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Cell Specific Gene Expression

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Related Experiment Video

Updated: Sep 18, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

QuadST identifies cell-cell interaction-changed genes in spatially resolved transcriptomics data.

Xiaoyu Song¹, Yuqing Shang², Michelle Ehrlich³

¹Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857; song.xiaoyu@duke-nus.edu.sg.

Genome Research

|June 23, 2025

Summary

This summary is machine-generated.

QuadST is a new statistical method that robustly identifies cell-cell interactions and their impacted genes in spatially resolved transcriptomics (SRT). It improves upon existing methods by controlling errors and increasing power for disease research.

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Last Updated: Sep 18, 2025

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Published on: March 12, 2021

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatially resolved transcriptomics (SRT) enables the study of cell-cell interactions crucial for understanding diseases.
Existing methods for identifying these interactions have limitations.

Purpose of the Study:

To introduce QuadST, a novel statistical method for robustly identifying cell-cell interactions and their impacted genes in single-cell SRT.
To provide a powerful and accurate tool for analyzing spatial transcriptomic data.

Main Methods:

QuadST models cell-cell interactions at varying distance quantiles.
It contrasts signals to identify genes affected by interactions, prioritizing those with stronger signals at shorter distances.
The method does not require pre-specified interacting cell pairs and is robust to confounding factors and data errors.

Main Results:

Simulation studies show QuadST effectively controls type I errors, even with misspecified models.
QuadST significantly improves statistical power compared to existing methods.
Applications to real datasets identified biologically significant interaction-changed genes across diverse cell types.

Conclusions:

QuadST offers a robust and powerful approach for analyzing cell-cell interactions in SRT.
This method enhances the discovery of genes involved in disease mechanisms through spatial transcriptomic analysis.
QuadST advances the field of spatial biology and disease research.