maplet: an extensible R toolbox for modular and reproducible metabolomics pipelines.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
27 01 2022
Historique:
received: 09 06 2021
revised: 24 09 2021
accepted: 22 10 2021
pubmed: 26 10 2021
medline: 3 2 2023
entrez: 25 10 2021
Statut: ppublish

Résumé

This article presents maplet, an open-source R package for the creation of highly customizable, fully reproducible statistical pipelines for metabolomics data analysis. It builds on the SummarizedExperiment data structure to create a centralized pipeline framework for storing data, analysis steps, results and visualizations. maplet's key design feature is its modularity, which offers several advantages, such as ensuring code quality through the maintenance of individual functions and promoting collaborative development by removing technical barriers to code contribution. With over 90 functions, the package includes a wide range of functionalities, covering many widely used statistical approaches and data visualization techniques. The maplet package is implemented in R and freely available at https://github.com/krumsieklab/maplet.

Identifiants

pubmed: 34694386
pii: 6409851
doi: 10.1093/bioinformatics/btab741
pmc: PMC8796365
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1168-1170

Subventions

Organisme : NIA NIH HHS
ID : R01 AG069901
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG058942
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG059093
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG061359
Pays : United States
Organisme : Biomedical Research Program' funds at Weill Cornell Medical College in Qatar
Organisme : Qatar Foundation and multiple grants from the Qatar National Research Fund (QNRF)
Organisme : NIA NIH HHS
ID : U19 AG063744
Pays : United States

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press.

Auteurs

Kelsey Chetnik (K)

Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.

Elisa Benedetti (E)

Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.

Daniel P Gomari (DP)

Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.

Annalise Schweickart (A)

Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.

Richa Batra (R)

Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.

Mustafa Buyukozkan (M)

Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.

Zeyu Wang (Z)

Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.

Matthias Arnold (M)

Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.

Jonas Zierer (J)

Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.

Karsten Suhre (K)

Department of Physiology and Biophysics, Weill Cornell Medical College-Qatar Education City, Doha, Qatar.

Jan Krumsiek (J)

Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software
Cephalometry Humans Anatomic Landmarks Software Internet
Humans Algorithms Software Artificial Intelligence Computer Simulation

Classifications MeSH