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TriCLFF: a multi-modal feature fusion framework using contrastive learning for spatial domain identification.

Fenglan Pang1, Guangfu Xue1, Wenyi Yang1

  • 1Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, 92 Xidazhi Street, Nangang District, Harbin 150001, Heilongjiang Province, China.

Briefings in Bioinformatics
|July 10, 2025
PubMed
Summary
This summary is machine-generated.

A new framework called TriCLFF effectively integrates spatial transcriptomics data, including gene expression and histology, for accurate spatial domain identification. This method enhances understanding of tissue organization and cell states.

Keywords:
contrastive learningfeature fusionmulti-modal learningspatial domain identificationspatial transcriptomics

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) offers rich multi-modal data on cell state and tissue organization.
  • Accurate identification of spatial domains requires integrating gene expression, histology, and spatial information.
  • Existing methods face challenges in comprehensively fusing these diverse data types.

Purpose of the Study:

  • To develop an advanced framework for multi-modal feature fusion in spatial transcriptomics.
  • To improve the accuracy and robustness of spatial domain identification using integrated data.
  • To uncover novel biological insights from spatial transcriptomics data.

Main Methods:

  • Proposed TriCLFF (contrastive learning-based multi-modal feature fusion) framework.
  • Integration of spatial associations, gene expression levels, and histological features.
  • Evaluation across diverse datasets (mouse brain, olfactory bulb, human PFC, breast cancer) and platforms (10x Visium, Stereo-seq).

Main Results:

  • TriCLFF significantly outperforms state-of-the-art methods in spatial domain identification accuracy and robustness.
  • Successfully identified finer-grained structures in breast cancer tissues.
  • Discovered novel gene expression patterns in the human dorsolateral prefrontal cortex.

Conclusions:

  • TriCLFF provides an effective paradigm for integrating multi-modal spatial transcriptomics data.
  • The framework advances the field of spatial domain identification and biological discovery.
  • Demonstrates potential for deeper understanding of tissue functions and disease mechanisms.