Figure 4.

Analysis of normalization of gene count data across all samples and principal component analysis (PCA) of sample relationship post DESeq2 normalization in R. (A) Quality analysis was investigated by comparing bar plots of the raw (un-normalized) log₂ count data across all samples to the normalized log₂ count data after the DESeq2 function for differential gene expression analysis had been applied across all samples. (B) Clustering of samples per condition was visualized through a principal component analysis (PCA) plot, which breaks down the maximum levels of variation into components of the top 100 differentially expressed genes after regularized-logarithm transformation (rlog) in DESeq2.