ArticleScientific reports2024
Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- STcompare: comparative spatial transcriptomics data analysis of structurally matched tissues to characterize differentially spatially patterned genes.Bioinformatics (Oxford, England) · 2026Article
- TIMSImaging: an open and interoperable workflow for trapped ion mobility mass spectrometry imaging data processing and visualization.Bioinformatics (Oxford, England) · 2026Article
- Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.Cell reports methods · 2026Article
- Spatial Transcriptomics and Dual Dye Mapping Identify Wnt-Driven BBB Protection in Endothelial EphA4-Deficiency.Research square · 2026Article
- Differential expression analysis for spatially correlated data using smiDE.Genome biology · 2026Article
- Spatial transcriptomic profiling identifies lacrimal-gland-epithelial cell-driven mechanisms underlying autoimmunity in Sjögren's disease.Frontiers in immunology · 2026Article
- Differential expression analysis in single cell and spatial RNASeq without model assumptions.bioRxiv : the preprint server for biology · 2025Article
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- Spatial transcriptomic profiling identifies lacrimal gland epithelial cell-driven mechanisms underlying autoimmunity in Sjögren's disease.bioRxiv : the preprint server for biology · 2025Article
- Spatial Dependence and Heterogeneity in Molecular Imaging: Moran Quadrant Maps Enable Advanced Spatial-Statistical Analysis.bioRxiv : the preprint server for biology · 2025Article
- scBSP: a fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data.Bioinformatics (Oxford, England) · 2025Article
- Prioritizing perturbation-responsive gene patterns using interpretable deep learning.Nature communications · 2025Article
- spatialGE Is a User-Friendly Web Application That Facilitates Spatial Transcriptomics Data Analysis.Cancer research · 2025Article
- scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data.bioRxiv : the preprint server for biology · 2025Article
- Cell-specific priors rescue differential gene expression in spatial spot-based technologies.Briefings in bioinformatics · 2024Article
- Recovering single-cell expression profiles from spatial transcriptomics with scResolve.Cell reports methods · 2024Article
- spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis.bioRxiv : the preprint server for biology · 2024Article
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6 authors.
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Abstract
Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).
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