly available at (https://github.com/LiuzLab/RESORT), implemented in Python.
动机
空间转录组学（ST）可捕获组织内的位置基因表达，但缺乏单细胞分辨率。基于参考的细胞类型反卷积方法被开发出来以了解ST的细胞类型分布。然而，参考数据与ST之间的批次/平台差异会影响其准确性。
结果
我们提出了基于区域的细胞分选（ReSort）方法，该方法利用ST的区域级数据来减少对参考数据的依赖并缓解这些技术问题。在模拟研究中，ReSort增强了基于参考的反卷积方法。将ReSort应用于小鼠乳腺癌模型，突出了上皮克隆中M0和M2巨噬细胞的富集，揭示了上皮-间质转化和免疫浸润的相关见解。
可用性和实现方式
ReSort的源代码可在（https://github.com/LiuzLab/RESORT）上公开获取，用Python实现。
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