moved corr_data and corr_PS_LIG.R to redundant
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Prepare for correlation data
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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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#===========
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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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my_df_u = pd_df[[1]] # this forms one of the input for combining_dfs_plotting()
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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 = 'ligand_distance'
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, lig_dist_cutoff = 10)
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cat(paste0("Directories imported:"
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, "\ndatadir:", datadir
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, "\nindir:", indir
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, "\noutdir:", outdir
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, "\nplotdir:", plotdir))
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cat(paste0("Variables imported:"
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nAngstrom symbol:", angstroms_symbol
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, "\nNo. of duplicated muts:", dup_muts_nu
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, "\nNA count for ORs:", na_count
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, "\nNA count in df2:", na_count_df2
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, "\nNA count in df3:", na_count_df3))
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#=======
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# output
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#=======
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# corr_ps_df2
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# corr_lig_df2
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####################################################################
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# end of loading libraries and functions
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####################################################################
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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#df_ps = merged_df3
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df_ps = merged_df2
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#df_lig = merged_df3_lig
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df_lig = merged_df2_lig
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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########################################################################
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# end of data extraction and cleaning for plots #
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########################################################################
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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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# Data for Correlation 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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# assign nice colnames (for display)
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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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, "AF"
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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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#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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# 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$`adjusted (OR)`))
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check2 = nrow(corr_ps_df3) - na_adj_or
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#if ( nrow(corr_ps_df3) == nrow(merged_df3) ) {
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# cat( "PASS: No. of rows for corr_ps_df3 match" )
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#}if ( nrow(merged_df3_comp) == check1 ){
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# cat( "PASS: No. of OR values checked" )
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#}
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################################################################################################
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#=================================
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# Data for Correlation plots: LIG
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#=================================
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table(df_lig$ligand_outcome)
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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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# 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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# assign nice colnames (for display)
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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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, "AF"
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, "AF_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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#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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# 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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#######################################################
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rm(merged_df2, merged_df2_lig, merged_df3, merged_df3_lig
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, merged_df2_comp , merged_df3_comp, merged_df2_comp_lig, merged_df3_comp_lig
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, corr_data_ps, corr_data_lig)
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