limma powers differential expression analyses for RNA-sequencing and microarray studies.
DEGs of AR were identified by the
limma package following the process of linear model, contrast model, and DEGs selection.
Smyth, "
Limma: linear models for microarray data," Bioinformatics and Computational Biology Solution Using R and Bioconductor, Springer, Berlin, Germany, 2005.
For each experimental factor combination item, concentration and post-exposure, a model to estimate the treatment effect was fitted with
LIMMA (Smyth, 2004) by including the covariate exposure run as a blocking variable to account for the pairing during an exposure run (exposed vs.
Finally, the
limma package in R [13] was used to identify significant DEGs in the POAG ONH and normal ONH samples.
After variance-stabilizing transformation and normalization with the robust spline normalization method in the package lumi of R, differential analysis was performed using package
limma for the following groups: (1) 20 [micro]M versus untreated control; (2) 300 [micro]M versus untreated.
The obtained counts were analyzed using the "
limma" package (implemented in R environment) specially developed for whole-transcriptome analyses of differentially expressed genes [27].
R package '
limma' was used to normalize and qualify microarray images.