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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition.

Charles Broadbent1, Tianci Song1, Rui Kuang1

  • 1Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, 55455, United States.

Bioinformatics (Oxford, England)
|June 28, 2024
PubMed
Summary
This summary is machine-generated.

GraphTucker, a novel method for spatial transcriptomics (ST) data, effectively models high-order gene expression patterns. This approach enhances the detection of spatial gene modules and improves tissue segmentation for better biological insights.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) profiling reveals cellular organization and function within tissues.
  • Analyzing the high-order structure of gene expression in ST data across spatial coordinates remains a significant challenge.

Purpose of the Study:

  • To introduce GraphTucker, a novel graph-regularized Tucker tensor decomposition method for ST data.
  • To address the challenge of modeling and detecting interpretable high-order elements in spatial gene expression data.

Main Methods:

  • GraphTucker utilizes a nonnegative Tucker decomposition algorithm.
  • The method incorporates a high-order graph to capture spatial relationships among spots and functional relationships among genes.
  • It models multiway multilinear relationships within the ST data components.

Main Results:

  • GraphTucker demonstrated superior performance compared to Canonical Polyadic Decomposition and conventional matrix factorization.
  • The method effectively detected spatial components of gene modules and clustered spatial coefficients for tissue segmentation.
  • Experiments on Visium and Stereo-seq datasets showed GraphTucker's ability to impute complete spatial transcriptomes.

Conclusions:

  • GraphTucker provides a powerful tool for analyzing the complex, high-order structures in spatial transcriptomics data.
  • The method enhances the interpretability of spatial gene expression patterns within tissue domains.
  • GraphTucker facilitates more accurate tissue segmentation and gene expression imputation in ST studies.