What question did this study set out to answer?

The research aims to develop and evaluate a user-friendly tool, ClusterApp, for visualizing and analyzing metabolomics data through clustering methods.

July 31, 2026PLoS Computational BiologyOpen Access

ClusterApp to visualize, organize, and navigate metabolomics data

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Authors

VCVinicius Hansel Figueiredo da CostaUniversidade de São Paulo PKPothuvilage KarunarathneMichigan United TBTiago Cabral BorelliUniversidade de Ribeirão Preto

Discussion

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Implication

This randomized trial demonstrates ClusterApp's effectiveness in visualizing metabolomics data, indicating improved data analysis accessibility.

Key Points

The research aims to develop and evaluate a user-friendly tool, ClusterApp, for visualizing and analyzing metabolomics data through clustering methods.
Developed a web application using QIIME 2 Docker image for Principal Coordinate Analysis (PCoA).
Analyzed two datasets from Liquid Chromatography coupled with Tandem Mass Spectrometry (LC-MS/MS).
Utilized dissimilarity measures and data preprocessing techniques, including blank removal and normalization.
Successfully identified key metabolomic variations using the Bray-Curtis dissimilarity measure.
Enhanced data reliability with targeted filtering, retaining 10,617 features in coral and 7,341 features in mouse datasets.
Improved clustering resolution with Total Ion Current (TIC) normalization, revealing distinct separations in tissue types and coral life stages.

Cite This Study

Costa et al. (2026) studied this question.

synapsesocial.com/papers/6a6c4761747664a1aa73ca89 https://doi.org/10.1371/journal.pcbi.1014586

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July 31, 2026PLoS Computational BiologyOpen Access

ClusterApp to visualize, organize, and navigate metabolomics data

View Full Paper

Ask AI

Bookmark

Authors

VCVinicius Hansel Figueiredo da CostaUniversidade de São Paulo PKPothuvilage KarunarathneMichigan United TBTiago Cabral BorelliUniversidade de Ribeirão Preto

Discussion

Member takes

Implication

This randomized trial demonstrates ClusterApp's effectiveness in visualizing metabolomics data, indicating improved data analysis accessibility.

Key Points

The research aims to develop and evaluate a user-friendly tool, ClusterApp, for visualizing and analyzing metabolomics data through clustering methods.
Developed a web application using QIIME 2 Docker image for Principal Coordinate Analysis (PCoA).
Analyzed two datasets from Liquid Chromatography coupled with Tandem Mass Spectrometry (LC-MS/MS).
Utilized dissimilarity measures and data preprocessing techniques, including blank removal and normalization.
Successfully identified key metabolomic variations using the Bray-Curtis dissimilarity measure.
Enhanced data reliability with targeted filtering, retaining 10,617 features in coral and 7,341 features in mouse datasets.
Improved clustering resolution with Total Ion Current (TIC) normalization, revealing distinct separations in tissue types and coral life stages.

Cite This Study

Costa et al. (2026) studied this question.

synapsesocial.com/papers/6a6c4761747664a1aa73ca89 https://doi.org/10.1371/journal.pcbi.1014586

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