Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data

PloS One
Almudena Espín-PérezTheo M C M de Kok

Abstract

Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding "treatment" and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample sizes and level of balanced/unbalanced designs. The simulations showed small differences among LMM and Combat. LMM identifies stronger relationships between big effect sizes and gene expression than Combat, while Combat identifies in general more true and false positives than LMM. However, these small differences can still be relevant depending on the research goal. When any of these methods are applied, quality control samples did not reduce the ...Continue Reading

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Citations

Nov 27, 2019·Aging·Alex ZhavoronkovPolina Mamoshina
May 6, 2020·Environmental Science and Pollution Research International·Jacopo FabrelloValerio Matozzo
May 24, 2021·Journal of Genetics and Genomics = Yi Chuan Xue Bao·Fangyoumin FengHong Li
Oct 4, 2021·Bioinformatics·John GrafFiona Ginty

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Methods Mentioned

BETA
PCA

Software Mentioned

EXPOsOMICS
R package “ pROC
Combat
LMM
R package “ stats ”
Surrogate Variable Analysis ( sva )

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