MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery

Area of Science:

• Metabolomics
• Bioinformatics
• Data Science

Background:

• Mass spectrometry-based metabolomics is crucial for profiling metabolic changes and identifying differential metabolites.
• Metabolomics data often suffers from high dimensionality, missing values, nonlinearity, and non-normality, complicating analysis.
• Effective data cleaning, including batch alignment, imputation, normalization, and scaling, is essential before differential metabolite discovery.

Purpose of the Study:

• To develop an integrated and comprehensive workflow for metabolomics data cleaning and differential metabolite discovery.
• To provide an interactive web server, MetFlow, to facilitate these processes.

Main Methods:

• Development of an interactive web server named MetFlow.
• Integration of various data cleaning techniques: batch alignment, missing value imputation, data normalization, and scaling.
• Incorporation of differential metabolite discovery algorithms.

Main Results:

• MetFlow offers a user-friendly platform for robust metabolomics data post-processing.
• The server addresses common confounding factors in metabolomics data acquisition.
• Enables streamlined identification of differential metabolites for biological insights.

Conclusions:

• MetFlow provides a valuable tool for the metabolomics research community.
• The integrated workflow simplifies complex data cleaning and analysis pipelines.
• Facilitates more accurate and reliable differential metabolite discovery.