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As a result, potential batch effects were confounded with sample effects (i.e., if the platforms were varying in performance through time, it might easily appear as if it were varying in response to different samples).
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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.
Diet effects were confounded with breed.
Which effects are confounded depends on the defining relation of the fractional study.
Batch effects were removed by aligning the within-batch medians for all measurements.
Batch effects were then adjusted by the COMBAT method [ 30].
Confounding batch effects are even more of a problem when comparing array data from experiments conducted in different laboratories.
Systematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results.
(3) Can batch effects be controlled?
The batch effects are the systematic differences between batches (groups) of samples in microarray experiments due to technical reasons, such as variability in materials, protocols or operators, possibly introducing a bias able to confound true biological differences (recently reviewed by Luo and coll. [ 30]).
Batch effects are not the only source of unwanted variance.
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