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Updated: Jul 26, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural Networks.

Yawei Li^1,2, Yuan Luo^1,2

¹Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.

Biorxiv : the Preprint Server for Biology

|June 19, 2023

Summary

This summary is machine-generated.

We developed STdGCN, a novel graph neural network model for spatial transcriptomic (ST) data analysis. This method accurately deconvolutes cell types in spatial transcriptomics, outperforming existing models.

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Last Updated: Jul 26, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

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Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

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Published on: March 14, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatially resolved transcriptomics (ST) measures gene expression with spatial context but often lacks single-cell resolution.
Existing ST technologies analyze mixed cell populations within spatial spots, limiting precise cellular analysis.
Accurate cell type deconvolution is crucial for understanding tissue organization and function.

Conclusions:

STdGCN offers a powerful new approach for cell type deconvolution in ST data.
The model effectively leverages both expression and spatial information for high-resolution tissue analysis.
STdGCN facilitates deeper insights into tumor microenvironments and developmental processes.