Alternative empirical Bayes models for adjusting for batch effects in genomic studies

BMC Bioinformatics
Yuqing ZhangW Evan Johnson

Abstract

Combining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies. Here we contribute multiple methods and software tools for improved combination and analysis of data from multiple batches. In particular, we provide batch effect solutions for cases where the severity of the batch effects is not extreme, and for cases where one high-quality batch can serve as a reference, such as the training set in a biomarker study. We illustrate our approaches and software in both simulated and real data scenarios. We demonstrate the value of these...Continue Reading

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Citations

Oct 6, 2020·NAR Genomics and Bioinformatics·Yuqing ZhangW Evan Johnson
Nov 28, 2020·Bioinformatics·Yuqing ZhangGiovanni Parmigiani
Jan 26, 2021·Clinical and Translational Gastroenterology·Michael RohrDeborah A Altomare
Oct 4, 2021·Bioinformatics·John GrafFiona Ginty

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Datasets Mentioned

BETA
GSE83083
GSE59765

Methods Mentioned

BETA
RNA-Seq

Software Mentioned

sva
R
Bioconductor
BatchQC
ComBat
ASSIGN

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