LETSmix: a spatially informed and learning-based domain adaptation method for cell-type deconvolution in spatial transcriptomics

LETSmix: a spatially informed and learning-based domain adaptation method for cell-type deconvolution in spatial transcriptomics

Abstract Spatial transcriptomics (ST) enables the study of gene expression in spatial context, but many ST technologies face challenges due to limited resolution, leading to cell mixtures at each spot. We present LETSmix to deconvolve cell types by integrating spatial correlations through a tailored...

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Bibliographic Details
Main Authors: Yangen Zhan, Yongbing Zhang, Zheqi Hu, Yifeng Wang, Zirui Zhu, Sijing Du, Xiangming Yan, Xiu Li
Format: Article
Language:English
Published: BMC 2025-02-01
Series:Genome Medicine
Subjects:
Spatial transcriptomics
Single-cell RNA-seq
Cell-type deconvolution
Spatial correlation
Histological image
Domain adaptation
Online Access:https://doi.org/10.1186/s13073-025-01442-8
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