Persistent Homology: A Pedagogical Introduction with Biological Applications

TL;DR

This paper provides an accessible introduction to persistent homology, explaining its core concepts and demonstrating its application to biological data, thereby bridging theory and practice for newcomers.

Contribution

It offers a clear, comprehensive pedagogical resource on persistent homology with practical biological examples, filling a gap for beginners in the field.

Findings

Demonstrated persistent homology on a 3-1 supercoiled DNA structure

Showcased applications in biological and financial data analysis

Facilitated understanding of topological data analysis for newcomers

Abstract

Persistent Homology (PH) is a fundamental tool in computational topology, designed to uncover the intrinsic geometric and topological features of data across multiple scales. Originating within the broader framework of Topological Data Analysis (TDA), PH has found diverse applications ranging from protein structure and knot analysis to financial domains such as Bitcoin behaviour and stock market dynamics. Despite its growing relevance, there remains a lack of accessible resources that bridge the gap between theoretical foundations and practical implementation for beginners. This paper offers a clear and comprehensive introduction to persistent homology, guiding readers from core concepts to real-world applications. Specifically, we illustrate the methodology through the analysis of a 3-1 supercoiled DNA structure. The paper is tailored for readers without prior exposure to algebraic topology, aiming to demystify persistent homology and foster its broader adoption in data analysis tasks.