ReSort enhances reference-based cell type deconvolution for spatial transcriptomics through regional information integration.

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Bioinformatics advances Pub Date : 2025-05-27 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbaf091

Linhua Wang, Ling Wu, Guantong Qi, Chaozhong Liu, Wanli Wang, Xiang H-F Zhang, Zhandong Liu

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引用次数: 0

Abstract

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.

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ReSort通过区域信息整合增强了基于参考的细胞类型反褶积的空间转录组学。

动机：空间转录组学（ST）捕获组织内的位置基因表达，但缺乏单细胞分辨率。研究人员开发了基于参考文献的细胞类型反卷积方法，以了解ST的细胞类型分布。然而，参考文献和ST之间的批/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排序（ReSort），它利用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，发现上皮克隆中巨噬细胞M0和M2的富集，揭示了上皮-间质转化和免疫浸润的意义。可用性和实现：ReSort的源代码可以在（https://github.com/LiuzLab/RESORT）上公开获得，用Python实现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

发文量