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The array and dye functions were defined by randomly selecting coefficients for a two-dimensional B-spline basis function from a N 0,0.75), the probe-specific batch effects were sampled from a N 0,0.4).
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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).
Batch effects were removed by aligning the within-batch medians for all measurements.
For Comparative gene expression profiling of blood samples from RM, CM, and AG, we used DESeq-normalized expression values; however, no correction for batch effects was required as these samples were all prepared and processed simultaneously.
Again, though, batch effects are complicated, and do not affect all samples equally.
Our evaluation makes clear that adjustment for batch effects is a mandatory step in the analysis of microarray data when the sample size is too large to fit in a single batch.
Batch effects are almost inevitable; largely because most of the available microarray platforms can assay fewer than 24 samples at a time (the latest technology may process 96 samples in each batch).
(3) Can batch effects be controlled?
No significant batch effect was detected while the samples' genders were well discriminated (see Figure 1).
To limit batch effects, samples were randomly distributed across slides and arrays.
To avoid batch effects, the samples were randomized during RNA isolation and DeepSAGE sample preparation.
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