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After QNβ, lumi or ABnorm, the number of CpGs associated with batch effects were reduced to 24, 32, and 26percentt for Dataset 2; and 37, 46, and 35percentt for Dataset 3, respectively.
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).
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How could the effects be reduced?
In OHS, this effect is reduced.
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].
Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch.
One way of potentially reducing batch effects is to apply color normalization on the digitized images prior to training or application of the ConvNet classifier.
All three commonly used normalization approaches reduce batch effects, but none of them can remove it completely, particularly when batch effects are substantial.
(3) Can batch effects be controlled?
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