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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

Dealing with missing values in large-scale studies: microarray data imputation and beyond.

Tero Aittokallio¹

¹Biomathematics Research Group, Department of Mathematics, FI-20014 University of Turku, Finland. tero.aittokallio@utu.fi

Briefings in Bioinformatics

|December 8, 2009

Summary

This summary is machine-generated.

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Missing values in high-throughput data hinder analysis. This review details imputation strategies and evaluation methods, offering guidance for choosing tools and suggesting future research directions for missing data imputation.

Area of Science:

Biotechnology
Bioinformatics
Genomics
Proteomics

Background:

High-throughput biotechnologies like gene expression microarrays and proteomic assays frequently generate missing values due to experimental variability.
These missing data points can significantly impede downstream data analyses, necessitating robust handling strategies.
Missing value imputation has become a standard preprocessing step in large-scale biological data analysis.

Purpose of the Study:

To systematically review current missing value imputation strategies for high-throughput biological data.
To describe performance evaluation measures for imputation methods.
To provide practical guidance for selecting appropriate imputation tools and identify future research directions.

Main Methods:

Related Experiment Videos

Last Updated: Jun 18, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

Review of existing literature on missing value imputation techniques.
Categorization of imputation methods based on their principles and applications.
Discussion of performance metrics for evaluating imputation accuracy and effectiveness.

Main Results:

A comprehensive overview of imputation methods, initially focusing on gene expression microarray data, and extending to other large-scale datasets.
Description of various strategies for addressing missing data, including their strengths and weaknesses.
Identification of a gap in systematic evaluations of imputation methods across different data types and research questions.

Conclusions:

Effective imputation is crucial for reliable analysis of high-throughput biological data.
A systematic evaluation framework is needed to guide the selection of imputation methods.
Further research is required to develop and refine imputation methodologies for diverse biological datasets.