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Differentially expressed genes (Table S5) identified using the limma software package [54].
All normalization and differential expression analysis was conducted using the limma software package[19] for the R programming environment (http://www.r-project.org).org
The fluorescence intensity data was background corrected and analyzed using the limma software [ 67].
The list of differentially expressed genes were calculated using the Limma software package [ 39].
Fluorescence signals were preprocessed and normalized using the limma software package from R/Bioconductor [ 27].
Statistical analysis was carried out using the limma software package [ 24].
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Statistical analysis used the limma software package [49].
Thus, we used the limma software [ 31] to deconvolve the undesired effects of differing mouse ages as explanatory variables in a linear model, and we subtracted these variables from the original gene expression values.
All statistical analyses were completed using the R statistical computing software (R Development Core Team 2008), with microarray data analyzed using the LIMMA package in the BioConductor software suite (Smyth 2004).
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.
Differential expression was assessed using empirical Bayes moderated t-statistics [98] and P-values were adjusted for multiple testing [99] using the limma package and R statistical software (www.r-project.org) [100].
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