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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Published on: July 6, 2022

Graph domain adaptation-based framework for gene expression enhancement and cell type identification in large-scale

Rongbo Shen^1,2,3, Meiling Cheng^2,4, Wencang Wang²

¹GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, No. 1 Xinzao Road, Xinzao Town, Panyu District, Guangzhou 510005, China.

Briefings in Bioinformatics

|November 7, 2024

Summary

SpaGDA, a deep learning framework, enhances spatial transcriptomics by improving gene detection and cell type identification. It effectively transfers knowledge from single-cell RNA sequencing data, offering more accurate biological insights.

Keywords:

cell type identification deep learning–based graph domain adaptation gene expression enhancement graph convolutional network spatially resolved transcriptomics

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatially resolved transcriptomics (SRT) offers gene expression profiling with spatial context.
Current SRT technologies face limitations in transcript detection sensitivity and gene throughput, impacting data precision and coverage.

Purpose of the Study:

To introduce SpaGDA, a deep learning-based graph domain adaptation framework.
To address gene expression imputation and cell type identification challenges in SRT data.
To leverage reference single-cell RNA sequencing (scRNA-seq) data for improved analysis.

Main Methods:

Developed SpaGDA, a graph domain adaptation framework utilizing deep learning.
Applied SpaGDA to impute gene expression and identify cell types in SRT datasets.
Benchmarked SpaGDA against state-of-the-art methods across multiple SRT datasets and biological contexts.

Main Results:

SpaGDA demonstrated superior effectiveness in gene expression imputation and cell type identification compared to existing methods.
The framework successfully recovered established biological knowledge from public atlases and literature.
SpaGDA generated more informative spatial gene expression patterns.

Conclusions:

SpaGDA effectively overcomes limitations of current SRT technologies.
The framework provides more accurate insights into biological processes and disease development.
SpaGDA is a valuable tool for advancing spatial transcriptomics research.