RNA-seq data analysis

Samples were analyzed using Partek Flow software. In short, mRNA contaminants were filtered using Bowtie 2, and reads were aligned using STAR. The samples were then normalized to generate counts per million, which were then transformed using DESeq2 (Love et al., 2014) and used for downstream analyses. A heatmap was generated from a filtered list of features with a false discovery rate (FDR)–adjusted P value of <0.1, followed by hierarchical clustering. A volcano plot was created from the unfiltered feature list, with dotted lines denoting an FDR-adjusted P value of 0.1. Fold change of >1.5 or <1.5 is also depicted on the graph. Gene set enrichment analysis was performed on a ranked gene list calculated as the sign (either + or −) of log2(fold change) × −log₁₀(FDR-adjusted P value) and performed using default parameters (Subramanian et al., 2005). This calculated ranked gene list was compared with a list of KEGG pathway gene sets (Kanehisa and Goto, 2000; Kanehisa et al., 2017, 2019) for determination of pathway enrichment, with q value scores of <0.05 deemed as significant.

No statistical methods were used to predetermine sample size. The experiments were not randomized. All investigators were not blinded to allocation during experiments and outcome assessment.