Takes 2 tables, performs column-wise Wilcoxon test for each shared colname, generates p value table

get_pvals.R

require(tibble)
require(dplyr)
require(stats)

# make mock data, genes as columns
control <- tibble(subject = paste0("control", sprintf('%0.2d', 1:20)),
                  gene1 = rnorm(20, 10, 2),
                  gene2 = rnorm(20, 10, 2),
                  gene3 = rnorm(20, 10, 2))
  
lupus   <- tibble(subject = paste0("lupus", sprintf('%0.2d', 1:20)),
                  gene1 = rnorm(20, 10, 2),
                  gene2 = rnorm(20, 10, 2),
                  gene3 = rnorm(20, 10, 2))

# extract list of genes shared by both datasets
genes <- dplyr::intersect(colnames(control[,-1]),
                          colnames(lupus[,-1]))

# return dataframe of p values for each gene
results <- sapply(genes,
                  function(gene) {
  
                    c_dat <- purrr::as_vector(dplyr::select(control, gene))
                    l_dat <- purrr::as_vector(dplyr::select(lupus, gene))
                    wilcox.test(c_dat, l_dat)$p.value
         
}) |> tibble::enframe()