ArticleNature methods2020
Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies.
Article in Nature methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 302 papers, 2 of them syntheses that pooled it.
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Who cites it
302 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Cross-expression meta-analysis of mouse brain slices reveals coordinated gene expression across spatially adjacent cells.Genome biology · 2025Pooled it
- Society for Immunotherapy of Cancer: updates and best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) image analysis and data sharing.Journal for immunotherapy of cancer · 2025Guideline
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
- Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions.Briefings in bioinformatics · 2026Review
- Accurately Predicting Cell Type Abundance from Spatial Histology Image Through HPCell.Interdisciplinary sciences, computational life sciences · 2026Article
- Spatial isoform sequencing at single-cell resolution reveals cell-type-specific spatial isoform variability in multiple brain cell types.Nature methods · 2026Article
- DESpace2: detection of differential spatial patterns in spatial omics data.Bioinformatics (Oxford, England) · 2026Article
- Spatial multi-omics analyses of the tumor immune microenvironment: advances and novel therapeutic avenues.Molecular cancer · 2026Review
- ViMST: vision transformer-based dual modality multi-task graph contrastive network for spatial transcriptomics microenvironments investigation.BMC biology · 2026Article
- D3aist: data-driven detection of atypical interactions in spatial transcriptomics.BMC genomics · 2026Article
- Investigating Spatial Dynamics in Spatial Omics Data with StarTrail.Journal of the American Statistical Association · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Variational sparse Gaussian-process method for detecting spatially variable genes and cellular interactions in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- ISPAT-3D: Spatially Varying Conditional Volumetric Network Estimation for 3D Tumor Imaging.Research square · 2026Article
- Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE.bioRxiv : the preprint server for biology · 2026Article
- Spatial Gene Set Enrichment Analysis with Applications to Spatially Resolved Transcriptomic Data.bioRxiv : the preprint server for biology · 2026Article
- SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics.NAR genomics and bioinformatics · 2026Article
- Review
- BART-spatial unravels biologically significant transcriptional regulators from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- Logistic regression for estimating functional effects with spatial transcriptomics.Nucleic acids research · 2026Article
242 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
Identifying genes that display spatial expression patterns in spatially resolved transcriptomic studies is an important first step toward characterizing the spatial transcriptomic landscape of complex tissues. Here we present a statistical method, SPARK, for identifying spatial expression patterns of genes in data generated from various spatially resolved transcriptomic techniques. SPARK directly models spatial count data through generalized linear spatial models. It relies on recently developed statistical formulas for hypothesis testing, providing effective control of type I errors and yielding high statistical power. With a computationally efficient algorithm, which is based on penalized quasi-likelihood, SPARK is also scalable to datasets with tens of thousands of genes measured on tens of thousands of samples. Analyzing four published spatially resolved transcriptomic datasets using SPARK, we show it can be up to ten times more powerful than existing methods and disclose biological discoveries that otherwise cannot be revealed by existing approaches.
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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.