Uncovering Symbolic Convergence in Human Sperm Motility: A Data-Driven Analysis of Monotonic Trajectory Clusters

BackgroundUnderstanding heterogeneity in sperm motility requires tools capable of capturing both dynamic patterns and interpretable structure. Symbolic encoding methods offer a novel way to represent motion trajectories through discrete motifs. ObjectiveThis study explores whether symbolic time-series representations can uncover latent structure in sperm motility data, focusing on patterns of symbolic monotony and entropy. MethodsWe applied Symbolic Aggregate approXimation (SAX) to 1176 sperm trajectories and computed motif entropy and dominance across multiple parameterizations (a = 3-7, k = 2-4). Trajectories were embedded using UMAP and evaluated for symbolic convergence. ResultsA compact trajectory cluster (82/1176, 7%) consistently emerged across SAX configurations, characterized by very low entropy (median: 0.00) and high motif dominance (median: 0.97), with >85% of motifs consisting of the "DDD" triplet. The cluster exhibited a markedly negative VSL slope (median: -0.00021), in contrast to non-clustered trajectories (median: +0.00002). No external labels were available to determine functional significance. ConclusionsSymbolic encoding revealed a highly consistent pattern of motion monotony. While these findings may reflect constrained or declining motility, their biological interpretation remains uncertain. Symbolic representations may serve as useful hypothesis-generating tools in the discovery of emergent sperm motility phenotypes.