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Prioritizing perturbation-responsive gene patterns using interpretable deep learning.

Yan Cui1,2,3, Zhiyuan Yuan4,5,6

  • 1Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.

Nature Communications
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

River identifies genes with differential spatial expression patterns (DSEPs) across multiple conditions. This deep learning framework offers a scalable and interpretable solution for analyzing tissue-wide gene expression dynamics.

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Spatially resolved transcriptomics maps gene expression within tissue contexts.
  • Existing methods focus on single-slice spatial variation, limiting cross-condition analysis.
  • Identifying differential spatial expression patterns (DSEPs) across conditions is crucial for complex biological studies.

Purpose of the Study:

  • Introduce DSEP gene prioritization as a novel analytical task.
  • Present River, a deep learning framework for identifying condition-relevant spatial gene expression changes.
  • Provide a scalable and interpretable solution for multi-condition spatial transcriptomics analysis.

Main Methods:

  • Developed River, a deep learning framework with a two-branch predictive architecture.
  • Implemented a post hoc attribution strategy to rank genes by contribution to condition differences.
  • Incorporated spatially-informed modeling and decoupled spatial/non-spatial components for scalability and interpretability.

Main Results:

  • River successfully identifies genes with DSEPs across diverse biological contexts.
  • Demonstrated scalability to large spatial datasets and compatibility with various data types.
  • Applied to embryogenesis, diabetes, lupus, and triple-negative breast cancer, revealing condition-specific spatial patterns.

Conclusions:

  • River offers a flexible and scalable solution for analyzing tissue-wide expression dynamics across multiple biological conditions.
  • The framework prioritizes survival-associated spatial patterns in triple-negative breast cancer that generalize across patients.
  • River advances the analysis of complex spatial transcriptomics data, enabling new biological discoveries.