ArticleIEEE transactions on visualization and computer graphics2023
Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data.
Article in IEEE transactions on visualization and computer graphics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
The trial behind it
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
16 citing papers in PubMed, 22 citations in OpenAlex.
- hoodscanR: profiling single-cell neighborhoods in spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- SEAL: Spatially-resolved Embedding Analysis with Linked Imaging Data.IEEE transactions on visualization and computer graphics · 2026Article
- CytoSpatio: Learning cell type spatial relationships using multirange, multitype point process models.PLoS computational biology · 2025Article
- Article
- Advancing open-source visual analytics in digital pathology: A systematic review of tools, trends, and clinical applications.Journal of pathology informatics · 2025Review
- SEAL : Spatially-resolved Embedding Analysis with Linked Imaging Data.bioRxiv : the preprint server for biology · 2025Article
- A toolkit for mapping cell identities in relation to neighbors reveals conserved patterning of neuromesodermal progenitor populations.PLoS biology · 2025Article
- Spatial Patterning Analysis of Cellular Ensembles (SPACE) finds complex spatial organization at the cell and tissue levels.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cell2Cell: Explorative Cell Interaction Analysis in Multi-Volumetric Tissue Data.IEEE transactions on visualization and computer graphics · 2025Article
- Aardvark: Composite Visualizations of Trees, Time-Series, and Images.IEEE transactions on visualization and computer graphics · 2025Article
- Consensus tissue domain detection in spatial omics data using multiplex image labeling with regional morphology (MILWRM).Communications biology · 2024Article
- psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring.Computer graphics forum : journal of the European Association for Computer Graphics · 2024Article
- Spatial Patterning Analysis of Cellular Ensembles (SPACE) discovers complex spatial organization at the cell and tissue levels.bioRxiv : the preprint server for biology · 2024Article
- Spatial Multiplexing and Omics.Nature reviews. Methods primers · 2024Article
- DASS Good: Explainable Data Mining of Spatial Cohort Data.Computer graphics forum : journal of the European Association for Computer Graphics · 2023Article
- Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma.Cancer cell · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 1 institution in 1 country.
Funding
Abstract
New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.
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What OpenQuestion holds
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.