Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data
File(s)
Published version
Author(s)
Type
Journal Article
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 batch effect, showing no added value for including them in the study design.
Date Issued
2018-08-30
Date Acceptance
2018-08-13
Citation
PLoS ONE, 2018, 13 (8)
ISSN
1932-6203
Publisher
Public Library of Science (PLoS)
Journal / Book Title
PLoS ONE
Volume
13
Issue
8
Copyright Statement
© 2018 Espı´n-Pe´rez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/).
License URL
Sponsor
Commission of the European Communities
Commission of the European Communities
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000443388900053&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
308610
633666
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
GENE-EXPRESSION
MICROARRAY
DIAGNOSIS
Publication Status
Published
Article Number
ARTN e0202947
Date Publish Online
2018-08-30