Normalizing and Correcting Variable and Complex LC-MS Metabolomic Data with the R Package pseudoDrift.

Area of Science:

• Biochemistry
• Plant Science
• Bioinformatics

Background:

• Liquid chromatography-mass spectroscopy (LC-MS) is crucial for metabolomic data generation in biological research.
• Technical errors, particularly signal drift and batch effects, frequently occur in LC-MS data, hindering biological insights.
• Large-scale studies are especially susceptible to these technical challenges.

Purpose of the Study:

• To introduce pseudoDrift, an R package designed for simulating data and detecting outliers.
• To present a novel training and testing approach for capturing and correcting technical errors in LC-MS metabolomic data.
• To offer researchers a flexible and effective tool for managing technical variability in their studies.

Main Methods:

• Development of the pseudoDrift R package for data simulation and outlier detection.
• Implementation of a new training and testing methodology to identify and optionally correct technical errors.
• Generation of a targeted LC-MS dataset from maize seedling stems.

Main Results:

• The developed approach demonstrates comparable performance to existing methods through data simulation.
• The pseudoDrift package offers enhanced flexibility for researchers analyzing LC-MS metabolomic data.
• A unique dataset of 33 phenolic compounds from 602 maize inbred lines was generated.

Conclusions:

• The pseudoDrift R package and associated methodology effectively address technical errors in LC-MS metabolomic data.
• This approach provides a valuable tool for improving the quality and reliability of metabolomic studies.
• The generated maize dataset facilitates investigations into plant specialized metabolism dynamics.