PyRAT: An Open-Source Python Library for Animal Behavior Analysis

Abstract

Here we developed an open-source Python-based library called Python rodent Analysis and Tracking (PyRAT). Our library analyzes tracking data to classify distinct behaviors, estimate traveled distance, speed and area occupancy. To classify and cluster behaviors, we used two unsupervised algorithms: hierarchical agglomerative clustering and t-distributed stochastic neighbor embedding (t-SNE). Finally, we built algorithms that associate the detected behaviors with synchronized neural data and facilitate the visualization of this association in the pixel space. PyRAT is fully available on GitHub: https://github.com/pyratlib/pyrat.

A modular, cost-effective, versatile, open-source operant box solution for long-term miniscope imaging, 3D tracking, and deep learning behavioral analysis

“…This can be done with models such as DeepLabCut, which tracks location and posture across video frames [47] . Pose estimation can be further analyzed with behavior classification algorithms such as PyRAT, HubDT, and deep behavior mapping (DBM) [6 , [48] , [49] , [50] . DBM is used to capture behavioral microstates on a moment-to-moment basis [6] .…”

Using deep learning to study emotional behavior in rodent models

“…Unsupervised methods generally involve a dimensionality reduction step, where a large set of input features are compressed into a low-dimensional representation, and a clustering step, where data points (i.e., frames in a video sequence) are clustered to maximize the similarity of points within a cluster, which in some cases requires the experimenter to specify the number of clusters. B-SOiD (Hsu and Yttri, 2021 ) and PyRAT (De Almeida et al, 2022 ) apply nonlinear dimensionality reduction, which can capture nonlinear relationships in the input data (Portnova-Fahreeva et al, 2020 ), while AlphaTracker (Chen et al, 2020 ) uses linear dimensionality reduction (principal component analysis, PCA). All three methods then group video frames into behavioral categories through hierarchical clustering.…”