ArticlePLoS computational biology2023
FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 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
- Detecting clinically relevant topological structures in multiplexed spatial proteomics using TopKAT.Patterns (New York, N.Y.) · 2026Article
- QeITH: Quantifies Tumor Ecosystem Heterogeneity to Predict Cancer Progression and Treatment Benefit.Computational and structural biotechnology journal · 2026Article
- Review
- A self-supervised learning approach for high throughput and high content cell segmentation.Communications biology · 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
- Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma.NPJ systems biology and applications · 2024Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- 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
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.
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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.