Year: 2025
Type: article
Abstract: Topological data analysis (TDA) uses persistent homology to quantify loops and higher-dimensional holes in data, making it particularly relevant for examining the characteristics of images of cells in the field of cell biology. In the context of a cell injury, as time progresses, a wound in the form of a ring emerges in the cell image and then gradually vanishes. Performing statistical inference on this ring-like pattern in a single image is challenging due to the absence of repeated samples. In this paper, we develop a novel framework leveraging TDA to estimate underlying structures within an individual image and quantify associated uncertainties through confidence regions. Our proposed method partitions the image into the background and the damaged cell regions. Then, pixels within the affected cell region are used to establish confidence regions in the space of persistence diagrams (topological summary statistics). The proposed method establishes an estimate of a persistence diagram for an image that mitigates the bias of a traditional persistence diagram computation. A simulation study is conducted to evaluate the coverage probabilities of the proposed confidence regions in comparison to an alternative approach that is proposed in this paper. We also illustrate our methodology by a real-world example provided by biological cell repair.
Source: Foundations of Data Science
DOI: 10.3934/fods.2025017
Authors: Susan Glenn, Jessi Cisewski-Kehe, Jun Zhu, William M Bement
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413369476