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Related Experiment Video

Updated: Jul 26, 2025

Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans
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WormTensor: a clustering method for time-series whole-brain activity data from C. elegans.

Koki Tsuyuzaki1, Kentaro Yamamoto2, Yu Toyoshima3

  • 1Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, Wako, Saitama, 351-0198, Japan. koki.tsuyuzaki@gmail.com.

BMC Bioinformatics
|June 16, 2023
PubMed
Summary
This summary is machine-generated.

We developed WormTensor, a new method to identify neural functional modules in C. elegans whole-brain activity data. This approach effectively merges data from multiple animals, improving reliability and accuracy in neuroscience research.

Keywords:
C. elegansCalcium imagingConsensus clusteringFunctional modulesNaCl stimuliNeural activityTensor decompositionWeighting

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Systems Neuroscience

Background:

  • Neural modules and circuits govern biological functions within neural networks.
  • Neural activity correlations aid in identifying these functional modules.
  • Advances allow whole-brain neural activity measurement in species like C. elegans, but data often has missing points.

Purpose of the Study:

  • To develop a novel time-series clustering method for identifying functional modules in C. elegans.
  • To address challenges of missing data in whole-brain neural activity datasets.
  • To enable merging of data from multiple animals for more reliable module discovery.

Main Methods:

  • Introduced WormTensor, a new time-series clustering method.
  • Utilized a modified shape-based distance to handle lags and mutual inhibition in cell interactions.
  • Applied a tensor decomposition algorithm (MC-MI-HOOI) for multi-view clustering and data reliability weighting.

Main Results:

  • Successfully identified known functional modules in C. elegans using whole-brain activity data from 24 individuals.
  • WormTensor demonstrated superior performance compared to a standard consensus clustering method, evidenced by higher silhouette coefficients.
  • Simulations confirmed WormTensor's robustness against noisy data contamination.

Conclusions:

  • WormTensor is an effective tool for discovering neural functional modules in C. elegans.
  • The method provides a reliable way to integrate data from multiple animals, overcoming common data limitations.
  • WormTensor is available as an open-source R/CRAN package, facilitating its use in the research community.