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Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

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Computational Methods for Data Integration and Imputation of Missing Values in Omics Datasets.

Yannis Schumann¹, Antonia Gocke^2,3, Julia E Neumann^2,4

¹IT-Department, Deutsches Elektronen-Synchroton DESY, Hamburg, Germany.

|December 31, 2024

Summary

This summary is machine-generated.

This review offers a comprehensive guide to computational methods for integrating omics data and imputing missing values. It addresses batch effects and missing data challenges, providing researchers with tools for robust data analysis.

Keywords:

algorithm data integration missing values omics

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Area of Science:

Bioinformatics
Computational Biology
Data Science

Background:

Omics data (DNA methylomics, transcriptomics, proteomics) are crucial for research and clinical decisions.
Batch effects and missing values are significant challenges hindering omics data integration and analysis.

Purpose of the Study:

To provide a comprehensive overview of computational methods for omics data integration and missing value imputation.
To define missing value mechanisms and propose a taxonomy for batch effects, especially with missing data.

Main Methods:

Systematic literature review and automated document search.
Description of 32 data integration methods and 37 missing value imputation algorithms.
Evaluation of quantitative methods for selecting appropriate tools.

Main Results:

Categorization of 32 data integration methods and 37 imputation algorithms.
Formal definitions for missing value mechanisms and a novel taxonomy for batch effects.
Discussion on the relevance of batch effects and missing values in omics data.

Conclusions:

A proposed three-step workflow for omics data analysis from study conception to final analysis.
Recommendations for selecting appropriate methods for imputation and data integration.
Identification of future research perspectives in omics data preprocessing.