Fish 3D Clustering App (F3DCA): A Non-Supervised Clustering Method with a Machine Learning Autoencoder for Analyzing...

Fish behaviors are widely studied in various research fields due to their relevance to many aspects, including neurodegenerative processes, since they can impair motor control, complex behavior, habituation, and response to environmental stimuli. 3D locomotion trajectory analysis is one of the assays to analyze complex behaviors in fish. However, while existing 3D tracking software can capture the 3D trajectories of fish, they still rely heavily on manual or semi-automated analysis. Moreover, traditional analysis methods are inadequate for handling the high complexity of raw 3D data (X, Y, Z coordinates), and manual/semi-automated procedures introduce subjective human bias and are highly time-consuming, highlighting the need for a fully automated, unsupervised machine learning method. Here, we aimed to develop an unsupervised clustering method with machine learning to automatically analyze fish behaviors by using fish 3D coordinates. To evaluate the performance of this method, five different datasets, including (1) interspecies fish that consisted of three fish species, which were Danio rerio, Oryzias woworae, and Kryptopterus vitreolus, (2) shoal sex composition, (3) caudal fin amputation effect, (4) habituation time effect, and (5) acute ethanol exposure effect on zebrafish behaviors, were analyzed. The current method utilizes multiple clustering algorithms, including K-Means, BIRCH, Gaussian Mixture Model, and Spectral Clustering, which were then applied to the 3D trajectory data in every dataset. Afterward, the clustering performance was evaluated using the Silhouette Score and Davies-Bouldin Index, followed by the use of deep-learning autoencoders to improve latent feature representation and enhance cluster separation. The clustering outcomes were further compared with predefined experimental groups using ARI and NMI and were qualitatively interpreted alongside conventional behavioral endpoints. External agreement varied among datasets and clustering algorithms. The proposed method can characterize differences in zebrafish 3D locomotor trajectory patterns across experimental conditions, while its separation power may decrease when behavioral differences are subtle or highly overlapping. Furthermore, the present study evaluated the method using multiple behavioral datasets; a similar framework can be extended to zebrafish models of neurodegenerative disease to identify disease-associated alterations in movement patterns and neurobehavioral organization. Finally, a user-friendly software package with easy installation and readable trajectory-analysis outputs was also developed that can be useful in biomedical research.