The quantile normalization method was used to normalize expression values at the probe level.
For each contrast and following previously described approaches [ 39], a combination of ANOVA models and mixtures of distributions were employed to normalize expression signals and to identify differentially expressed genes, respectively.
There are many approaches to normalize expression levels and the most commonly used, referred to as global normalization methods, apply to experiments in which most of the genes are equally expressed in both channels [ 3].
Finally, the qPCR approach utilized here, using gDNA to normalize expression estimates, could provide more reliable results than the typical alternative of normalizing to expression of a single reference gene in any instance where relative expression estimates are needed.
If spike-ins are available, they can be used to estimate individual cell's RNA content and normalize expression estimates more accurately.
A Loess transformation was performed using Beehive (http://stagbeetle.animal.uiuc.edu/Beehive) to normalize expression intensities.
Information from these controls was used to develop a predictive equation to normalize expression levels within and among arrays.
The recA gene was used as an internal reference transcription control to normalize expression data for each target gene.
18S (sense, 5'-GTAACCCGTTGAACCCCATT-3', and antisense, 5'- CCATCCAATCGGTAGTAGCG-3') was used as a housekeeping gene to normalize expression.
18S RNA expression levels served as a housekeeping gene to normalize expression between different samples and to monitor assay reproducibility.
Expression of TATA box binding protein (TBP) and human 18S ribosomal RNA (18S) was used to normalize expression levels.
