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Updated: Jan 11, 2026

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

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

Published on: July 6, 2022

scGPD: single-cell informed gene panel design for targeted spatial transcriptomics.

Yunshan Guo¹, Jia Zhao¹, Rui B Chang^2,3

¹Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA.

Biorxiv : the Preprint Server for Biology

|November 19, 2025

Summary

This summary is machine-generated.

We developed scGPD, a deep learning tool for selecting informative gene panels for spatial transcriptomics. It identifies compact, nonredundant gene sets, improving cell type classification and spatial analysis.

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Selecting informative gene panels is crucial for targeted spatial transcriptomics.
Existing methods often overlook gene-gene correlations, leading to suboptimal tissue heterogeneity capture.

Purpose of the Study:

To introduce scGPD, a deep learning framework for designing efficient gene panels for spatial profiling.
To address limitations in current gene panel selection methods.

Main Methods:

scGPD leverages single-cell RNA sequencing data.
It employs a gene-gene correlation-aware gating mechanism to select compact, nonredundant gene sets.
The framework is designed for flexibility and adaptability.

Main Results:

scGPD outperforms existing methods in recovering transcriptome-wide expression with limited genes.
It achieves superior cell type classification accuracy on spatial transcriptomics data.
Selected gene panels show well-defined spatial expression patterns.

Conclusions:

scGPD offers a robust and adaptable solution for designing gene panels for spatial transcriptomics.
The framework enhances tissue mapping and disease characterization by enabling efficient spatial profiling.
scGPD facilitates prioritization of genes relevant to specific diseases or phenotypes.