Differentially expressed gene analysis
To begin with, the raw data was normalized with the quantile algorithm. The probes that at least 1 conditions out of 2 conditions have flags in “P” were chosen for further data analysis. Differentially expressed genes or lncRNAs were then identified through fold change as well as P value calculated with t-test. The threshold set for up and down-regulated genes was a fold change≥ 2.0 and adj P value≤ 0.05. Afterwards, GO analysis and KEGG analysis were applied to determine the roles of these DEGs. Finally, Hierarchical Clustering was performed to display the distinguishable genes’ expression pattern among samples.