title: An unsupervised method for quantifying the behavior of paired animals.
abstract: Behaviors involving the interaction of multiple individuals are complex and frequently crucial for an animal's survival. These interactions, ranging across sensory modalities, length scales, and time scales, are often subtle and difficult to characterize. Contextual effects on the frequency of behaviors become even more difficult to quantify when physical interaction between animals interferes with conventional data analysis, e.g. due to visual occlusion. We introduce a method for quantifying behavior in fruit fly interaction that combines high-throughput video acquisition and tracking of individuals with recent unsupervised methods for capturing an animal's entire behavioral repertoire. We find behavioral differences between solitary flies and those paired with an individual of the opposite sex, identifying specific behaviors that are affected by social and spatial context. Our pipeline allows for a comprehensive description of the interaction between two individuals using unsupervised machine learning methods, and will be used to answer questions about the depth of complexity and variance in fruit fly courtship.
authors.affiliation: Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, United States of America.
authors.forename: Ugne
authors.initials: U
authors.lastname: Klibaite
authors.forename: Gordon J
authors.initials: GJ
authors.lastname: Berman
authors.forename: Jessica
authors.initials: J
authors.lastname: Cande
authors.forename: David L
authors.initials: DL
authors.lastname: Stern
authors.forename: Joshua W
authors.initials: JW
authors.lastname: Shaevitz
mesh_terms: D000818:Animals; D001522:Behavior, Animal; D004331:Drosophila melanogaster / Q000502:physiology*; D005260:Female; D000069550:Machine Learning; D008297:Male; D010152:Pair Bond; D012726:Sexual Behavior, Animal; D014741:Video Recording