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As a quality check, MA-plots (M = log2594/log2488, A = (log2594 + log2488)/2), image plots and box plots were constructed using the Limma tools for visualisation of the data before and after normalisation.
To evaluate the analyses, MA-plots (M = log2594/log2488, A = (log2594 + log2488)/2), image plots and box plots were constructed using the Limma tools for visualisation both before and after normalisation (see additional file 6: Boxplots).
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Empirical Bayes robustification and adjustment for multiple testing was included even in this analysis, using the limma tool [ 40].
We use the LIMMA tool, which can operate on time series data determining significance of gene expression changes over time [ 12].
The feature extraction files were analyzed using the Limma bioconductor package implemented in the Robin/RobiNA tool [ 78, 100].
Statistical analysis was performed using the limma R package30.
Data were log2-transformed and quantile-normalized using the "limma" package15.
Statistical analysis for the microarray analysis of differential gene expression was performed using the limma and lumi R/Bioconductor packages.
Quality control and differential analysis of summary data was performed using the limma (linear models for microarray data) bioconductor package.
Data were imported into R software using the limma package (Smyth [2005]), transformed to the log scale (base 2), and normalized via loess smoothing with no background correction.
Significant genes were identified using the Limma package [75].
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