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Our results show that batch effects can be attenuated statistically when proper experimental design is used.
Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch.
Batch effects can be a problem with RNAseq data [ 26].
Batch effects can be subdivided into two types, those that happen during manufacturing of the chip and those that happen during the processing of the chip.
Although some of these batch effects can be minimized or even prevented with appropriate precautions and experimental design, certain batch effects are unavoidable.
For example, in observational studies the effects of potentially confounding factors such as batch effects can be alleviated via careful use of randomisation.
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The magnitude of the batch effect can be significant or negligible, but it cannot be removed from the data without compromising the biological signal.
In the non-linear intensity case, if the batch effect can be modelled using a family-specific covariate that acts on the raw intensity data in an additive manner, this covariate will have the same effect on the parental and offspring intensities.
Methods, such as 'ComBat', developed for batch effect removal can be applied, even if possible confounding due to batch and slide effects can be at least partially avoided thanks to a good study design.
In case of a direct comparison of signals that are derived from different matrix batches spiked with analytes subsequent to the extraction, relative matrix effects can be addressed.
However for the design in Table 4, the batch effects are nested within sex and time effects and the result of disregarding batch effects can easily be evaluated.
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