ReviewFrontiers in physiology2022
Spatial Statistics for Understanding Tissue Organization.
Review in Frontiers in physiology, 2022. 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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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- Morphomechanically-Informed Spatial Curvature Sequencing in Prostate Cancer.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence, machine learning-based automated fibrosis quantification in preclinical models of pulmonary fibrosis.Respiratory research · 2026Article
- Spider: a flexible and unified framework for simulating spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- From cells to pixels: A decision tree for designing bioimage analysis pipelines.Journal of microscopy · 2025Article
- CytoSpatio: Learning cell type spatial relationships using multirange, multitype point process models.PLoS computational biology · 2025Article
- Topological data analysis of pattern formation of human induced pluripotent stem cell colonies.Scientific reports · 2025Article
- SpatialOne: end-to-end analysis of visium data at scale.Bioinformatics (Oxford, England) · 2024Article
- Spatial tumor immune microenvironment phenotypes in ovarian cancer.NPJ precision oncology · 2024Article
- Redefining clinical practice through spatial profiling: a revolution in tissue analysis.Annals of the Royal College of Surgeons of England · 2024Article
- Visualization and quality control tools for large-scale multiplex tissue analysis in TissUUmaps3.Biological imaging · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Interpreting tissue architecture plays an important role in gaining a better understanding of healthy tissue development and disease. Novel molecular detection and imaging techniques make it possible to locate many different types of objects, such as cells and/or mRNAs, and map their location across the tissue space. In this review, we present several methods that provide quantification and statistical verification of observed patterns in the tissue architecture. We categorize these methods into three main groups: Spatial statistics on a single type of object, two types of objects, and multiple types of objects. We discuss the methods in relation to four hypotheses regarding the methods' capability to distinguish random and non-random distributions of objects across a tissue sample, and present a number of openly available tools where these methods are provided. We also discuss other spatial statistics methods compatible with other types of input data.
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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.