ReSort Enhances Reference-Based Cell Type Deconvolution for Spatial Transcriptomics Through Regional Information Integration

Authors

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

Language

English

Publication Date

1-1-2025

Journal

Bioinformatics Advances

DOI

10.1093/bioadv/vbaf091

PMID

40510374

PMCID

PMC12161990

PubMedCentral® Posted Date

5-27-2025

PubMedCentral® Full Text Version

Post-print

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.

Published Open-Access

yes

Recommended Citation

Wang, Linhua; Wu, Ling; Qi, Guantong; et al., "ReSort Enhances Reference-Based Cell Type Deconvolution for Spatial Transcriptomics Through Regional Information Integration" (2025). Duncan NRI Faculty and Staff Publications. 157.
https://digitalcommons.library.tmc.edu/duncar_nri_pub/157