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Selected study indicators were normalized in order to render them comparable to one another.
The weight vectors are normalized (in order to obtain a sum equal to 1) after each iteration.
Gene array raw data was first normalized in order to make multiple chip data comparable using the pre-processing algorithm "GeneChip - Robust Multi-Array Average" (GC-RMA) method to account for background correction, normalization, and summarization.
The original expression values were scaling normalized in order to ensure that samples were comparable [ 57].
Before they were compiled into an expression profile data matrix, all arrays were quantile normalized in order to be comparable.
The data (GEO accession number GSE21059) were background corrected but not normalized in order to preserve dependence across time points.
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In order to perform comparisons across the sample set, expression values were rank-normalized, in order to preserve the ordering of genes in a sample while removing any other factor affecting the set.
The data was subsequently normalized using quantile normalization in order to adjust the scale of intensities across arrays [ 52].
For the normalization of the entire set of 85 Affymetrix gene expression arrays, the Extrapolation Strategy (RMA+) algorithm (Harbron et al. 2007) was used that applies background correction, log2 transformation, quantile normalization and a linear model fit to the normalized data in order to obtain a value for each PS on each array.
In testing alternatives, designers require both an absolute measure and a normalized measure in order to make a definitive evaluation.
A Student' t-test with a p value of ≤ 0.05 was applied to each set of normalized values in order to determine statistical significant differences in expression levels.
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