ADCoexpressionNetwork/plot_cnetplot_neighborhood.R at main · TAKMU/ADCoexpressionNetwork · GitHub

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library(dplyr)
library(ggplot2)
library(clusterProfiler)
library(org.Hs.eg.db)

drugs <- read.csv("./data/neighborhood_final_LINCS_ad_gene_nw_modified_with_symbols.csv")  %>%
            pull("Source")  %>%
            unique()
top_bottom <- c("bottom", "top")
universe_genes <- read.csv("./data/universe_genes_LINCS.csv") %>% pull(gene) %>% unique()
for (drug in drugs){
    df_drug <- read.csv("./data/neighborhood_final_LINCS_ad_gene_nw_modified_with_symbols.csv") %>%
        filter(Source == drug)
    for (name in top_bottom){
        if (name == "top"){
            gene_list <- df_drug %>%
                filter(Weight == 1) %>%
                pull(Target) %>%
                unique()
        } else {
            gene_list <- df_drug %>%
                filter(Weight == -1)%>%
                pull(Target)%>%
                unique()
        }

       if (length(gene_list) == 0) next

        enrich_results <- enrichGO(
            gene = gene_list,
            OrgDb = org.Hs.eg.db,
            keyType = "SYMBOL",
            universe = universe_genes,
            ont = "BP",
            pAdjustMethod = "BH",
            pvalueCutoff = 0.05,
            qvalueCutoff = 0.05
        )

        df_result <- as.data.frame(enrich_results)

        if (nrow(df_result) == 0) next

        write.csv(df_result, file = paste0("./data/neighborhood/enr_", name, "_neighborhood_", drug, ".csv"), row.names = FALSE)


        cnet <- cnetplot(enrich_results,
                       layout = 'circle',
                       showCategory = 5,
                       color_edge = "category")
        title <- paste0("Gene-concept network of", name, "5 enriched BPs for neighborhood of ", drug, " based on ORA")
        cnet <- cnet + ggplot2::ggtitle(title)
        ggplot2::ggsave(paste0("./img/neighborhood/cnetplot_", name, "_neighborhood_", drug, ".png"), cnet, width = 15, height = 15)
    }
}