ArticleCommunications biology2026
Smoothie: efficient inference and integration of spatial co-expression networks from denoised spatial transcriptomics data.
Article in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- The expression pattern of EXO70 subunits in single-cell stereo-seq of Arabidopsis thaliana leaves using coexistence network analysis.Scientific reports · 2026Article
- Smoothie: efficient inference and integration of spatial co-expression networks from denoised spatial transcriptomics data.Communications biology · 2026Article
- Spatiotemporal Atlas of Heart Development Reveals Blood-Flow-Dependent Cellular, Structural, Metabolic, and Spatial Remodeling.bioRxiv : the preprint server for biology · 2025Article
- SCOUT: Ornstein-Uhlenbeck modelling of gene expression evolution on single-cell lineage trees.bioRxiv : the preprint server for biology · 2025Article
- Linking spatial omics to patient phenotypes at the population scale by BSNMani: Bayesian scalar-on-network regression with manifold learning.medRxiv : the preprint server for health sciences · 2025Article
- Spatial Transcriptomics to Study Virus-Host Interactions.Annual review of virology · 2025Review
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Abstract
Finding correlations in spatial gene expression is fundamental in spatial transcriptomics, as co-expressed genes within a tissue are linked by regulation, function, pathway, or cell type. Yet, sparsity and noise in spatial transcriptomics data pose significant analytical challenges. Here, we introduce Smoothie, a pipeline that denoises spatial transcriptomics data with Gaussian smoothing and constructs and integrates genome-wide co-expression networks. Utilizing implicit and explicit parallelization, Smoothie scales to datasets exceeding 100 million spatially resolved spots with fast run times and low memory usage. We demonstrate how co-expression networks measured by Smoothie enable precise gene module detection, functional annotation of uncharacterized genes, linkage of gene expression to genome architecture, and multi-sample comparisons to assess stable or dynamic gene expression patterns across tissues, conditions, and time points. Overall, Smoothie provides a scalable and versatile framework for extracting deep biological insights from high-resolution spatial transcriptomics data.
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