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Updated: Sep 24, 2025

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

Published on: August 13, 2020

Controlling Batch Effect in Epigenome-Wide Association Study.

Yale Jiang^1,2, Jianjiao Chen¹, Wei Chen³

¹Division of Pulmonary Medicine, Department of Pediatrics, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|May 3, 2022

Summary

Batch effects in omics data, like DNA methylation, can skew results. This review covers methods to correct these technical biases, offering practical recommendations for epigenome-wide association studies (EWAS).

Keywords:

Batch effects COMBAT DNA methylation Linear mixed effect model

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A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

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Published on: August 13, 2020

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

Genomics
Bioinformatics
Epigenetics

Background:

Omics data, including DNA methylation, are prone to technical variability.
Differences in laboratory procedures or sequencing platforms can introduce batch effects.
Batch effects are common in large-scale omics datasets and require careful management.

Purpose of the Study:

To review and demonstrate popular methods for batch effect correction in omics data.
To provide practical recommendations for handling batch effects in epigenome-wide association studies (EWAS).

Main Methods:

Review of statistical methods for batch effect control.
Demonstration of selected correction techniques.
Focus on methods applicable to DNA methylation data.

Main Results:

Batch effects are a significant challenge in omics data analysis.
Various statistical approaches exist to mitigate bias and inflation caused by batch effects.
Effective correction strategies are crucial for reliable EWAS findings.

Conclusions:

Accurate batch effect correction is essential for robust omics data interpretation.
The choice of method depends on the nature of the batch factor (known or estimated).
Recommendations are provided to guide researchers in selecting appropriate correction methods for EWAS.