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.