Topological data analysis of C. elegans locomotion and behavior

TL;DR

This paper applies topological data analysis to C. elegans behavior, enabling quantitative summaries, classification of environmental conditions, and visualization of behaviors through persistent homology.

Contribution

It introduces a novel topological approach for analyzing and visualizing complex biological behavior data, enhancing interpretability and classification accuracy.

Findings

Effective classification of behaviors under different conditions

Visualization of stereotypical behaviors via synthetic videos

Trade-off analysis between accuracy and interpretability

Abstract

We apply topological data analysis to the behavior of C. elegans, a widely-studied model organism in biology. In particular, we use topology to produce a quantitative summary of complex behavior which may be applied to high-throughput data. Our methods allow us to distinguish and classify videos from various environmental conditions and we analyze the trade-off between accuracy and interpretability. Furthermore, we present a novel technique for visualizing the outputs of our analysis in terms of the input. Specifically, we use representative cycles of persistent homology to produce synthetic videos of stereotypical behaviors.