ArticleCell systems2022
Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics.
Article in Cell systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 29 citations in OpenAlex.
- Spatial transcriptomics profiling across tissue boundaries in the developing mouse brain.iScience · 2026Article
- LYNX: a deep generative model for linking spatial dynamics and cell interactions in multimodal spatial data.bioRxiv : the preprint server for biology · 2026Article
- Riemannian metric learning for alignment of spatial multiomics.Bioinformatics (Oxford, England) · 2026Article
- Decoding the causal drivers of spatial cellular topology.iScience · 2026Article
- CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data.Bioinformatics (Oxford, England) · 2026Article
- Riemannian Metric Learning for Alignment of Spatial Multiomics.bioRxiv : the preprint server for biology · 2025Article
- MaskGraphene: an advanced framework for interpretable joint representation for multi-slice, multi-condition spatial transcriptomics.Genome biology · 2025Article
- GASTON-Mix: a unified model of spatial gradients and domains using spatial mixture-of-experts.Bioinformatics (Oxford, England) · 2025Article
- Mapping the topography of spatial gene expression with interpretable deep learning.Nature methods · 2025Article
- Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data.Nature communications · 2025Review
- Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics.Computational and structural biotechnology journal · 2024Review
- Article
- Benchmarking clustering, alignment, and integration methods for spatial transcriptomics.Genome biology · 2024Article
- A count-based model for delineating cell-cell interactions in spatial transcriptomics data.Bioinformatics (Oxford, England) · 2024Article
- spVC for the detection and interpretation of spatial gene expression variation.Genome biology · 2024Article
- Mapping the topography of spatial gene expression with interpretable deep learning.bioRxiv : the preprint server for biology · 2023Article
- Principles and challenges of modeling temporal and spatial omics data.Nature methods · 2023Review
- Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.