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Cross-cohort batch effects were corrected using the COMBAT empirical Bayes method (Johnson et al, 2007).
Cross-cohort batch effects were corrected by using the COMBAT empirical Bayes method [ 11].
Batch effects were corrected by applying parametric empirical Bayes method (Johnson et al, 2007).
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Empirical results obtained through application of the SampleNetwork R function to many datasets indicated that as outlying samples are removed, data are normalized, and technical artifacts (e.g. batch effects) are corrected, Z. K and Z. C exhibit a progressively linear, inverse relationship.
Finally, the batch effect was corrected using ComBat normalisation method [ 40].
Batch effect was corrected using ComBat with default options through the Bioconductor package sva 3.10 [ 52, 53].
The microarray data were normalized by a quantile normalization procedure by using the bioconductor package affy, and batch effect was corrected by using the ComBat algorithm [ 28].
Microarrays were normalized using Robust Multiarray Average (12), and the batch effect was corrected using the distance-weighted discrimination method (13).
Batch-effect was corrected using the algorithm ComBat implemented in MSPrep[ 35].
As summarized Table 1 none of the PCs were associated with the batch effects anymore for the batch corrected data; and the CpGs that were associated with the batch effects were reduced to a few (12, 25, 23 in Dataset 2 and 2, 6, 8 in Dataset 3 for QNβ, lumi and ABnorm normalized data, respectively).
Batch effects were removed by aligning the within-batch medians for all measurements.
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