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Transformer and graph variational autoencoder to identify microenvironments: A deep learning protocol for spatial

Karla Paniagua1, Yufei Huang2, Shou-Jiang Gao3

  • 1Department of Electrical and Computer Engineering, KLESSE School of Engineering and Integrated Design, University of Texas at San Antonio, San Antonio, TX 78249, USA.

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Summary
This summary is machine-generated.

We developed a computational framework using transformer and graph variational autoencoders to identify spatial microenvironments (TG-ME). This deep learning approach enables robust niche clustering in various tissue types.

Keywords:
BioinformaticsCancerGene ExpressionGenomicsSequence analysisSingle Cell

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Spatial transcriptomics

Background:

  • Understanding tissue microenvironments is crucial for disease research.
  • Spatial transcriptomics and morphological imaging offer complementary data for niche analysis.

Purpose of the Study:

  • To present a computational framework, TG-ME, for dissecting spatial niches.
  • To integrate spatial transcriptomics and morphological data for microenvironment identification.

Main Methods:

  • Utilizing transformer and graph variational autoencoders (TG-ME).
  • Implementing data normalization and morphological feature extraction.
  • Applying deep learning for robust niche clustering.

Main Results:

  • TG-ME successfully integrates diverse spatial data.
  • The framework enables effective clustering of spatial niches.
  • Applicable to healthy, tumor, and infected tissues.

Conclusions:

  • TG-ME provides a powerful tool for spatial niche analysis.
  • The computational framework enhances understanding of tissue microenvironments.
  • Deep learning integration facilitates robust microenvironment identification.