WormTensor: a clustering method for time-series whole-brain activity data from C. elegans
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Published:2023-06-16
Issue:1
Volume:24
Page:
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ISSN:1471-2105
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Container-title:BMC Bioinformatics
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language:en
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Short-container-title:BMC Bioinformatics
Author:
Tsuyuzaki Koki,Yamamoto Kentaro,Toyoshima Yu,Sato Hirofumi,Kanamori Manami,Teramoto Takayuki,Ishihara Takeshi,Iino Yuichi,Nikaido Itoshi
Abstract
Abstract
Background
In the field of neuroscience, neural modules and circuits that control biological functions have been found throughout entire neural networks. Correlations in neural activity can be used to identify such neural modules. Recent technological advances enable us to measure whole-brain neural activity with single-cell resolution in several species including $$Caenorhabditis\ elegans$$
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. Because current neural activity data in C. elegans contain many missing data points, it is necessary to merge results from as many animals as possible to obtain more reliable functional modules.
Results
In this work, we developed a new time-series clustering method, , to identify functional modules using whole-brain activity data from C. elegans. uses a distance measure, modified shape-based distance to account for the lags and the mutual inhibition of cell–cell interactions and applies the tensor decomposition algorithm multi-view clustering based on matrix integration using the higher orthogonal iteration of tensors (HOOI) algorithm (), which can estimate both the weight to account for the reliability of data from each animal and the clusters that are common across animals.
Conclusion
We applied the method to 24 individual C. elegans and successfully found some known functional modules. Compared with a widely used consensus clustering method to aggregate multiple clustering results, showed higher silhouette coefficients. Our simulation also showed that is robust to contamination from noisy data. is freely available as an /CRAN package https://cran.r-project.org/web/packages/WormTensor.
Funder
Japan Science and Technology Corporation
The special postdoctoral researcher program at RIKEN
Japan Society for the Promotion of Science
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
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