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Related Experiment Video

Updated: Sep 11, 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

DiffusionST: a deep generative diffusion model-based framework for enhancing spatial transcriptomics data quality and

Yaxuan Cui¹, Yang Cui^2,3, Ruheng Wang⁴

¹Department of Computer Science, University of Tsukuba, Tennodai 1-1-1, Tsukuba-shi, Ibaraki-ken 3058577, Japan.

Briefings in Bioinformatics

|August 12, 2025

Summary

This summary is machine-generated.

DiffusionST enhances spatial transcriptomics (ST) data quality by imputing noisy datasets and improving clustering accuracy. This method effectively handles high-resolution ST data, aiding in biological discovery.

Keywords:

breast cancer tissues data enhancement diffusion model dropout noise spatial transcriptomics technology

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics (ST) technologies generate large datasets, but data quality is often limited by current sequencing methods.
Existing methods struggle with noise and imputation in ST data, hindering downstream analysis.
Accurate analysis of ST data is crucial for understanding tissue architecture and cellular functions.

Purpose of the Study:

To develop and validate DiffusionST, a novel computational method for imputing and clustering ST data.
To improve the accuracy and robustness of ST data analysis, particularly in the presence of noise.
To demonstrate the utility of DiffusionST in dissecting spatial domains and facilitating biological insights from high-resolution ST data.

Main Methods:

DiffusionST utilizes a graph convolutional network with a novel loss function for data denoising.
The method incorporates a diffusion model for data enhancement and employs the zero-inflated negative binomial distribution for noise reduction.
Performance was evaluated against established ST clustering and single-cell RNA sequencing imputation algorithms.

Main Results:

DiffusionST significantly outperforms eight leading ST clustering algorithms in accuracy.
The method shows superior data imputation capabilities compared to five single-cell RNA sequencing imputation algorithms.
DiffusionST demonstrates robustness against noise and effectively enhances ST data quality, validated through artificial noise introduction.
The model successfully dissects spatial domains in breast cancer tissues using survival analysis and cell-cell communication studies.

Conclusions:

DiffusionST is a powerful tool for imputing and clustering noisy ST data, enhancing overall data quality.
The method is well-suited for high-resolution ST data and provides valuable insights into tissue organization and function.
DiffusionST offers a robust solution for ST data analysis, advancing research in genomics and computational biology.