#!/usr/bin/env Rscript ######################################################### # TASK: producing boxplots for dr and other muts ######################################################### #======================================================================= # working dir and loading libraries # getwd() setwd("~/git/LSHTM_analysis/scripts/plotting") # getwd() # make cmd # globals # drug = "streptomycin" # gene = "gid" source("get_plotting_dfs.R") #======================================================================= # MOVE TO COMBINE or singular file for deepddg cols_to_select = c("mutation", "mutationinformation" , "wild_type", "position", "mutant_type" , "mutation_info") merged_df3_short = merged_df3[, cols_to_select] infilename_mcsm_f_snps <- paste0("~/git/Data/", drug, "/output/", gene , "_mcsm_formatted_snps.csv") mcsm_f_snps<- read.csv(infilename_mcsm_f_snps, header = F) names(mcsm_f_snps) <- "mutationinformation" # write merged_df3 to generate structural figure on chimera #write.csv(merged_df3_short, "merged_df3_short.csv") #======================================================================== # MOVE TO COMBINE or singular file for deepddg #============================ # adding deepddg scaled values # scale data b/w -1 and 1 #============================ n = which(colnames(merged_df3) == "deepddg"); n my_min = min(merged_df3[,n]); my_min my_max = max(merged_df3[,n]); my_max merged_df3$deepddg_scaled = ifelse(merged_df3[,n] < 0 , merged_df3[,n]/abs(my_min) , merged_df3[,n]/my_max) # sanity check my_min = min(merged_df3$deepddg_scaled); my_min my_max = max(merged_df3$deepddg_scaled); my_max if (my_min == -1 && my_max == 1){ cat("PASS: DeepDDG successfully scaled b/w -1 and 1" #, "\nProceeding with assigning deep outcome category") , "\n") }else{ cat("FAIL: could not scale DeepDDG ddg values" , "Aborting!") } #======================================================================== # cols to select cols_mcsm_df <- merged_df3[, c("mutationinformation", "mutation" , "mutation_info", "position" , LigDist_colname , "duet_stability_change", "duet_scaled", "duet_outcome" , "ligand_affinity_change", "affinity_scaled", "ligand_outcome" , "ddg_foldx", "foldx_scaled", "foldx_outcome" , "deepddg", "deepddg_scaled", "deepddg_outcome" , "asa", "rsa" , "rd_values", "kd_values" , "log10_or_mychisq", "neglog_pval_fisher", "af")] cols_mcsm_na_df <- mcsm_na_df[, c("mutationinformation" , "mcsm_na_affinity", "mcsm_na_scaled" , "mcsm_na_outcome")] # entire dynamut_df cols_dynamut2_df <- dynamut2_df[, c("mutationinformation" , "ddg_dynamut2", "ddg_dynamut2_scaled" , "ddg_dynamut2_outcome")] n_comb_cols = length(cols_mcsm_df) + length(cols_mcsm_na_df) + length(dynamut_df) + length(cols_dynamut2_df); n_comb_cols i1<- intersect(names(cols_mcsm_df), names(cols_mcsm_na_df)) i2<- intersect(names(dynamut_df), names(cols_dynamut2_df)) merging_cols <- intersect(i1, i2) cat("\nmerging_cols:", merging_cols) if (merging_cols == "mutationinformation") { cat("\nStage 1: Found common col between dfs, checking values in it...") c1 <- all(mcsm_f_snps[[merging_cols]]%in%cols_mcsm_df[[merging_cols]]) c2 <- all(mcsm_f_snps[[merging_cols]]%in%cols_mcsm_na_df[[merging_cols]]) c3 <- all(mcsm_f_snps[[merging_cols]]%in%dynamut_df[[merging_cols]]) c4 <- all(mcsm_f_snps[[merging_cols]]%in%cols_dynamut2_df[[merging_cols]]) cols_check <- c(c1, c2, c3, c4) expected_cols = n_comb_cols - ( length(cols_check) - 1) if (all(cols_check)){ cat("\nStage 2:Proceeding with merging dfs:\n") comb_df <- Reduce(inner_join, list(cols_mcsm_df , cols_mcsm_na_df , dynamut_df , cols_dynamut2_df)) comb_df_s = arrange(comb_df, position) # if ( nrow(comb_df_s) == nrow(mcsm_f_snps) && ncol(comb_df_s) == expected_cols) { # cat("\Stage3, PASS: dfs merged sucessfully" # , "\nnrow of merged_df: ", nrow(comb_df_s) # , "\nncol of merged_df:", ncol(comb_df_s)) # } } } names(comb_df_s) #======================================================================= fact_cols = colnames(comb_df_s)[grepl( "_outcome|_info", colnames(comb_df_s) )] fact_cols lapply(comb_df_s[, fact_cols], class) comb_df_s[,fact_cols] <- lapply(comb_df_s[,cols],as.factor) if (any(lapply(comb_df_s[, fact_cols], class) == "character")){ cat("\nChanging cols to factor") comb_df_s[, fact_cols] <- lapply(comb_df_s[, fact_cols],as.factor) if (all(lapply(comb_df_s[, fact_cols], class) == "factor")){ cat("\nSuccessful: cols changed to factor") } } lapply(comb_df_s[, fact_cols], class) #======================================================================= table(comb_df_s$mutation_info) # further checks to make sure dr and other muts are indeed unique dr_muts = comb_df_s[comb_df_s$mutation_info == dr_muts_col,] dr_muts_names = unique(dr_muts$mutation) other_muts = comb_df_s[comb_df_s$mutation_info == other_muts_col,] other_muts_names = unique(other_muts$mutation) if ( table(dr_muts_names%in%other_muts_names)[[1]] == length(dr_muts_names) && table(other_muts_names%in%dr_muts_names)[[1]] == length(other_muts_names) ){ cat("PASS: dr and other muts are indeed unique") }else{ cat("FAIL: dr and others muts are NOT unique!") quit() } # pretty display names i.e. labels to reduce major code duplication later foo_cnames = data.frame(colnames(comb_df_s)) names(foo_cnames) <- "old_name" stability_suffix <- paste0(delta_symbol, delta_symbol, "G") flexibility_suffix <- paste0(delta_symbol, delta_symbol, "S") lig_dn = paste0("Ligand distance (", angstroms_symbol, ")"); lig_dn duet_dn = paste0("DUET ", stability_suffix); duet_dn foldx_dn = paste0("FoldX ", stability_suffix); foldx_dn deepddg_dn = paste0("Deepddg " , stability_suffix); deepddg_dn mcsm_na_dn = paste0("mCSM-NA affinity ", stability_suffix); mcsm_na_dn dynamut_dn = paste0("Dynamut ", stability_suffix); dynamut_dn dynamut2_dn = paste0("Dynamut2 " , stability_suffix); dynamut2_dn encom_ddg_dn = paste0("EnCOM " , stability_suffix); encom_ddg_dn encom_dds_dn = paste0("EnCOM " , flexibility_suffix ); encom_dds_dn sdm_dn = paste0("SDM " , stability_suffix); sdm_dn mcsm_dn = paste0("mCSM " , stability_suffix ); mcsm_dn # Change colnames of some columns using datatable comb_df_sl = comb_df_s names(comb_df_sl) setnames(comb_df_sl , old = c("asa", "rsa", "rd_values", "kd_values" , "log10_or_mychisq", "neglog_pval_fisher", "af" , LigDist_colname , "duet_scaled" , "foldx_scaled" , "deepddg_scaled" , "mcsm_na_scaled" , "ddg_dynamut_scaled" , "ddg_dynamut2_scaled" , "ddg_encom_scaled" , "dds_encom_scaled" , "ddg_sdm" , "ddg_mcsm") , new = c("ASA", "RSA", "RD", "KD" , "Log10 (OR)", "-Log (P)", "MAF" , lig_dn , duet_dn , foldx_dn , deepddg_dn , mcsm_na_dn , dynamut_dn , dynamut2_dn , encom_ddg_dn , encom_dds_dn , sdm_dn , mcsm_dn) ) foo_cnames <- cbind(foo_cnames, colnames(comb_df_sl)) # some more pretty labels table(comb_df_sl$mutation_info) levels(comb_df_sl$mutation_info)[levels(comb_df_sl$mutation_info)==dr_muts_col] <- "DM" levels(comb_df_sl$mutation_info)[levels(comb_df_sl$mutation_info)==other_muts_col] <- "OM" table(comb_df_sl$mutation_info) ####################################################################### #====================== # Selecting dfs # with appropriate cols #======================= static_cols_start = c("mutationinformation" , "position" , "mutation" , "mutation_info") static_cols_end = c(lig_dn , "ASA" , "RSA" , "RD" , "KD") # ordering is important! ######################################################################### #============== # DUET: LF #============== cols_to_select_duet = c(static_cols_start, c("duet_outcome", duet_dn), static_cols_end) wf_duet = comb_df_sl[, cols_to_select_duet] #pivot_cols_ps = cols_to_select_ps[1:5]; pivot_cols_ps pivot_cols_duet = cols_to_select_duet[1: (length(static_cols_start) + 1)]; pivot_cols_duet expected_rows_lf = nrow(wf_duet) * (length(wf_duet) - length(pivot_cols_duet)) expected_rows_lf # LF data: duet lf_duet = gather(wf_duet , key = param_type , value = param_value , all_of(duet_dn):tail(static_cols_end,1) , factor_key = TRUE) if (nrow(lf_duet) == expected_rows_lf){ cat("\nPASS: long format data created for ", duet_dn) }else{ cat("\nFAIL: long format data could not be created for duet") quit() } ############################################################################ #============== # FoldX: LF #============== cols_to_select_foldx= c(static_cols_start, c("foldx_outcome", foldx_dn), static_cols_end) wf_foldx = comb_df_sl[, cols_to_select_foldx] pivot_cols_foldx = cols_to_select_foldx[1: (length(static_cols_start) + 1)]; pivot_cols_foldx expected_rows_lf = nrow(wf_foldx) * (length(wf_foldx) - length(pivot_cols_foldx)) expected_rows_lf # LF data: duet print("TESTXXXXXXXXXXXXXXXXXXXXX---------------------->>>>") lf_foldx <<- gather(wf_foldx , key = param_type , value = param_value , all_of(foldx_dn):tail(static_cols_end,1) , factor_key = TRUE) if (nrow(lf_foldx) == expected_rows_lf){ cat("\nPASS: long format data created for ", foldx_dn) }else{ cat("\nFAIL: long format data could not be created for duet") quit() } ############################################################################ #============== # Deepddg: LF #============== cols_to_select_deepddg = c(static_cols_start, c("deepddg_outcome", deepddg_dn), static_cols_end) wf_deepddg = comb_df_sl[, cols_to_select_deepddg] pivot_cols_deepddg = cols_to_select_deepddg[1: (length(static_cols_start) + 1)]; pivot_cols_deepddg expected_rows_lf = nrow(wf_deepddg) * (length(wf_deepddg) - length(pivot_cols_deepddg)) expected_rows_lf # LF data: duet lf_deepddg = gather(wf_deepddg , key = param_type , value = param_value , all_of(deepddg_dn):tail(static_cols_end,1) , factor_key = TRUE) if (nrow(lf_deepddg) == expected_rows_lf){ cat("\nPASS: long format data created for ", deepddg_dn) }else{ cat("\nFAIL: long format data could not be created for duet") quit() } ############################################################################ #============== # mCSM-NA: LF #============== cols_to_select_mcsm_na = c(static_cols_start, c("mcsm_na_outcome", mcsm_na_dn), static_cols_end) wf_mcsm_na = comb_df_sl[, cols_to_select_mcsm_na] pivot_cols_mcsm_na = cols_to_select_mcsm_na[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm_na expected_rows_lf = nrow(wf_mcsm_na) * (length(wf_mcsm_na) - length(pivot_cols_mcsm_na)) expected_rows_lf # LF data: duet lf_mcsm_na = gather(wf_mcsm_na , key = param_type , value = param_value , all_of(mcsm_na_dn):tail(static_cols_end,1) , factor_key = TRUE) if (nrow(lf_mcsm_na) == expected_rows_lf){ cat("\nPASS: long format data created for ", mcsm_na_dn) }else{ cat("\nFAIL: long format data could not be created for duet") quit() } ############################################################################ #============== # Dynamut: LF #============== cols_to_select_dynamut = c(static_cols_start, c("ddg_dynamut_outcome", dynamut_dn), static_cols_end) wf_dynamut = comb_df_sl[, cols_to_select_dynamut] pivot_cols_dynamut = cols_to_select_dynamut[1: (length(static_cols_start) + 1)]; pivot_cols_dynamut expected_rows_lf = nrow(wf_dynamut) * (length(wf_dynamut) - length(pivot_cols_dynamut)) expected_rows_lf # LF data: duet lf_dynamut = gather(wf_dynamut , key = param_type , value = param_value , all_of(dynamut_dn):tail(static_cols_end,1) , factor_key = TRUE) if (nrow(lf_dynamut) == expected_rows_lf){ cat("\nPASS: long format data created for ", dynamut_dn) }else{ cat("\nFAIL: long format data could not be created for duet") quit() } ############################################################################ #============== # Dynamut2: LF #============== cols_to_select_dynamut2 = c(static_cols_start, c("ddg_dynamut2_outcome", dynamut2_dn), static_cols_end) wf_dynamut2 = comb_df_sl[, cols_to_select_dynamut2] pivot_cols_dynamut2 = cols_to_select_dynamut2[1: (length(static_cols_start) + 1)]; pivot_cols_dynamut2 expected_rows_lf = nrow(wf_dynamut2) * (length(wf_dynamut2) - length(pivot_cols_dynamut2)) expected_rows_lf # LF data: duet lf_dynamut2 = gather(wf_dynamut2 , key = param_type , value = param_value , all_of(dynamut2_dn):tail(static_cols_end,1) , factor_key = TRUE) if (nrow(lf_dynamut2) == expected_rows_lf){ cat("\nPASS: long format data created for ", dynamut2_dn) }else{ cat("\nFAIL: long format data could not be created for duet") quit() } ############################################################################ #============== # EnCOM ddg: LF #============== cols_to_select_encomddg = c(static_cols_start, c("ddg_encom_outcome", encom_ddg_dn), static_cols_end) wf_encomddg = comb_df_sl[, cols_to_select_encomddg] pivot_cols_encomddg = cols_to_select_encomddg[1: (length(static_cols_start) + 1)]; pivot_cols_encomddg expected_rows_lf = nrow(wf_encomddg ) * (length(wf_encomddg ) - length(pivot_cols_encomddg)) expected_rows_lf # LF data: encomddg lf_encomddg = gather(wf_encomddg , key = param_type , value = param_value , all_of(encom_ddg_dn):tail(static_cols_end,1) , factor_key = TRUE) if (nrow(lf_encomddg) == expected_rows_lf){ cat("\nPASS: long format data created for ", encom_ddg_dn) }else{ cat("\nFAIL: long format data could not be created for duet") quit() } ############################################################################ #============== # EnCOM dds: LF #============== cols_to_select_encomdds = c(static_cols_start, c("dds_encom_outcome", encom_dds_dn), static_cols_end) wf_encomdds = comb_df_sl[, cols_to_select_encomdds] pivot_cols_encomdds = cols_to_select_encomdds[1: (length(static_cols_start) + 1)]; pivot_cols_encomdds expected_rows_lf = nrow(wf_encomdds) * (length(wf_encomdds) - length(pivot_cols_encomdds)) expected_rows_lf # LF data: encomddg lf_encomdds = gather(wf_encomdds , key = param_type , value = param_value , all_of(encom_dds_dn):tail(static_cols_end,1) , factor_key = TRUE) if (nrow(lf_encomdds) == expected_rows_lf){ cat("\nPASS: long format data created for", encom_dds_dn) }else{ cat("\nFAIL: long format data could not be created for duet") quit() } ############################################################################ #============== # SDM: LF #============== cols_to_select_sdm = c(static_cols_start, c("ddg_sdm_outcome", sdm_dn), static_cols_end) wf_sdm = comb_df_sl[, cols_to_select_sdm] pivot_cols_sdm = cols_to_select_sdm[1: (length(static_cols_start) + 1)]; pivot_cols_sdm expected_rows_lf = nrow(wf_sdm) * (length(wf_sdm) - length(pivot_cols_sdm)) expected_rows_lf # LF data: encomddg lf_sdm = gather(wf_sdm , key = param_type , value = param_value , all_of(sdm_dn):tail(static_cols_end,1) , factor_key = TRUE) if (nrow(lf_sdm) == expected_rows_lf){ cat("\nPASS: long format data created for", sdm_dn) }else{ cat("\nFAIL: long format data could not be created for duet") quit() } ############################################################################ #============== # mCSM: LF #============== cols_to_select_mcsm = c(static_cols_start, c("ddg_mcsm_outcome", mcsm_dn), static_cols_end) wf_mcsm = comb_df_sl[, cols_to_select_mcsm] pivot_cols_mcsm = cols_to_select_mcsm[1: (length(static_cols_start) + 1)]; pivot_cols_mcsm expected_rows_lf = nrow(wf_mcsm) * (length(wf_mcsm) - length(pivot_cols_mcsm)) expected_rows_lf # LF data: encomddg lf_mcsm = gather(wf_mcsm , key = param_type , value = param_value , all_of(mcsm_dn):tail(static_cols_end,1) , factor_key = TRUE) if (nrow(lf_mcsm) == expected_rows_lf){ cat("\nPASS: long format data created for", mcsm_dn) }else{ cat("\nFAIL: long format data could not be created for duet") quit() } ############################################################################ # clear excess variables rm(all_plot_dfs , cols_dynamut2_df , cols_mcsm_df , cols_mcsm_na_df , comb_df , corr_data_ps , corr_ps_df3 , df_lf_ps , foo , foo_cnames , gene_metadata , logo_data , logo_data_or_mult , logo_data_plot , logo_data_plot_logor , logo_data_plot_or , my_data_snp , my_df , my_df_u , ols_mcsm_df , other_muts , pd_df , subcols_df_ps , tab_mt , wide_df_logor , wide_df_logor_m , wide_df_or , wide_df_or_mult , wt) rm(c3, c4, check1 , cols_check , cols_to_select , cols_to_select_deepddg , cols_to_select_duet , cols_to_select_dynamut , cols_to_select_dynamut2 , cols_to_select_encomddg , cols_to_select_encomdds , cols_to_select_mcsm , cols_to_select_mcsm_na , cols_to_select_sdm)