ArticleJournal of proteome research2024
SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA.
Article in Journal of proteome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Multi-sample and multi-group spatial colocalization analysis using PANORAMIC.Bioinformatics (Oxford, England) · 2026Article
- Differences in T-cell Densities and Neighborhood Patterns in Human Colorectal Adenomas and Sessile Serrated Lesions.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- SHADE: A multilevel Bayesian framework for modeling directional spatial interactions in tissue microenvironments.PLoS computational biology · 2026Article
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- Detecting clinically relevant topological structures in multiplexed spatial proteomics using TopKAT.Patterns (New York, N.Y.) · 2026Article
- Diagnostic features of Acanthamoeba keratitis via in vivo confocal microscopy.Scientific reports · 2025Observational
- Statistical analysis of multiple regions-of-interest in multiplexed spatial proteomics data.Briefings in bioinformatics · 2024Article
- A Spatial Omnibus Test (SPOT) for Spatial Proteomic Data.Bioinformatics (Oxford, England) · 2024Article
- A Spatial Omnibus Test (SPOT) for Spatial Proteomic Data.bioRxiv : the preprint server for biology · 2024Article
- mxfda: a comprehensive toolkit for functional data analysis of single-cell spatial data.Bioinformatics advances · 2024Article
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10 authors.
Funding
Abstract
Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.
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Registered trials
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.