Evidence map›Paper›PMID 37328814›Full record

ArticleBMC bioinformatics2023

WormTensor: a clustering method for time-series whole-brain activity data from C. elegans.

Koki Tsuyuzaki, Kentaro Yamamoto, Yu Toyoshima, Hirofumi Sato, Manami Kanamori, Takayuki Teramoto, Takeshi Ishihara, Yuichi Iino, Itoshi Nikaido

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 47% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Koki TsuyuzakiLaboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, Wako, Saitama, 351-0198, Japan. koki.tsuyuzaki@gmail.com.
Kentaro YamamotoLaboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, Wako, Saitama, 351-0198, Japan.
Yu ToyoshimaDepartment of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Hirofumi SatoDepartment of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Manami KanamoriDepartment of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Takayuki TeramotoDepartment of Biology, Faculty of Sciences, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.
Takeshi IshiharaDepartment of Biology, Faculty of Sciences, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.
Yuichi IinoDepartment of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Itoshi NikaidoLaboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, Wako, Saitama, 351-0198, Japan. itoshi.nikaido@riken.jp.
The University of Tokyo · JPKyushu University · JPRIKEN Center for Biosystems Dynamics Research · JP

Funding

Japan Science and Technology Corporation JPMJPR1945Japan Society for the Promotion of Science 19K20406
6 · The paper itself

Abstract

backgroundIn 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 [Formula: see text]. 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.

resultsIn this work, we developed a new time-series clustering method, WormTensor, to identify functional modules using whole-brain activity data from C. elegans. WormTensor 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 (MC-MI-HOOI), which can estimate both the weight to account for the reliability of data from each animal and the clusters that are common across animals.

conclusionWe 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, WormTensor showed higher silhouette coefficients. Our simulation also showed that WormTensor is robust to contamination from noisy data. WormTensor is freely available as an R/CRAN package https://cran.r-project.org/web/packages/WormTensor .

Indexed as

BrainCaenorhabditis elegansAlgorithmsAnimalsCluster AnalysisReproducibility of ResultsCalcium imagingC. elegansConsensus clusteringFunctional modulesNaCl stimuliNeural activityTensor decompositionWeighting

Identifiers

PMID37328814
PMCPMC10273573
OpenAlexW4380988512

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.