#!/usr/bin/env Rscript ######################################################### # TASK: formatting data that will be used for various plots ######################################################### # load libraries and functions library(data.table) library(dplyr) #======================================================== # plotting_data(): formatting data for plots # input args: ## input csv file ## lig cut off dist, default = 10 Ang # output: list of 4 dfs, that need to be decompressed ## my_df ## my_df_u ## my_df_u_lig ## dup_muts #======================================================== plotting_data <- function(df , lig_dist_colname = 'ligand_distance' , lig_dist_cutoff = 10) { my_df = data.frame() my_df_u = data.frame() my_df_u_lig = data.frame() dup_muts = data.frame() #=========================== # Read file: struct params #=========================== #df = read.csv(infile_params, header = T) cat("\nInput dimensions:", dim(df)) #================================== # add foldx outcome category # and foldx scaled values # This will enable to always have these variables available # when calling for plots # included this now in combine_dfs.py!!!! finallyS #================================== #------------------------------ # # adding foldx scaled values # # scale data b/w -1 and 1 # #------------------------------ # n = which(colnames(df) == "ddg"); n # # my_min = min(df[,n]); my_min # my_max = max(df[,n]); my_max # # df$foldx_scaled = ifelse(df[,n] < 0 # , df[,n]/abs(my_min) # , df[,n]/my_max) # # sanity check # my_min = min(df$foldx_scaled); my_min # my_max = max(df$foldx_scaled); my_max # # if (my_min == -1 && my_max == 1){ # cat("\nPASS: foldx ddg successfully scaled b/w -1 and 1" # , "\nProceeding with assigning foldx outcome category") # }else{ # cat("\nFAIL: could not scale foldx ddg values" # , "Aborting!\n") # } #------------------------------ # adding foldx outcome category # ddg<0 = "Stabilising" (-ve) #------------------------------ # c1 = table(df$ddg < 0) # df$foldx_outcome = ifelse(df$ddg < 0, "Stabilising", "Destabilising") # c2 = table(df$ddg < 0) # # if ( all(c1 == c2) ){ # cat("\nPASS: foldx outcome successfully created") # }else{ # cat("\nFAIL: foldx outcome could not be created. Aborting!\n") # exit() # } #------------------------------ # renaming foldx column from # "ddg" --> "ddg_foldx" #------------------------------ # # change name to foldx # colnames(df)[n] <- "ddg_foldx" #================================== # extract unique mutation entries #================================== # check for duplicate mutations if ( length(unique(df$mutationinformation)) != length(df$mutationinformation)){ cat(paste0("\nCAUTION:", " Duplicate mutations identified" , "\nExtracting these...\n")) #cat(my_df[duplicated(my_df$mutationinformation),]) dup_muts = df[duplicated(df$mutationinformation),] dup_muts_nu = length(unique(dup_muts$mutationinformation)) cat(paste0("\nDim of duplicate mutation df:", nrow(dup_muts) , "\nNo. of unique duplicate mutations:", dup_muts_nu , "\n\nExtracting df with unique mutations only\n")) my_df_u = df[!duplicated(df$mutationinformation),] }else{ cat(paste0("\nNo duplicate mutations detected\n")) my_df_u = df } upos = unique(my_df_u$position) cat("\nDim of clean df:"); cat(dim(my_df_u), "\n") cat("\nNo. of unique mutational positions:"); cat(length(upos), "\n") #=============================================== # extract mutations <10 Angstroms and symbol #=============================================== table(my_df_u[[lig_dist_colname]] < lig_dist_cutoff) my_df_u_lig = my_df_u[my_df_u[[lig_dist_colname]] < lig_dist_cutoff,] cat(paste0("There are ", nrow(my_df_u_lig), " sites lying within 10\u212b of the ligand\n")) # return list of DFs my_df = df #df_names = c("my_df", "my_df_u", "my_df_u_lig", "dup_muts") all_df = list(my_df, my_df_u, my_df_u_lig, dup_muts) #all_df = Map(setNames, all_df, df_names) return(all_df) } ######################################################################## # end of data extraction and cleaning for plots # ########################################################################