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

Abstract excerpt

<h4>ABSTRACT</h4> Spatial transcriptomics (ST) has revolutionized our understanding of gene expression patterns by incorporating spatial context. However, many ST technologies operate on heterogeneous cell mixtures due to limited spatial resolution. To resolve cell type composition at each sequencing spot, several deconvolution methods have been proposed. Yet, these approaches often underutilize spatial context i...

Topics

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• Brain
• Cancer Cells and Metastasis
• Cancer Genomics and Diagnostics
• COVID-19
• Mice
• Neural Networks, Computer
• Sequence Analysis, RNA
• Single-cell and spatial transcriptomics

Identifiers and source

Literature Corpus work

5e42b662-afe4-5fc6-8d58-154dff76938c

DOI

10.1101/2024.04.27.591425