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).
Each array underwent background adjustment with RMA convolution [ 9] and log2 transformation for variance stabilization.
Whole-image background adjustment was applied using Adobe Photoshop.
We reprocessed our Affymetrix microarray chip data using Robust Multichip Average (RMA) for further background adjustment and to reduce false positives of our Affymetrix microarray chip [35] [37].
A three-step process, RMA performs a background adjustment, quantile normalization and final summarization [ 92].
The normalization algorithm used was the default setting in the AffyMetrix miRNA QCTool program, which is background adjustment, BC-CG Adjust; normalization, Quantile; summarization, median polish.
After background adjustment and normalization with the GCRMA procedure, a Venn diagram of genes regulated in the transgenic mice was generated (Fig. 7).
The microarrays were preprocessed with a procedure similar to GCRMA, except that the background adjustment step is modified.
We used the LIMMA package to perform the background adjustment, normalization and summarization [ 58].
The platform is able to perform background adjustment, normalization and summarization of raw DNA microarray data based on widely accepted methods and algorithms.
Due to unspecific binding, the global background adjustment method robust multi-array average (RMA) expression measure, which ignores the MM intensities, has been developed [ 19].
