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Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

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Substituting missing data in compositional analysis.

Carlos Real¹, J Ángel Fernández, Jesús R Aboal

¹Área de Ecología, Departamento de Biología Celular y Ecología, Escuela Politécnica Superior, Universidad de Santiago de Compostela, 27002 Lugo, Spain. carlos.real@usc.es

Environmental Pollution (Barking, Essex : 1987)

|June 8, 2011

Summary

This study introduces a new method to assess data distortions caused by missing value imputation in environmental datasets. It helps decide on retaining samples or variables and evaluates imputation techniques using Mantel tests.

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Published on: January 7, 2019

Area of Science:

Environmental Science
Data Science
Statistical Analysis

Background:

Multivariate analysis of environmental data necessitates handling missing values.
Logarithmic transformation of data complicates traditional imputation methods, potentially creating outliers.
Existing imputation techniques may distort the dataset's inherent structure.

Purpose of the Study:

To propose a novel method for assessing distortions introduced by missing value imputation.
To provide a tool for deciding the retention of samples or variables with missing data.
To investigate the performance of various missing value substitution techniques.

Main Methods:

Utilizes Mantel tests to analyze the structure of distances among samples.
Applies the developed method to assess distortions in environmental datasets.
Evaluates the impact of different imputation strategies on data structure.

Main Results:

The proposed method effectively assesses distortions caused by missing value imputation.
It aids in making informed decisions regarding data preprocessing steps.
Demonstrates the application on real-world PCDD/F data from a biomonitoring study.

Conclusions:

The developed method is crucial for reliable multivariate analysis of environmental data with missing values.
It enhances the understanding and control of data distortions during imputation.
Facilitates more accurate biomonitoring studies by improving data integrity.