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Related Experiment Videos

Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved

Cong Ma1, Uthsav Chitra1, Shirley Zhang1

  • 1Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540, USA.

Cell Systems
|October 20, 2022
PubMed
Summary
This summary is machine-generated.

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We introduce Belayer, a new model for spatially resolved transcriptomics (SRT) data from layered tissues. Belayer accurately identifies tissue layers and spatially varying genes, improving analysis of complex tissue structures.

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatially resolved transcriptomics (SRT) enables gene expression analysis within tissue context.
  • Existing SRT methods often assume continuous or discrete spatial variation, limiting analysis of layered tissues.
  • Layered tissues, like the brain and skin, present unique challenges for spatial gene expression analysis.

Purpose of the Study:

  • To develop a novel computational model and algorithm for analyzing SRT data from layered tissues.
  • To account for both continuous and discrete spatial variations in gene expression within tissue layers.
  • To accurately identify tissue layers and spatially varying genes in complex layered tissues.

Main Methods:

  • Proposed a model for SRT data incorporating continuous and discrete spatial variation.
Keywords:
conformal mapsgene expressionlayered tissuessegmented regressionspatial variationspatially resolved transcriptomics

Related Experiment Videos

  • Developed an algorithm named Belayer to learn model parameters.
  • Utilized conformal maps to model relative depth within tissue layers.
  • Employed a dynamic programming algorithm to infer layer boundaries and gene expression functions.

    • Main Results:

    • Belayer accurately identifies distinct tissue layers in SRT data.
    • The algorithm successfully detects biologically meaningful spatially varying genes.
    • Demonstrated effectiveness on SRT data from brain and skin tissues.
    • The model captures piecewise linear gene expression with potential discontinuities at layer boundaries.

    Conclusions:

    • Belayer offers a powerful new approach for analyzing gene expression in layered tissues using SRT.
    • The method enhances the ability to discover spatially variable genes and understand tissue organization.
    • Belayer provides a framework for integrating continuous and discrete spatial variation models in transcriptomics.