ArticlePatterns (New York, N.Y.)2023
DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with distance matrices.
Article in Patterns (New York, N.Y.), 2023. 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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10 citing papers in PubMed, 15 citations in OpenAlex.
- Topological Data Analysis of Spatial Protein Expression in Multiplexed Spatial Proteomics Studies.bioRxiv : the preprint server for biology · 2026Article
- Metabolic characterization of tumor-immune interactions by multiplexed immunofluorescence reveals spatial mechanisms of immunotherapy response in non-small cell lung carcinoma (NSCLC).Nature communications · 2026Article
- smoppix: unified nonparametric analysis of single-molecule spatial omics data using probabilistic indices.Genome biology · 2026Article
- Kontextual reframes analysis of spatial omics data and reveals consistent cell relationships across images.Cell reports methods · 2025Article
- Quantile index predictors using R package hyper.gam.Bioinformatics (Oxford, England) · 2025Article
- Functional protein biomarkers based on distributions of expression levels in single-cell imaging data.Bioinformatics (Oxford, England) · 2025Article
- Benchmarking Spatial Co-Localization Methods for Single-Cell Multiplex Imaging Data with Applications to High-Grade Serous Ovarian and Triple Negative Breast Cancer.Statistics and data science in imaging · 2025Article
- 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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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
A major challenge in the spatial analysis of multiplex imaging (MI) data is choosing how to measure cellular spatial interactions and how to relate them to patient outcomes. Existing methods to quantify cell-cell interactions do not scale to the rapidly evolving technical landscape, where both the number of unique cell types and the number of images in a dataset may be large. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. By applying DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.
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