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Robust Multichip Average (RMA) was used with the default configuration for background adjustment and normalization [ 34].

The microarray data were preprocessed using RMA in the default configuration for background adjustment and normalization.

Background adjustment and normalization is needed in microarray data analysis in order to remove non-biological variation.

Background adjustment and normalization is necessary to remove systematic biases of non-biological origin in microarray studies.

After background adjustment and normalization with the GCRMA procedure, a Venn diagram of genes regulated in the transgenic mice was generated (Fig. 7).

Probe expression intensity in each tissue sample was subjected to background adjustment and normalization with the robust multiarray analysis (RMA) algorithm (Irizarry et al, 2003).

Normalization was performed with RMA algorithm which included the global background adjustment and quantile normalization.

Colour correction, background adjustment, and quantile normalization were performed using the lumi R package, and data was normalized using peak-based correction [ 23].

All arrays were analyzed using GC Robust Multi-array Average (GCRMA) background adjustment and quantile normalization on probe-level data sets with R software (http://www.bioconductor.org).

The background adjustment and quantile normalization were performed using the default settings.

Background adjustment and quantile normalization parameters were selected for data processing.