A combined raw count file generated by the subread featureCounts tool for each data set was imported into R. Technical replicates were combined using the collapseReplicates function from the DESeq2 package. The variance stabilizing transform from the DESeq2 package was applied to the count data from before and after combining technical replicates (if there were technical replicates) before conducting principal component analysis. The top 2 PCs were graphed to visualize clustering between experimental groups. DESeq2 was used with non-normalized count data to find differentially expressed genes between experimental groups. Covariates were controlled for by adding them to experimental design if available. A cutoff of logFC more than 0.4 and q less than 0.05 was used for differential expression for all the liver samples.

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