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In all cases a further correction step is required after normalisation to correct for the batch effect.
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However when a batch effect is confounded with an experimental factor, correcting for the batch will also effectively remove the biological signal.
Indeed, we showed a reduction of the deviation from zero (Fig. 8A) and an increase in correlation between replicates (Fig. 8B) after correcting for the batch with the ComBat function.
In order to correct for these batch effects after the fact, they need to first be detected, and then adjusted for, be it through the use of covariates in linear models, or more involved procedures such as surrogate variable analysis [ 26].
The leptin levels were corrected for the inter-batch difference of control samples in the 10 batches in total used in the Luminex based analysis of the total cohort.
The data then underwent batch correction using Partek Genomics Suite software (version 6.5) to correct for any batch effect created by the different NSRL campaigns.
In order to correct for any batch variations, we used the batch adjustment method described by Tibshirani [ 13].
The ComBat function in the R package sva [ 95] was then used to correct for batch effects in the data.
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).
It was necessary to correct for batch effects within the platforms due to date of sample processing using ComBat as described previously [ 3- 5].
This motivated Benito et al. to apply a different approach, Distance Weighted Discrimination (DWD), to correct for systematic batch effects.
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