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Deep clustering representation of spatially resolved transcriptomics data using multi-view variational graph

Jinyun Niu1, Fangfang Zhu2, Taosheng Xu3

  • 1School of Information Science and Engineering, Yunnan University, Kunming, 650091, Yunnan, China.

Computational and Structural Biotechnology Journal
|December 24, 2024
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Summary

STMVGAE integrates gene expression, histology, and spatial data for accurate spatial domain identification in spatial transcriptomics. This novel tool enhances downstream analyses, improving clustering accuracy and stability.

Keywords:
Consensus clusteringDeep learningMulti-view variational graph autoencodersSpatially resolved transcriptomics

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Spatial transcriptomics (ST) technology offers insights into tissue architecture.
  • Accurate spatial domain identification is vital for ST data analysis.
  • Integrating multi-modal ST data (gene expression, histology, spatial coordinates) is challenging.

Purpose of the Study:

  • To develop STMVGAE, a novel tool for spatial domain identification in ST data.
  • To effectively integrate gene expression, histological images, and spatial coordinate data.
  • To enhance the accuracy and stability of spatial domain identification.

Main Methods:

  • STMVGAE employs a multi-view variational graph autoencoder (VGAE) with consensus clustering.
  • Histological image features are extracted using a pre-trained CNN.
  • Multiple graphs are constructed using various similarity measures, integrated with gene expression data.

Main Results:

  • STMVGAE achieved competitive results across five real ST datasets compared to state-of-the-art methods.
  • The tool demonstrated robust performance in spatial domain identification.
  • Evaluated effectiveness in downstream tasks like UMAP visualization and trajectory inference.

Conclusions:

  • STMVGAE provides an effective framework for integrating multi-modal ST data.
  • The method significantly improves spatial domain identification accuracy and stability.
  • STMVGAE offers a valuable tool for advancing spatial transcriptomics research.