126 lines
No EOL
3.9 KiB
R
Executable file
126 lines
No EOL
3.9 KiB
R
Executable file
#!/usr/bin/env Rscript
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#########################################################
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# TASK: formatting data that will be used for various plots
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# useful links
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#https://stackoverflow.com/questions/38851592/r-append-column-in-a-dataframe-with-frequency-count-based-on-two-columns
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#########################################################
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# working dir and loading libraries
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#getwd()
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#setwd("~/git/LSHTM_analysis/scripts/plotting")
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#getwd()
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library(data.table)
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library(dplyr)
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#=========================================================
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plotting_data <- function(infile_params) {
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cat(paste0("Input file 1:", infile_params, '\n') )
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# These globals are created by import_dirs()
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cat('columns based on variables:\n'
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, drug
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, '\n'
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, dr_muts_col
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, '\n'
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, other_muts_col
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, "\n"
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, resistance_col
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, '\n===============================================================')
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#%%===============================================================
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###########################
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# Read file: struct params
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###########################
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#cat("Reading struct params including mcsm:", in_filename_params)
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my_df = read.csv(infile_params, header = T)
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cat("\nInput dimensions:", dim(my_df))
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###########################
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# add foldx outcome category
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# and foldx scaled values
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# This will enable to always have these variables available
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# when calling for plots
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###########################
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#------------------------------
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# adding foldx scaled values
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# scale data b/w -1 and 1
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#------------------------------
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n = which(colnames(my_df) == "ddg"); n
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my_min = min(my_df[,n]); my_min
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my_max = max(my_df[,n]); my_max
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my_df$foldx_scaled = ifelse(my_df[,n] < 0
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, my_df[,n]/abs(my_min)
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, my_df[,n]/my_max)
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# sanity check
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my_min = min(my_df$foldx_scaled); my_min
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my_max = max(my_df$foldx_scaled); my_max
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if (my_min == -1 && my_max == 1){
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cat("\nPASS: foldx ddg successfully scaled b/w -1 and 1"
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, "\nProceeding with assigning foldx outcome category")
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}else{
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cat("\nFAIL: could not scale foldx ddg values"
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, "Aborting!")
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}
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#------------------------------
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# adding foldx outcome category
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# ddg<0 = "Stabilising" (-ve)
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#------------------------------
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c1 = table(my_df$ddg < 0)
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my_df$foldx_outcome = ifelse(my_df$ddg < 0, "Stabilising", "Destabilising")
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c2 = table(my_df$ddg < 0)
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if ( all(c1 == c2) ){
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cat("\nPASS: foldx outcome successfully created")
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}else{
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cat("\nFAIL: foldx outcome could not be created. Aborting!")
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exit()
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}
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###########################
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# extract unique mutation entries
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###########################
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# check for duplicate mutations
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if ( length(unique(my_df$mutationinformation)) != length(my_df$mutationinformation)){
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cat(paste0("\nCAUTION:", " Duplicate mutations identified"
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, "\nExtracting these..."))
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dup_muts = my_df[duplicated(my_df$mutationinformation),]
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dup_muts_nu = length(unique(dup_muts$mutationinformation))
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cat(paste0("\nDim of duplicate mutation df:", nrow(dup_muts)
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, "\nNo. of unique duplicate mutations:", dup_muts_nu
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, "\n\nExtracting df with unique mutations only"))
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my_df_u = my_df[!duplicated(my_df$mutationinformation),]
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}else{
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cat(paste0("\nNo duplicate mutations detected"))
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my_df_u = my_df
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}
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upos = unique(my_df_u$position)
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cat("\nDim of clean df:"); cat(dim(my_df_u))
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cat("\nNo. of unique mutational positions:"); cat(length(upos), "\n")
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###########################
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# extract mutations <10Angstroms and symbols
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###########################
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table(my_df_u$ligand_distance<mcsm_lig_cutoff)
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my_df_u_lig = my_df_u[my_df_u$ligand_distance <mcsm_lig_cutoff,]
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cat(paste0("There are ", nrow(my_df_u_lig), " sites lying within 10", angstroms_symbol, " of the ligand\n"))
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########################################################################
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# end of data extraction and cleaning for plots #
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########################################################################
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} |