Literature DB >> 27248514

SMART: Statistical Metabolomics Analysis-An R Tool.

Yu-Jen Liang1,2, Yu-Ting Lin2, Chia-Wei Chen2, Chien-Wei Lin2, Kun-Mao Chao1,3, Wen-Harn Pan4, Hsin-Chou Yang2,5,6,7,8.   

Abstract

Metabolomics data provide unprecedented opportunities to decipher metabolic mechanisms by analyzing hundreds to thousands of metabolites. Data quality concerns and complex batch effects in metabolomics must be appropriately addressed through statistical analysis. This study developed an integrated analysis tool for metabolomics studies to streamline the complete analysis flow from initial data preprocessing to downstream association analysis. We developed Statistical Metabolomics Analysis-An R Tool (SMART), which can analyze input files with different formats, visually represent various types of data features, implement peak alignment and annotation, conduct quality control for samples and peaks, explore batch effects, and perform association analysis. A pharmacometabolomics study of antihypertensive medication was conducted and data were analyzed using SMART. Neuromedin N was identified as a metabolite significantly associated with angiotensin-converting-enzyme inhibitors in our metabolome-wide association analysis (p = 1.56 × 10(-4) in an analysis of covariance (ANCOVA) with an adjustment for unknown latent groups and p = 1.02 × 10(-4) in an ANCOVA with an adjustment for hidden substructures). This endogenous neuropeptide is highly related to neurotensin and neuromedin U, which are involved in blood pressure regulation and smooth muscle contraction. The SMART software, a user guide, and example data can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/metabolomics/SMART.htm .

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Year:  2016        PMID: 27248514     DOI: 10.1021/acs.analchem.6b00603

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  6 in total

1.  Data Processing and Analysis in Mass Spectrometry-Based Metabolomics.

Authors:  Ángela Peralbo-Molina; Pol Solà-Santos; Alexandre Perera-Lluna; Eduardo Chicano-Gálvez
Journal:  Methods Mol Biol       Date:  2023

2.  Visualization, Quantification, and Alignment of Spectral Drift in Population Scale Untargeted Metabolomics Data.

Authors:  Jeramie D Watrous; Mir Henglin; Brian Claggett; Kim A Lehmann; Martin G Larson; Susan Cheng; Mohit Jain
Journal:  Anal Chem       Date:  2017-01-26       Impact factor: 6.986

Review 3.  From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics.

Authors:  Leonardo Perez de Souza; Thomas Naake; Takayuki Tohge; Alisdair R Fernie
Journal:  Gigascience       Date:  2017-07-01       Impact factor: 6.524

Review 4.  Metabolomics: A Way Forward for Crop Improvement.

Authors:  Ali Razzaq; Bushra Sadia; Ali Raza; Muhammad Khalid Hameed; Fozia Saleem
Journal:  Metabolites       Date:  2019-12-14

5.  Identification of Serum Oxylipins Associated with the Development of Coronary Artery Disease: A Nested Case-Control Study.

Authors:  Kuang-Mao Chiang; Jia-Fu Chen; Chin-An Yang; Lili Xiu; Hsin-Chou Yang; Lie-Fen Shyur; Wen-Harn Pan
Journal:  Metabolites       Date:  2022-05-30

6.  A strategy for rapid discovery of traceable chemical markers in herbal products using MZmine 2 data processing toolbox: A case of Jing Liqueur.

Authors:  Jing Zhou; Feng-Jie Liu; Xin-Xin Li; Ping Li; Hua Yang; Yuan-Cai Liu; Yan-He Chen; Chao-Dan Wei; Hui-Jun Li
Journal:  Chin Herb Med       Date:  2021-05-28
  6 in total

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