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

Cong Ma, Uthsav Chitra, Shirley Zhang, Benjamin J. Raphael

Research output: Contribution to journalArticlepeer-review

Abstract

Spatially resolved transcriptomics (SRT) technologies measure gene expression at known locations in a tissue slice, enabling the identification of spatially varying genes or cell types. Current approaches for these tasks assume either that gene expression varies continuously across a tissue or that a tissue contains a small number of regions with distinct cellular composition. We propose a model for SRT data from layered tissues that includes both continuous and discrete spatial variation in expression and an algorithm, Belayer, to learn the parameters of this model. Belayer models gene expression as a piecewise linear function of the relative depth of a tissue layer with possible discontinuities at layer boundaries. We use conformal maps to model relative depth and derive a dynamic programming algorithm to infer layer boundaries and gene expression functions. Belayer accurately identifies tissue layers and biologically meaningful spatially varying genes in SRT data from the brain and skin.

Original languageEnglish (US)
Pages (from-to)786-797.e13
JournalCell Systems
Volume13
Issue number10
DOIs
StatePublished - Oct 19 2022

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

Keywords

  • conformal maps
  • gene expression
  • layered tissues
  • segmented regression
  • spatial variation
  • spatially resolved transcriptomics

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