240 lines
6.8 KiB
R
240 lines
6.8 KiB
R
getwd()
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setwd('~/git/LSHTM_analysis/scripts/plotting')
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getwd()
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#########################################################
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# TASK:
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#########################################################
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########################################################################
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# Installing and loading required packages and functions #
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########################################################################
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#source('Header_TT.R')
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#source('barplot_colour_function.R')
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########################################################################
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# Read file: call script for combining df for PS #
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########################################################################
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#?????????????
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#
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########################################################
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#%% variable assignment: input and output paths & filenames
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drug = 'pyrazinamide'
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gene = 'pncA'
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gene_match = paste0(gene,'_p.')
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cat(gene_match)
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#=============
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# directories
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#=============
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datadir = paste0('~/git/Data')
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indir = paste0(datadir, '/', drug, '/input')
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outdir = paste0('~/git/Data', '/', drug, '/output')
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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:', infile_params) )
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#=======
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# output
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#=======
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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
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#, stringsAsFactors = F
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, header = T)
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cat('Input dimensions:', dim(my_df))
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# clear variables
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rm(in_filename_params, infile_params)
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# quick checks
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colnames(my_df)
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str(my_df)
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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('CAUTION:', ' 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$mutationinformation),]
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}else{
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cat(paste0('No duplicate mutations detected'))
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my_df_u = my_df
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}
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#upos = unique(my_df_u$position)
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cat('Dim of clean df:'); cat(dim(my_df_u))
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cat('\nNo. of unique mutational positions:'); cat(length(unique(my_df_u$position)))
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#======================================================
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# create a new df with unique position numbers and cols
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position = unique(my_df$position) #130
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position_cols = as.data.frame(position)
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head(position_cols) ; tail(position_cols)
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# specify active site residues and bg colour
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position = c(49, 51, 57, 71
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, 8, 96, 138
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, 13, 68
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, 103, 137
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, 133, 134) #13
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lab_bg = rep(c("purple"
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, "yellow"
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, "cornflowerblue"
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, "blue"
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, "green"), times = c(4, 3, 2, 2, 2)
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)
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# second bg colour for active site residues
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#lab_bg2 = rep(c("white"
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# , "green" , "white", "green"
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# , "white"
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# , "white"
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# , "white"), times = c(4
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# , 1, 1, 1
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# , 2
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# , 2
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# , 2)
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#)
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#%%%%%%%%%
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# revised: leave the second box coloured as the first one incase there is no second colour
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#%%%%%%%%%
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lab_bg2 = rep(c("purple"
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, "green", "yellow", "green"
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, "cornflowerblue"
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, "blue"
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, "green"), times = c(4
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, 1, 1, 1
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, 2
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, 2
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, 2))
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# fg colour for labels for active site residues
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lab_fg = rep(c("white"
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, "black"
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, "black"
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, "white"
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, "black"), times = c(4, 3, 2, 2, 2))
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#%%%%%%%%%
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# revised: make the purple ones black
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# fg colour for labels for active site residues
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#%%%%%%%%%
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#lab_fg = rep(c("black"
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# , "black"
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# , "black"
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# , "white"
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# , "black"), times = c(4, 3, 2, 2, 2))
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# combined df with active sites, bg and fg colours
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aa_cols_ref = data.frame(position
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, lab_bg
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, lab_bg2
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, lab_fg
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, stringsAsFactors = F) #13, 4
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str(position_cols); class(position_cols)
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str(aa_cols_ref); class(aa_cols_ref)
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# since position is int and numeric in the two dfs resp,
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# converting numeric to int for consistency
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aa_cols_ref$position = as.integer(aa_cols_ref$position)
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class(aa_cols_ref$position)
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#===========
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# Merge 1: merging positions df (position_cols) and
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# active site cols (aa_cols_ref)
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# linking column: "position"
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# This is so you can have colours defined for all positions
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#===========
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head(position_cols$position); head(aa_cols_ref$position)
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mut_pos_cols = merge(position_cols, aa_cols_ref
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, by = "position"
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, all.x = TRUE)
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head(mut_pos_cols)
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# replace NA's
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# :column "lab_bg" with "white"
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# : column "lab_fg" with "black"
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mut_pos_cols$lab_bg[is.na(mut_pos_cols$lab_bg)] <- "white"
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mut_pos_cols$lab_bg2[is.na(mut_pos_cols$lab_bg2)] <- "white"
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mut_pos_cols$lab_fg[is.na(mut_pos_cols$lab_fg)] <- "black"
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head(mut_pos_cols)
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#===========
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# Merge 2: Merge mut_pos_cols with mcsm df
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# Now combined the positions with aa colours with
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# the mcsm_data
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#===========
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# dfs to merge
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df0 = my_df # my_df_o
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df1 = mut_pos_cols
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# check the column on which merge will be performed
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head(df0$position); tail(df0$position)
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head(df1$position); tail(df1$position)
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# should now have 3 extra columns
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my_df = merge(df0, df1
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, by = "position"
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, all.x = TRUE)
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# sanity check
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my_df[my_df$position == "49",]
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my_df[my_df$position == "13",]
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rm(df0, df1)
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#===========
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# Merge 3: Merge mut_pos_cols with mcsm df_u
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# Now combined the positions with aa colours with
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# the mcsm_data
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#===========
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# dfs to merge
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df0 = my_df_u # my_df_u
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df1 = mut_pos_cols
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# check the column on which merge will be performed
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head(df0$position); tail(df0$position)
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head(df1$position); tail(df1$position)
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# should now have 3 extra columns
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my_df_u = merge(df0, df1
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, by = "position"
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, all.x = TRUE)
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# sanity check
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my_df[my_df$position == "49",]
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my_df[my_df$position == "13",]
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# clear variables
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rm(aa_cols_ref
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, df0
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, df1
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, position_cols
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, lab_bg
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, lab_bg2
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, lab_fg
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, position
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, dup_muts)
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