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SpaceWalker enables interactive gradient exploration for spatial transcriptomics data.

Chang Li1, Julian Thijssen1, Thomas Kroes1

  • 1Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands.

Cell Reports Methods
|November 16, 2023
PubMed
Summary
This summary is machine-generated.

SpaceWalker is a new visual analytics tool that helps researchers explore gene expression gradients in spatial transcriptomics data. It identifies transcriptomically similar cells to reveal tissue structures and confirm biological findings.

Keywords:
CP: Systems biologydata visualizationgene expression gradientsspatial transcriptomicsvisual analytics

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Gene expression gradients in spatial transcriptomics (ST) data reveal critical biological features like tissue compartments and cell-state transitions.
  • Effective visualization and analysis tools are needed to explore these complex, high-dimensional datasets.

Purpose of the Study:

  • To introduce SpaceWalker, a visual analytics tool designed for exploring local gradient structures in 2D and 3D spatial transcriptomics data.
  • To enable users to identify transcriptomically similar cells and visualize tissue architecture.

Main Methods:

  • SpaceWalker utilizes local intrinsic dimensionality to guide seed location selection.
  • A flood-fill algorithm identifies transcriptomically similar cells based on high-dimensional data topology.
  • The tool projects identified cells spatially and allows interactive retrieval of gene expression gradients.

Main Results:

  • Spatial projection of identified cells highlights tissue architectural features.
  • Interactive analysis of gene expression gradients confirms known biological patterns.
  • SpaceWalker demonstrates versatility across different ST protocols and scales effectively to large 3D datasets.

Conclusions:

  • SpaceWalker provides an effective method for exploring gene expression gradients in spatial transcriptomics data.
  • The tool aids in uncovering tissue architecture and validating biological hypotheses.
  • Its scalability and compatibility with various ST protocols make it a valuable resource for researchers.