Differential expression analysis was performed to identify differentially expressed miRNAs and mRNAs in each brain region of AUD subjects (given that gene expression is tissue-specific) and ethanol-exposed hESC-derived cortical interneurons. For RNA-seq data from Set 1 brain tissue samples, the voom method [34], which is a function of the limma package [35], was used to estimate the mean-variance relationship of the log-counts and generate a precision weight for each gene. The RNA-seq read counts information from the voom analysis was then entered into the empirical Bayes analysis pipeline. The lmfit function in the limma package [35] was then used to fit a linear regression model using the weighted least square for each gene, and comparisons between case and control groups in log2 fold-changes (log2FC) were obtained as contrasts of the fitted linear model, with a number of confounding factors being considered as covariates. We did principal component analysis (PCA) to extract the first three PCs for both technical (batch, RIN, and PMI) and biological (sex, age, brain weight, brain pH, left-right brain, smoking, and liver disease) confounding variables, and the
