ArticleBioinformatics advances2023
LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Spatial analysis of malignant-immune cell interactions in the tumor microenvironment using topological data analysis.NPJ systems biology and applications · 2026Article
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- 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
- Article
- mxfda: a comprehensive toolkit for functional data analysis of single-cell spatial data.Bioinformatics advances · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Recent advances in highly multiplexed imaging have provided unprecedented insights into the complex cellular organization of tissues, with many applications in translational medicine. However, downstream analyses of multiplexed imaging data face several technical limitations, and although some computational methods and bioinformatics tools are available, deciphering the complex spatial organization of cellular ecosystems remains a challenging problem. Results: To mitigate this problem, we develop a novel computational tool, LOCATOR (anaLysis Of CAncer Tissue micrOenviRonment), for spatial analysis of cancer tissue microenvironments using data acquired from mass cytometry imaging technologies. LOCATOR introduces a graph-based representation of tissue images to describe features of the cellular organization and deploys downstream analysis and visualization utilities that can be used for data-driven patient-risk stratification. Our case studies using mass cytometry imaging data from two well-annotated breast cancer cohorts re-confirmed that the spatial organization of the tumour-immune microenvironment is strongly associated with the clinical outcome in breast cancer. In addition, we report interesting potential associations between the spatial organization of macrophages and patients' survival. Our work introduces an automated and versatile analysis tool for mass cytometry imaging data with many applications in future cancer research projects. Availability and implementation: Datasets and codes of LOCATOR are publicly available at https://github.com/RezvanEhsani/LOCATOR.
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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.