ArticleCell reports methods2022
SpatialCorr identifies gene sets with spatially varying correlation structure.
Article in Cell reports methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cross-expression meta-analysis of mouse brain slices reveals coordinated gene expression across spatially adjacent cells.Genome biology · 2025Pooled it
- A reproducible endothelial-state contrast with limited bulk prognostic robustness in hepatocellular carcinoma.Functional & integrative genomics · 2026Article
- Flexible and scalable inference of spatially varying correlation in spatial transcriptomics with spCorr.Genome research · 2026Article
- geneSCOPE: gene spatial co-occurrence of pairwise expression.Briefings in bioinformatics · 2026Article
- SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling.Nature communications · 2026Article
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- Finding spatially variable ligand-receptor interactions with functional support from downstream genes.Nature communications · 2025Article
- Cross-expression meta-analysis of 695 brain samples reveals coordinated gene expression across spatially adjacent cells.bioRxiv : the preprint server for biology · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- The tumor microenvironment across four dimensions: assessing space and time in cancer biology.Frontiers in immunology · 2025Review
- SpatialView: an interactive web application for visualization of multiple samples in spatial transcriptomics experiments.Bioinformatics (Oxford, England) · 2024Article
- SpatialDM for rapid identification of spatially co-expressed ligand-receptor and revealing cell-cell communication patterns.Nature communications · 2023Article
- Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS.Science advances · 2023Article
- Spatial transcriptomics in glioblastoma: is knowing the right zip code the key to the next therapeutic breakthrough?Frontiers in oncology · 2023Review
- Model-free prediction test with application to genomics data.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies.Genome biology · 2022Article
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Authors and funding
8 authors.
Funding
Abstract
Recent advances in spatially resolved transcriptomics technologies enable both the measurement of genome-wide gene expression profiles and their mapping to spatial locations within a tissue. A first step in spatial transcriptomics data analysis is identifying genes with expression that varies spatially, and robust statistical methods exist to address this challenge. While useful, these methods do not detect spatial changes in the coordinated expression within a group of genes. To this end, we present SpatialCorr, a method for identifying sets of genes with spatially varying correlation structure. Given a collection of gene sets pre-defined by a user, SpatialCorr tests for spatially induced differences in the correlation of each gene set within tissue regions, as well as between and among regions. An application to cutaneous squamous cell carcinoma demonstrates the power of the approach for revealing biological insights not identified using existing methods.
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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.