R/figure.R R/topGO_analysis.R: add correction of p-values

R/figure.R

@@ -17,10 +17,10 @@ build_figure <- function(top_go_result, go_term_type, outfile) {
     }
     top_go_result$fish <- as.numeric(gsub("< ", "", top_go_result$fish))
     top_go_result <- top_go_result %>%
-        mutate(sigv = -log10(pvalue),
+        mutate(sigv = -log10(padj),
         significance = ifelse(sigv < 400, sigv, 400),
         ft = paste(GO.ID, ":", Term)) %>%
-        filter(pvalue <= 0.05) %>%
+        filter(padj <= 0.05) %>%
         arrange(desc(significance))
     fig <- ggplot(top_go_result, mapping = aes(
         x = significance, y = fct_reorder(ft, significance),

R/topGO_analysis.R

@@ -55,8 +55,7 @@ get_gene_list <- function(de_table, regulation, alpha, log2fc_threshold,
 #' @import org.Hs.eg.db
 #' @export
 top_go_analysis <- function(de_table, kind, id_kind, go_term_type, regulation,
-                            top, alpha, log2fc_threshold, basemean_threshold
-                            ) {
+                            top, alpha, log2fc_threshold, basemean_threshold) {
     if (kind == "tibble") {
         gene_list <- get_gene_list(
             de_table, regulation, alpha, log2fc_threshold,
@@ -90,9 +89,10 @@ top_go_analysis <- function(de_table, kind, id_kind, go_term_type, regulation,
     considered_genes <- length(gene_list[gene_list == 1])
     results.f <- runTest(GOdata, algorithm = "classic", statistic = "fisher")
     res <- GenTable(GOdata, fish = results.f, orderBy = "fish", topNodes = top)
-    df <- as.data.frame(sort(score(results.f))[1:top])
+    df <- as.data.frame(sort(score(results.f)))
     colnames(df) <- c("pvalue")
-    df$GO.ID <- rownames(df)
+    df$padj <- p.adjust(df$pvalue, method = "fdr")
+    df$GO.ID <- rownames(head(df, n=top))
     res <- res %>% left_join(df)
     return(list(
         res = res, nb_gene = considered_genes, nb_go_gene = nb_go_gene,