ArticleBioinformatics advances2022
DenVar: density-based variation analysis of multiplex imaging data.
Article in Bioinformatics advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Topological Data Analysis of Spatial Protein Expression in Multiplexed Spatial Proteomics Studies.bioRxiv : the preprint server for biology · 2026Article
- Spatial Distribution of Straw-Colored Fruit Bats (Ecology and evolution · 2025Article
- cytoKernel: robust kernel embeddings for assessing differential expression of single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Functional protein biomarkers based on distributions of expression levels in single-cell imaging data.Bioinformatics (Oxford, England) · 2025Article
- Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments.PLoS computational biology · 2024Article
- DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with distance matrices.Patterns (New York, N.Y.) · 2023Article
- FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures.PLoS computational biology · 2023Article
- Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis.Bioinformatics (Oxford, England) · 2023Article
- LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies.Bioinformatics advances · 2023Article
- SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data.Frontiers in genetics · 2023Article
- MIAMI: mutual information-based analysis of multiplex imaging data.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Summary: Multiplex imaging platforms have become popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions becomes extremely crucial for subject-specific assessment of risks. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular protein, and the other to classify the subjects based on the proportion of the positive cells. We present a threshold-free approach in which distance between a pair of subjects is computed based on the probability density of the protein in their TMEs. The distance matrix can either be used to classify the subjects into meaningful groups or can directly be used in a kernel machine regression framework for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling easier but interpretable analysis. We analyze a lung cancer dataset, finding the difference in the density of protein HLA-DR to be significantly associated with the overall survival and a triple-negative breast cancer dataset, analyzing the effects of multiple proteins on survival and recurrence. The reliability of our method is demonstrated through extensive simulation studies. Availability and implementation: The associated Supplementary information: Supplementary data are available at
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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.