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Data were quantile normalized and subject to log2 transformation Potential batch effects were assessed by visual inspection of MDS plots and PCA analysis.
Batch effects were assessed using multidimensional scaling (MDS) plots for gender, sequencing flow cell and disease stage and by using the edgeR bioconductor package for RNA-Seq.
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After BMIQ normalization, magnitude of batch effects are assessed and corrected using the ComBat normalization method, which is an empirical Bayes based method to correct for technical variation related to the slide [ 41].
Batch effects were removed by aligning the within-batch medians for all measurements.
Batch effects were then adjusted by the COMBAT method [ 30].
Experimental batch effects were adjusted for by including experimental batch as a covariate in the statistical model [ 56].
In order to assess if batch effects are uniform across all genes, we investigated whether the properties of the probes themselves were affected by different sources of systematic error (see methods).
In order to establish the best model for experimental and batch effects, a series of ANOVAs were used to assess the contribution of different sources of error (potential causes of batch effects are described in the array annotation deposited with the raw microarray data).
(3) Can batch effects be controlled?
Batch effects are not the only source of unwanted variance.
They can also mistake population stratification of common CNVs for batch effects if true batch effects are minimal.
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