451 lines
14 KiB
R
451 lines
14 KiB
R
#!/usr/bin/env Rscript
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#########################################################
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# TASK: Get formatted data for plots
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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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source("../functions/my_pairs_panel.R") # with lower panel turned off
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source("../functions/plotting_globals.R")
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source("../functions/plotting_data.R")
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source("../functions/combining_dfs_plotting.R")
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#********************
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# cmd args passed
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# in from other scripts
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# to call this
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#********************
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#drug = 'streptomycin'
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#gene = 'gid'
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#====================
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# variables for lig
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#====================
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LigDist_colname = "ligand_distance"
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LigDist_cutoff = 20
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#===========
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# input
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#===========
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#---------------------
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# call: import_dirs()
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#---------------------
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import_dirs(drug, gene)
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#---------------------------
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# call: plotting_data()
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#---------------------------
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#if (!exists("infile_params") && exists("gene")){
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if (!is.character(infile_params) && exists("gene")){ # when running as cmd
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#in_filename_params = paste0(tolower(gene), "_all_params.csv")
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in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid
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infile_params = paste0(outdir, "/", in_filename_params)
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cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
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}
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# Input 1: read <gene>_comb_afor.csv
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cat("\nReading mcsm combined data file: ", infile_params)
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mcsm_df = read.csv(infile_params, header = T)
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pd_df = plotting_data(mcsm_df
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, lig_dist_colname = LigDist_colname
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, lig_dist_cutoff = LigDist_cutoff)
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my_df = pd_df[[1]]
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my_df_u = pd_df[[2]] # this forms one of the input for combining_dfs_plotting()
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my_df_u_lig = pd_df[[3]]
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dup_muts = pd_df[[4]]
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#--------------------------------
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# call: combining_dfs_plotting()
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#--------------------------------
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#if (!exists("infile_metadata") && exists("gene")){
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if (!is.character(infile_metadata) && exists("gene")){ # when running as cmd
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in_filename_metadata = paste0(tolower(gene), "_metadata.csv") # part combined for gid
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infile_metadata = paste0(outdir, "/", in_filename_metadata)
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cat("\nInput file for gene metadata not specified, assuming filename: ", infile_metadata, "\n")
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}
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# Input 2: read <gene>_meta data.csv
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cat("\nReading meta data file: ", infile_metadata)
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gene_metadata <- read.csv(infile_metadata
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, stringsAsFactors = F
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, header = T)
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all_plot_dfs = combining_dfs_plotting(my_df_u
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, gene_metadata
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, lig_dist_colname = LigDist_colname
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, lig_dist_cutoff = LigDist_cutoff)
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merged_df2 = all_plot_dfs[[1]]
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merged_df3 = all_plot_dfs[[2]]
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merged_df2_comp = all_plot_dfs[[3]]
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merged_df3_comp = all_plot_dfs[[4]]
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merged_df2_lig = all_plot_dfs[[5]]
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merged_df3_lig = all_plot_dfs[[6]]
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merged_df2_comp_lig = all_plot_dfs[[7]]
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merged_df3_comp_lig = all_plot_dfs[[8]]
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####################################################################
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# Data for logoplots
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####################################################################
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#-------------------------
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# choose df for logoplot
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#-------------------------
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logo_data = merged_df3
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#logo_data = merged_df3_comp
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# quick checks
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colnames(logo_data)
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str(logo_data)
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c1 = unique(logo_data$position)
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nrow(logo_data)
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cat("No. of rows in my_data:", nrow(logo_data)
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, "\nDistinct positions corresponding to snps:", length(c1)
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, "\n===========================================================")
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#=======================================================================
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#==================
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# logo data: OR
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#==================
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foo = logo_data[, c("position"
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, "mutant_type","duet_scaled", "or_mychisq"
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, "mut_prop_polarity", "mut_prop_water")]
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logo_data$log10or = log10(logo_data$or_mychisq)
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logo_data_plot = logo_data[, c("position"
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, "mutant_type", "or_mychisq", "log10or")]
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logo_data_plot_or = logo_data[, c("position", "mutant_type", "or_mychisq")]
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wide_df_or <- logo_data_plot_or %>% spread(position, or_mychisq, fill = 0.0)
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wide_df_or = as.matrix(wide_df_or)
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rownames(wide_df_or) = wide_df_or[,1]
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dim(wide_df_or)
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wide_df_or = wide_df_or[,-1]
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str(wide_df_or)
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position_or = as.numeric(colnames(wide_df_or))
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#==================
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# logo data: logOR
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#==================
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logo_data_plot_logor = logo_data[, c("position", "mutant_type", "log10or")]
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wide_df_logor <- logo_data_plot_logor %>% spread(position, log10or, fill = 0.0)
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wide_df_logor = as.matrix(wide_df_logor)
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rownames(wide_df_logor) = wide_df_logor[,1]
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wide_df_logor = subset(wide_df_logor, select = -c(1) )
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colnames(wide_df_logor)
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wide_df_logor_m = data.matrix(wide_df_logor)
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rownames(wide_df_logor_m)
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colnames(wide_df_logor_m)
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position_logor = as.numeric(colnames(wide_df_logor_m))
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#===============================
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# logo data: multiple nsSNPs (>1)
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#=================================
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#require(data.table)
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# get freq count of positions so you can subset freq<1
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setDT(logo_data)[, mut_pos_occurrence := .N, by = .(position)]
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table(logo_data$position)
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table(logo_data$mut_pos_occurrence)
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max_mut = max(table(logo_data$position))
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# extract freq_pos > 1
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my_data_snp = logo_data[logo_data$mut_pos_occurrence!=1,]
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u = unique(my_data_snp$position)
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max_mult_mut = max(table(my_data_snp$position))
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if (nrow(my_data_snp) == nrow(logo_data) - table(logo_data$mut_pos_occurrence)[[1]] ){
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cat("PASS: positions with multiple muts extracted"
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, "\nNo. of mutations:", nrow(my_data_snp)
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, "\nNo. of positions:", length(u)
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, "\nMax no. of muts at any position", max_mult_mut)
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}else{
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cat("FAIL: positions with multiple muts could NOT be extracted"
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, "\nExpected:",nrow(logo_data) - table(logo_data$mut_pos_occurrence)[[1]]
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, "\nGot:", nrow(my_data_snp) )
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}
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cat("\nNo. of sites with only 1 mutations:", table(logo_data$mut_pos_occurrence)[[1]])
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#--------------------------------------
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# matrix for_mychisq mutant type
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# frequency of mutant type by position
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#---------------------------------------
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table(my_data_snp$mutant_type, my_data_snp$position)
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tab_mt = table(my_data_snp$mutant_type, my_data_snp$position)
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class(tab_mt)
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# unclass to convert to matrix
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tab_mt = unclass(tab_mt)
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tab_mt = as.matrix(tab_mt, rownames = T)
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# should be TRUE
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is.matrix(tab_mt)
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rownames(tab_mt) #aa
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colnames(tab_mt) #pos
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#-------------------------------------
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# matrix for wild type
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# frequency of wild type by position
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#-------------------------------------
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tab_wt = table(my_data_snp$wild_type, my_data_snp$position); tab_wt
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tab_wt = unclass(tab_wt)
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# remove wt duplicates
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wt = my_data_snp[, c("position", "wild_type")]
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wt = wt[!duplicated(wt),]
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tab_wt = table(wt$wild_type, wt$position); tab_wt # should all be 1
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rownames(tab_wt)
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rownames(tab_wt)
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identical(colnames(tab_mt), colnames(tab_wt))
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identical(ncol(tab_mt), ncol(tab_wt))
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#----------------------------------
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# logo data OR: multiple nsSNPs (>1)
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#----------------------------------
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logo_data_or_mult = my_data_snp[, c("position", "mutant_type", "or_mychisq")]
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#wide_df_or <- logo_data_or %>% spread(position, or_mychisq, fill = 0.0)
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wide_df_or_mult <- logo_data_or_mult %>% spread(position, or_mychisq, fill = NA)
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wide_df_or_mult = as.matrix(wide_df_or_mult)
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rownames(wide_df_or_mult) = wide_df_or_mult[,1]
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wide_df_or_mult = wide_df_or_mult[,-1]
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str(wide_df_or_mult)
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position_or_mult = as.numeric(colnames(wide_df_or_mult))
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####################################################################
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# Data for Corrplots
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####################################################################
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cat("\n=========================================="
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, "\nCORR PLOTS data: PS"
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, "\n===========================================")
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df_ps = merged_df2
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#--------------------
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# adding log cols
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#--------------------
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df_ps$log10_or_mychisq = log10(df_ps$or_mychisq)
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df_ps$neglog_pval_fisher = -log10(df_ps$pval_fisher)
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##df_ps$log10_or_kin = log10(df_ps$or_kin)
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##df_ps$neglog_pwald_kin = -log10(df_ps$pwald_kin)
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#df_ps$mutation_info_labels = ifelse(df_ps$mutation_info == dr_muts_col, 1, 0)
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#----------------------------
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# columns for corr plots:PS
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#----------------------------
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# subset data to generate pairwise correlations
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cols_to_select = c("mutationinformation"
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, "duet_scaled"
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, "foldx_scaled"
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#, "mutation_info_labels"
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, "asa"
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, "rsa"
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, "rd_values"
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, "kd_values"
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, "log10_or_mychisq"
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, "neglog_pval_fisher"
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##, "or_kin"
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##, "neglog_pwald_kin"
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, "af"
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##, "af_kin"
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, "duet_outcome"
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, drug)
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corr_data_ps = df_ps[cols_to_select]
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dim(corr_data_ps)
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#--------------------------------------
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# assign nice colnames (for display)
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#--------------------------------------
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my_corr_colnames = c("Mutation"
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, "DUET"
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, "Foldx"
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#, "Mutation class"
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, "ASA"
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, "RSA"
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, "RD"
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, "KD"
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, "Log (OR)"
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, "-Log (P)"
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##, "Adjusted (OR)"
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##, "-Log (P wald)"
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, "MAF"
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##, "AF_kin"
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, "duet_outcome"
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, drug)
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length(my_corr_colnames)
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colnames(corr_data_ps)
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colnames(corr_data_ps) <- my_corr_colnames
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colnames(corr_data_ps)
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start = 1
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end = which(colnames(corr_data_ps) == drug); end # should be the last column
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offset = 1
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#===========================
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# Corr data for plots: PS
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# big_df ps: ~ merged_df2
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#===========================
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#corr_ps_df2 = corr_data_ps[start:(end-offset)] # without drug
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corr_ps_df2 = corr_data_ps[start:end]
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head(corr_ps_df2)
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#===========================
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# Corr data for plots: PS
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# short_df ps: ~merged_df3
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#===========================
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corr_ps_df3 = corr_ps_df2[!duplicated(corr_ps_df2$Mutation),]
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na_or = sum(is.na(corr_ps_df3$`Log (OR)`))
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check1 = nrow(corr_ps_df3) - na_or
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##na_adj_or = sum(is.na(corr_ps_df3$`adjusted (OR)`))
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##check2 = nrow(corr_ps_df3) - na_adj_or
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if (nrow(corr_ps_df3) == nrow(merged_df3) && nrow(merged_df3_comp) == check1) {
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cat( "\nPASS: No. of rows for corr_ps_df3 match"
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, "\nPASS: No. of OR values checked: " , check1)
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} else {
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cat("\nFAIL: Numbers mismatch:"
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, "\nExpected nrows: ", nrow(merged_df3)
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, "\nGot: ", nrow(corr_ps_df3)
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, "\nExpected OR values: ", nrow(merged_df3_comp)
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, "\nGot: ", check1)
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}
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#=================================
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# Data for Correlation plots: LIG
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#=================================
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cat("\n=========================================="
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, "\nCORR PLOTS data: PS"
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, "\n===========================================")
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df_lig = merged_df2_lig
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table(df_lig$ligand_outcome)
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#--------------------
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# adding log cols
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#--------------------
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df_lig$log10_or_mychisq = log10(df_lig$or_mychisq)
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df_lig$neglog_pval_fisher = -log10(df_lig$pval_fisher)
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##df_lig$log10_or_kin = log10(df_lig$or_kin)
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##df_lig$neglog_pwald_kin = -log10(df_lig$pwald_kin)
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#----------------------------
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# columns for corr plots:PS
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#----------------------------
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# subset data to generate pairwise correlations
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cols_to_select = c("mutationinformation"
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, "affinity_scaled"
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#, "mutation_info_labels"
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, "asa"
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, "rsa"
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, "rd_values"
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, "kd_values"
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, "log10_or_mychisq"
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, "neglog_pval_fisher"
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##, "or_kin"
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##, "neglog_pwald_kin"
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, "af"
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##, "af_kin"
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, "ligand_outcome"
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, drug)
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corr_data_lig = df_lig[, cols_to_select]
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dim(corr_data_lig)
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#--------------------------------------
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# assign nice colnames (for display)
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#--------------------------------------
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my_corr_colnames = c("Mutation"
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, "Ligand Affinity"
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#, "Mutation class"
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, "ASA"
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, "RSA"
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, "RD"
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, "KD"
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, "Log (OR)"
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, "-Log (P)"
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##, "Adjusted (OR)"
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##, "-Log (P wald)"
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, "MAF"
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##, "MAF_kin"
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, "ligand_outcome"
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, drug)
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length(my_corr_colnames)
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colnames(corr_data_lig)
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colnames(corr_data_lig) <- my_corr_colnames
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colnames(corr_data_lig)
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start = 1
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end = which(colnames(corr_data_lig) == drug); end # should be the last column
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offset = 1
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#=============================
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# Corr data for plots: LIG
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# big_df lig: ~ merged_df2_lig
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#==============================
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#corr_lig_df2 = corr_data_lig[start:(end-offset)] # without drug
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corr_lig_df2 = corr_data_lig[start:end]
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head(corr_lig_df2)
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#=============================
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# Corr data for plots: LIG
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# short_df lig: ~ merged_df3_lig
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#==============================
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corr_lig_df3 = corr_lig_df2[!duplicated(corr_lig_df2$Mutation),]
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na_or_lig = sum(is.na(corr_lig_df3$`Log (OR)`))
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check1_lig = nrow(corr_lig_df3) - na_or_lig
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if (nrow(corr_lig_df3) == nrow(merged_df3_lig) && nrow(merged_df3_comp_lig) == check1_lig) {
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cat( "\nPASS: No. of rows for corr_lig_df3 match"
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, "\nPASS: No. of OR values checked: " , check1_lig)
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} else {
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cat("\nFAIL: Numbers mismatch:"
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, "\nExpected nrows: ", nrow(merged_df3_lig)
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, "\nGot: ", nrow(corr_ps_df3_lig)
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, "\nExpected OR values: ", nrow(merged_df3_comp_lig)
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, "\nGot: ", check1_lig)
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}
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# remove unnecessary columns
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identical(corr_data_lig, corr_lig_df2)
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identical(corr_data_ps, corr_ps_df2)
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rm(df_ps, df_lig, corr_data_ps, corr_data_lig)
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########################################################################
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# End of script
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########################################################################
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rm(foo)
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