metabolomicsR: a streamlined workflow to analyze metabolomic data in R

Area of Science:

• * Computational Biology
• * Bioinformatics
• * Data Science

Background:

• * Metabolomic data analysis involves complex preprocessing and visualization steps.
• * Existing tools may lack comprehensive functionalities or user-friendliness.
• * Streamlined R packages are needed to facilitate metabolomic research.

Purpose of the Study:

• * To introduce metabolomicsR, a novel R package for metabolomic data analysis.
• * To demonstrate the comprehensive functionalities of metabolomicsR for preprocessing, analysis, and visualization.
• * To provide a user-friendly tool for researchers working with metabolomic datasets.

Main Methods:

• * Development of an R package named metabolomicsR.
• * Implementation of functions for quality control, outlier detection, and missing value imputation.
• * Integration of methods for dimensional reduction, batch effect normalization, and data integration.
• * Inclusion of regression, metabolite annotation, and visualization tools.

Main Results:

• * metabolomicsR provides a unified platform for the entire metabolomic data analysis pipeline.
• * The package includes robust methods for data preprocessing and quality assurance.
• * Comprehensive visualization tools aid in the interpretation of metabolomic data and results.

Conclusions:

• * metabolomicsR offers a streamlined, flexible, and user-friendly solution for metabolomic data analysis.
• * The package empowers researchers to efficiently preprocess, analyze, and visualize their metabolomic data.
• * metabolomicsR facilitates deeper insights into biological systems through advanced metabolomic analysis.