Identification of DEGs

The raw signal intensities were normalized with the quantile method by GeneSpring GX v11.5, and low intensity genes were filtered. The probe quality control was determined using principal component analysis (PCA) in GeneSpring. The statistical software R (version 3.4.1; R Core Team, 2017) and the linear models for microarray data package (limma) (Ritchie et al., 2015) in Bioconductor (http://www.bioconductor.org/) were applied to identify DEGs by comparing expression values between samples in the surgery and control groups. An empirical Bayes method was used to select significant DEGs based on the “limma” package in Bioconductor. In this study, we were interested in determining which genes were expressed at different levels between the control and surgery groups. In our analysis, linear models are fitted to the data with the assumption that the underlying data are normally distributed. Therefore, a design matrix was set up with the vein graft tissue information.