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Related Experiment Video

Updated: Jul 4, 2025

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

Gene panel selection for targeted spatial transcriptomics.

Yida Zhang^1,2, Viktor Petukhov¹, Evan Biederstedt¹

¹Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

|January 25, 2024

Summary

This summary is machine-generated.

We developed gpsFISH, a new computational method for selecting genes in spatial transcriptomics. It optimizes cell type detection and accounts for platform differences, outperforming existing methods.

Keywords:

Cell type hierarchy Gene panel selection Platform effect Single-cell RNA sequencing Targeted spatial transcriptomics

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Targeted spatial transcriptomics enables complex tissue analysis.
Current methods often rely on pre-selected gene panels, limiting discovery.
Existing gene selection methods are biased by single-cell RNA sequencing (scRNA-seq) platform effects.

Purpose of the Study:

To introduce gpsFISH, a novel computational method for gene selection in targeted spatial transcriptomics.
To improve the accuracy and reliability of gene selection by addressing platform effects.
To provide a flexible tool for gene selection that accommodates diverse experimental needs.

Main Methods:

Developed gpsFISH, a computational approach for gene selection.
Optimized gene selection by focusing on the detection of known cell types.
Modeled and adjusted for platform effects between different transcriptomic technologies.
Incorporated cell type hierarchies and custom gene preferences into the selection process.

Main Results:

gpsFISH demonstrated superior performance compared to existing gene selection methods.
The method effectively models and corrects for platform-specific biases.
gpsFISH successfully optimizes gene panels for accurate cell type identification.

Conclusions:

gpsFISH offers a robust and adaptable solution for gene selection in spatial transcriptomics.
The method enhances the utility of targeted spatial transcriptomics by improving gene panel design.
gpsFISH provides a valuable tool for researchers studying complex biological systems.