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Updated: May 27, 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

STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics.

Haohao Su¹, Yuesong Wu¹, Bin Chen^2,3,4

¹Department of Statistics and Probability, Michigan State University, East Lansing, 48824, MI, USA.

Nature Communications

|February 20, 2025

Summary

STANCE is a new statistical model for detecting spatially variable genes (SVGs) and cell type-specific SVGs (ctSVGs) in spatial transcriptomics. It provides rotation-invariant results, improving accuracy in gene expression analysis.

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Last Updated: May 27, 2025

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Detecting spatially variable genes (SVGs) is crucial in spatial transcriptomics.
Existing methods for cell type-specific SVGs (ctSVGs) can yield rotation-dependent results.
The relationship between SVGs and ctSVGs can be complex and variable.

Purpose of the Study:

To develop a unified statistical model for detecting both SVGs and ctSVGs.
To address the limitations of existing methods, particularly their dependence on tissue orientation.
To provide robust and rotation-invariant results in spatial transcriptomics analysis.

Main Methods:

Proposed STANCE, a unified statistical model using a linear mixed-effect framework.
Integrated gene expression, spatial location, and cell type composition data.
Employed a two-stage approach: initial SVG/ctSVG detection and subsequent ctSVG-specific testing.

Main Results:

STANCE demonstrated robust performance in simulations and real data analyses.
The model ensures tissue rotation-invariant results for SVG and ctSVG detection.
Validated through extensive simulations and public spatial transcriptomics datasets.

Conclusions:

STANCE offers a novel and improved approach for identifying SVGs and ctSVGs.
The method enhances the reliability and interpretability of spatial transcriptomics data.
STANCE has significant potential for advancing downstream analyses in the field.