GO富集分析柱状图

 target_gene_id <- unique(read.delim("miRNA-gene interactions.txt")$EntrezID)
 # BiocInstaller::biocLite("clusterProfiler")
 # BiocInstaller::biocLite("org.Hs.eg.db")
 
 display_number = c(, , )
 ## GO enrichment with clusterProfiler
 library(clusterProfiler)
 ego_MF <- enrichGO(OrgDb="org.Hs.eg.db",
              gene = target_gene_id,
              pvalueCutoff = 0.05,
              ont = "MF",
              readable=TRUE)
 ego_result_MF <- as.data.frame(ego_MF)[:display_number[], ]
 # ego_result_MF <- ego_result_MF[order(ego_result_MF$Count),]
 
 ego_CC <- enrichGO(OrgDb="org.Hs.eg.db",
                    gene = target_gene_id,
                    pvalueCutoff = 0.05,
                    ont = "CC",
                    readable=TRUE)
 ego_result_CC <- as.data.frame(ego_CC)[:display_number[], ]
 # ego_result_CC <- ego_result_CC[order(ego_result_CC$Count),]
 
 ego_BP <- enrichGO(OrgDb="org.Hs.eg.db",
                    gene = target_gene_id,
                    pvalueCutoff = 0.05,
                    ont = "BP",
                    readable=TRUE)
 ego_result_BP <- na.omit(as.data.frame(ego_BP)[:display_number[], ])
 # ego_result_BP <- ego_result_BP[order(ego_result_BP$Count),]
 
 go_enrich_df <- data.frame(ID=c(ego_result_BP$ID, ego_result_CC$ID, ego_result_MF$ID),
                                    Description=c(ego_result_BP$Description, ego_result_CC$Description, ego_result_MF$Description),
                                    GeneNumber=c(ego_result_BP$Count, ego_result_CC$Count, ego_result_MF$Count),
                                    type=factor(c(rep("biological process", display_number[]), rep("cellular component", display_number[]),
                                           rep("molecular function", display_number[])), levels=c("molecular function", "cellular component", "biological process")))
 
 ## numbers as data on x axis
 go_enrich_df$number <- factor(rev(:nrow(go_enrich_df)))
 ## shorten the names of GO terms
 shorten_names <- function(x, n_word=, n_char=){
   if (length(strsplit(x, " ")[[]]) > n_word || (nchar(x) > ))
   {
     if (nchar(x) > ) x <- substr(x, , )
     x <- paste(paste(strsplit(x, " ")[[]][:min(length(strsplit(x," ")[[]]), n_word)],
                        collapse=" "), "...", sep="")
     return(x)
   }
   else
   {
     return(x)
   }
 }
 
 labels=(sapply(
   levels(go_enrich_df$Description)[as.numeric(go_enrich_df$Description)],
   shorten_names))
 names(labels) = rev(:nrow(go_enrich_df))
 
 ## colors for bar // green, blue, orange
 CPCOLS <- c("#8DA1CB", "#FD8D62", "#66C3A5")
 library(ggplot2)
 p <- ggplot(data=go_enrich_df, aes(x=number, y=GeneNumber, fill=type)) +
   geom_bar(stat="identity", width=0.8) + coord_flip() +
   scale_fill_manual(values = CPCOLS) + theme_bw() +
   scale_x_discrete(labels=labels) +
   xlab("GO term") +
   theme(axis.text=element_text(face = "bold", color="gray50")) +
   labs(title = "The Most Enriched GO Terms")
 
 p
 
 pdf("go_enrichment_of_miRNA_targets.pdf")
 p
 dev.off()
 
 svg("go_enrichment_of_miRNA_targets.svg")
 p
 dev.off()