ReSort enhances reference-based cell type deconvolution for spatial transcriptomics through regional information integration
Year: 2024
Type: article
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.
Source: Bioinformatics Advances
Institutions: Baylor College of Medicine, Texas Children's Hospital
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,917
Awards: G345484959, G9000010214
OpenAlex ID: W4410769875