131 lines
3.6 KiB
R
Executable file
131 lines
3.6 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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#source("Header_TT.R")
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library(ggplot2)
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library(data.table)
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library(dplyr)
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source("dirs.R")
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require("getopt", quietly = TRUE) #cmd parse arguments
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#========================================================
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# command line args
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#spec = matrix(c(
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# "drug" , "d", 1, "character",
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# "gene" , "g", 1, "character"
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#), byrow = TRUE, ncol = 4)
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#opt = getopt(spec)
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#drug = opt$druggene = opt$gene
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#if(is.null(drug)|is.null(gene)) {
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# stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
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#}
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#========================================================
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#======
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# input
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#======
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#in_filename = "mcsm_complex1_normalised.csv"
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in_filename_params = paste0(tolower(gene), "_all_params.csv")
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infile_params = paste0(outdir, "/", in_filename_params)
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cat(paste0("Input file 1:", infile_params) )
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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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# 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$ligand_distance<10)
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my_df_u_lig = my_df_u[my_df_u$ligand_distance <10,]
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#==================
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# Angstroms symbol
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#==================
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angstroms_symbol = "\u212b"
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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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# Delta symbol
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#==================
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delta_symbol = "\u0394"; delta_symbol
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###########################
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# variables for my cols
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###########################
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mcsm_red2 = "#ae301e" # most negative
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mcsm_red1 = "#f8766d"
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mcsm_mid = "white" # middle
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mcsm_blue1 = "#00bfc4"
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mcsm_blue2 = "#007d85" # most positive
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########################################################################
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# end of data extraction and cleaning for plots #
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########################################################################
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# clear variables
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rm(opt, spec)
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