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The data were subsequently normalized using the standard normalization algorithm within JMP Genomics.
We base our correlation matrix on measured values x normalized with the standard normalization function f(x) = log(x+1).
Gene-expression values were global linearly normalized according to manufacturers' standard normalization procedure [ 9, 26].
In the absence of a gold standard normalization method, it appears to be good practice to use multiple normalizers showing low variation within the samples.
Standard normalization techniques fail to correct for these hidden variables and lead to flawed downstream analyses.
Finally, through a standard normalization, we use the said gradient in a multi-objective utility function that aims to maximize delivery and minimize delay concurrently.
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We applied standard normalizations addressing typographical, morphological, and syntactic variation, applied some partially domain-specific term reduction rules, and resolved abbreviations.
While standard normalizations failed to account for the effects of degradation, we found that by explicitly controlling for the effects of RIN using a linear model framework we can correct for the majority of these effects.
Combining with the standard LOESS normalization and the method without any normalization, there are four normalization methods.
In order to reduce the illumination dependence, the images are firstly normalized using a standard statistical normalization.
After background subtraction, the raw microarray data were normalized using a standard global normalization technique, and the signal intensities were calculated as the fold changes of expression values.
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