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All subsequent analysis was performed using the Bioconductor software environment.
All data were analyzed using the BioConductor software project and the statistical language R. Raw data from all hybridizations were background corrected, normalized and summarized using gcRMA [73] as implemented in R with default parameters [74].
Differential gene expression analyses were made with R using the Bioconductor software package edgeR [ 19].
The resulting datasets were analysed using the Bioconductor software environment [ 65].
Data were processed using the Bioconductor software suite (Gentleman et al, 2004).
Probe cell intensity files (CEL) were analyzed for potential outliers using the Bioconductor software package arrayQualityMetrics (Kauffmann et al., 2009).
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Microarray data were normalized by the GC-RMA algorithm using the bioconductor package of R software.
We further examined differential expression between the OP and CP samples using the Bioconductor package edgeR and the Tuxedo software Cuffdiff.
It has been completely implemented in the software platform R using the BioConductor package collection [ 32- 34].
Intensity values for each spot were extracted using GenePix 4.1 software and analyzed using the Bioconductor package 'Linear models for microarray data analysis' under R 2.9.1.
Data were corrected by subarray (print tip) median centering and LOWESS smoothing using the Bioconductor R software package [ 16].
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