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

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 (+4 others)
2016 PLoS ONE  
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
more » ... ge 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
doi:10.1371/journal.pone.0156594 pmid:27272489 pmcid:PMC4896498 fatcat:mr4uqao6wvbjlbtnaazfekh7fq