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2.5. Analysis of differential expression
The DESeq R package was used to identify differentially expressed genes between the control and Aza-treated samples (Anders and Huber, 2010). The P-values in multiple tests were adjusted using the Benjamini and Hochberg (1995) method to control the false discovery rate (FDR). We used “fold changes ≥2 and P < 0.05” as the threshold to assess differentially expressed genes between the Aza treatment and control groups. To minimize false-positives and -negatives ( Zenoni et al., 2010), we performed only the statistical analysis on the genes with an RPKM ≥2 in at least one of the two groups.
The differentially expressed genes were annotated according to Gene Ontology (GO) terms for biological process, molecular function, and cellular component using a gene functional classification tool of DAVID (version 6.7) (Huang et al., 2008).
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