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Updated: Jun 8, 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

Adding Highly Variable Genes to Spatially Variable Genes Can Improve Cell Type Clustering Performance in Spatial

Yijun Li¹, Stefan Stanojevic², Bing He²

¹Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.

Research Square

|November 6, 2024

Summary

This summary is machine-generated.

Combining highly variable (HV) genes with spatially variable (SV) genes enhances cell type clustering in spatial transcriptomics. This integrated approach improves the analysis of gene expression patterns within tissue samples.

Keywords:

clustering feature selection spatial transcriptomics

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Published on: October 25, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics enables gene expression analysis within tissue context.
Spatially variable (SV) genes show expression patterns related to tissue structure.
Highly variable (HV) genes exhibit significant cell-to-cell expression differences.

Purpose of the Study:

To investigate if incorporating highly variable (HV) genes improves cell type clustering in spatial transcriptomics.
To compare clustering performance using HV genes, SV genes, and their combined set.

Main Methods:

Tested clustering performance on over 50 spatial transcriptomics datasets.
Utilized multiple platforms and both spatial and non-spatial evaluation metrics.
Compared gene sets: HV genes only, SV genes only, and the union of both.

Main Results:

The combination of HV and SV genes demonstrated improved cell type clustering.
This integrated gene set approach outperformed using either HV or SV genes alone.
Performance gains were consistent across diverse datasets and platforms.

Conclusions:

Integrating highly variable and spatially variable genes is a superior strategy for cell type clustering in spatial transcriptomics.
This method enhances the accuracy and robustness of spatial transcriptomics data analysis.
The findings suggest a new standard for gene selection in spatial transcriptomics studies.