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Updated: Jun 12, 2025

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

Relational Persistent Homology for Multispecies Data with Application to the Tumor Microenvironment.

Bernadette J Stolz^1,2, Jagdeep Dhesi², Joshua A Bull²

¹Laboratory for Topology and Neuroscience, EPFL, Station 8, Lausanne, 1015, Switzerland.

Bulletin of Mathematical Biology

|September 17, 2024

Summary

This summary is machine-generated.

Topological data analysis (TDA) methods now quantify multispecies data relations. New Dowker and Witness complex methods reveal biological insights from complex spatial data, overcoming traditional persistent homology limits.

Keywords:

Dowker complex Multiplex imaging Persistent homology Spatial relations Witness complex

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Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

Area of Science:

Mathematics
Computational Biology
Data Science

Background:

Topological Data Analysis (TDA) and persistent homology (PH) traditionally analyze single-entity data.
Modern data collection yields complex multispecies datasets requiring relational analysis.
Applications span biomedical imaging, geospatial analysis, and species ecology.

Purpose of the Study:

Develop novel TDA methods for quantifying spatial relations in multispecies data.
Adapt Dowker complexes and Witness complexes for heterogeneous data analysis.
Apply methods to tumor microenvironment data to extract biological insights.

Main Methods:

Proposed two novel methods for encoding spatial relations among different data types.
Utilized Dowker complexes and Witness complexes as foundational frameworks.
Applied methods to synthetic multispecies tumor microenvironment data.

Main Results:

Identified topological features capturing inter-species cell type relations (e.g., tumor cells, immune cells).
Extracted significant biological insights, including dominant immune cell phenotype.
Demonstrated ability to infer parameter regimes of data-generating models.

Conclusions:

Developed quantitative methods for relational analysis of multispecies spatial data.
Overcame limitations of traditional persistent homology for complex datasets.
Methods are readily computable and offer new perspectives in TDA.