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Probe signals were normalized using the Limma method [ 29].
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To find the differential genes between two conditions, we used the limma method [ 34, 35].
Differential expression between treatments was determined using the limma linear modeling method, and the significance of differences was ranked by the moderated t-statistic.
Differential expression was explored using linear models and empirical Bayes methods, using the limma package (31).
Normalization and statistical analysis of the gene array bead summary data was carried out with established methods using the limma package [ 38] and in-house scripts in R/Bioconductor [ 39].
Data normalization and differential gene expression analysis was performed using the limma package in R (for details see Methods).
Inter-array normalization using the quantile method was then performed using the limma package [ 42].
The microarray data were analyzed using the Limma software package ([ 23- 28]; see Methods for details).
P-values were calculated using the limma package available in Bioconductor employing the empirical Bayes method [ 15].
All 20 different preprocessing methods as described are implemented using the LIMMA package [ 4] of Bioconductor [ 33].
The GSEA analysis was performed using the Limma package from Bioconductor [ 14] as described in the materials and methods section.
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