TY - JOUR
T1 - Functional clustering of mouse ultrasonic vocalization data
AU - Dou, Xiaoling
AU - Shirahata, Shingo
AU - Sugimoto, Hiroki
N1 - Funding Information:
This work was supported by Transdisciplinary Research Integration Center, Research Organization of Information and Systems, Japan (Dr. Xiaoling Dou) (http://www. rois.ac.jp). The authors are grateful to the Editor, Academic Editor and the two reviewers for their helpful comments which improve the presentation of the paper. The authors also express their sincere thanks to Professor Tsuyoshi Koide of National Institute of Genetics for his help with the experiments and valuable advice on the paper.
Publisher Copyright:
© 2018 Dou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Mouse ultrasonic vocalizations (USVs) are studied in many fields of science. However, various noise and varied USV patterns in observed signals make complete automatic analysis difficult. We improve several methods to reduce noise, detect USV calls and automatically cluster USV calls. After reduction of noise and detection of USV calls, we consider USV calls as functional data and characterize them as USV functions with B-spline basis functions. For discontinuous USV calls, breakpoints in the USV functions are defined using multiple knots in the construction of the B-spline basis functions, and a hierarchical method is used to cluster the USV functions by shape. We finally show the performance of the proposed methods with USV data recorded for laboratory mice.
AB - Mouse ultrasonic vocalizations (USVs) are studied in many fields of science. However, various noise and varied USV patterns in observed signals make complete automatic analysis difficult. We improve several methods to reduce noise, detect USV calls and automatically cluster USV calls. After reduction of noise and detection of USV calls, we consider USV calls as functional data and characterize them as USV functions with B-spline basis functions. For discontinuous USV calls, breakpoints in the USV functions are defined using multiple knots in the construction of the B-spline basis functions, and a hierarchical method is used to cluster the USV functions by shape. We finally show the performance of the proposed methods with USV data recorded for laboratory mice.
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U2 - 10.1371/journal.pone.0196834
DO - 10.1371/journal.pone.0196834
M3 - Article
C2 - 29742174
AN - SCOPUS:85051003431
SN - 1932-6203
VL - 13
JO - PloS one
JF - PloS one
IS - 5
M1 - e0196834
ER -