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Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data

Author

Listed:
  • Christian Müller
  • Arne Schillert
  • Caroline Röthemeier
  • David-Alexandre Trégouët
  • Carole Proust
  • Harald Binder
  • Norbert Pfeiffer
  • Manfred Beutel
  • Karl J Lackner
  • Renate B Schnabel
  • Laurence Tiret
  • Philipp S Wild
  • Stefan Blankenberg
  • Tanja Zeller
  • Andreas Ziegler

Abstract

Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data.

Suggested Citation

  • Christian Müller & Arne Schillert & Caroline Röthemeier & David-Alexandre Trégouët & Carole Proust & Harald Binder & Norbert Pfeiffer & Manfred Beutel & Karl J Lackner & Renate B Schnabel & Laurence T, 2016. "Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-23, June.
  • Handle: RePEc:plo:pone00:0156594
    DOI: 10.1371/journal.pone.0156594
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    References listed on IDEAS

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    1. Chao Chen & Kay Grennan & Judith Badner & Dandan Zhang & Elliot Gershon & Li Jin & Chunyu Liu, 2011. "Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-10, February.
    2. Jeffrey T Leek & John D Storey, 2007. "Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis," PLOS Genetics, Public Library of Science, vol. 3(9), pages 1-12, September.
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    1. Almudena Espín-Pérez & Chris Portier & Marc Chadeau-Hyam & Karin van Veldhoven & Jos C S Kleinjans & Theo M C M de Kok, 2018. "Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-19, August.
    2. Xia Qing & Thompson Jeffrey A. & Koestler Devin C., 2021. "Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 20(4-6), pages 101-119, December.

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