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To find the differentially expressed genes we utilized the limma package for R to calculate the Log2 fold change, the moderate t-statistic, B value, and the adjusted p value (Benjamini Hochberg FDR) for each array probe [28].
The t-statistic was generated using the Limma package, which utilizes a standard error moderated across all genes using a simple Bayesian model and produces p-values with greater degrees of freedom and hence greater reliability [ 89].
Quantile normalization was performed as implemented in the limma package.
Statistical tests between cell types were performed using the eBays function of the limma package.
Venn diagrams were generated using the VennDiagram function in R 3.2.1 with the limma 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].
Significance testing was performed using the limma package [72].
Differentially expressed genes were identified using the limma package.
The statistical analysis was performed using the limma package.
The limma package within R was used in the genomic microarray data analysis.
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