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metabolomicsR: a streamlined workflow to analyze metabolomic data in R

SUMMARY: metabolomicsR is a streamlined, flexible and user-friendly R package to preprocess, analyze and visualize metabolomic data. metabolomicsR includes comprehensive functionalities for sample and metabolite quality control, outlier detection, missing value imputation, dimensional reduction, bat...

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Detalles Bibliográficos
Autores principales: Han, Xikun, Liang, Liming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512519/
https://www.ncbi.nlm.nih.gov/pubmed/36177485
http://dx.doi.org/10.1093/bioadv/vbac067
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author Han, Xikun
Liang, Liming
author_facet Han, Xikun
Liang, Liming
author_sort Han, Xikun
collection PubMed
description SUMMARY: metabolomicsR is a streamlined, flexible and user-friendly R package to preprocess, analyze and visualize metabolomic data. metabolomicsR includes comprehensive functionalities for sample and metabolite quality control, outlier detection, missing value imputation, dimensional reduction, batch effect normalization, data integration, regression, metabolite annotation and visualization of data and results. In this application note, we demonstrate the step-by-step use of the main functions from this package. AVAILABILITY AND IMPLEMENTATION: The metabolomicsR package is available via CRAN and GitHub (https://github.com/XikunHan/metabolomicsR/). A step-by-step online tutorial is available at https://xikunhan.github.io/metabolomicsR/docs/articles/Introduction.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.
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spelling pubmed-95125192022-09-27 metabolomicsR: a streamlined workflow to analyze metabolomic data in R Han, Xikun Liang, Liming Bioinform Adv Application Note SUMMARY: metabolomicsR is a streamlined, flexible and user-friendly R package to preprocess, analyze and visualize metabolomic data. metabolomicsR includes comprehensive functionalities for sample and metabolite quality control, outlier detection, missing value imputation, dimensional reduction, batch effect normalization, data integration, regression, metabolite annotation and visualization of data and results. In this application note, we demonstrate the step-by-step use of the main functions from this package. AVAILABILITY AND IMPLEMENTATION: The metabolomicsR package is available via CRAN and GitHub (https://github.com/XikunHan/metabolomicsR/). A step-by-step online tutorial is available at https://xikunhan.github.io/metabolomicsR/docs/articles/Introduction.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online. Oxford University Press 2022-09-16 /pmc/articles/PMC9512519/ /pubmed/36177485 http://dx.doi.org/10.1093/bioadv/vbac067 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Application Note
Han, Xikun
Liang, Liming
metabolomicsR: a streamlined workflow to analyze metabolomic data in R
title metabolomicsR: a streamlined workflow to analyze metabolomic data in R
title_full metabolomicsR: a streamlined workflow to analyze metabolomic data in R
title_fullStr metabolomicsR: a streamlined workflow to analyze metabolomic data in R
title_full_unstemmed metabolomicsR: a streamlined workflow to analyze metabolomic data in R
title_short metabolomicsR: a streamlined workflow to analyze metabolomic data in R
title_sort metabolomicsr: a streamlined workflow to analyze metabolomic data in r
topic Application Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512519/
https://www.ncbi.nlm.nih.gov/pubmed/36177485
http://dx.doi.org/10.1093/bioadv/vbac067
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