added plotting/other_plots_data.R
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1262df40c9
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3 changed files with 277 additions and 7 deletions
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@ -76,7 +76,7 @@ print('Input file: ', infile_merged_df3
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#=======
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# output
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#=======
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out_filename = 'mut_elec_changes.txt'
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out_filename = gene.lower() + '_mut_elec_changes.txt'
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outfile_elec_changes = outdir + '/' + out_filename
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print('Output file: ', outfile_elec_changes
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, '\n============================================================')
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@ -35,21 +35,36 @@ write.csv(merged_df3_short, "merged_df3_short.csv")
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#%%%%%%%%%%%%%%%%%%%%
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df_ps = merged_df3
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# adding folds scaled values
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n = which(colnames(df_ps) == "ddg"); n # 10
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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(df_ps) == "ddg"); n
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my_min = min(df_ps[,n]); my_min
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my_max = max(df_ps[,n]); my_max
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df_ps$foldx_scaled = ifelse(df_ps[,n] < 0
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, df_ps[,n]/abs(my_min)
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, df_ps[,n]/my_max) #335, 15
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, df_ps[,n]/my_max)
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# sanity check
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my_min = min(df_ps$foldx_scaled); my_min
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my_max = max(df_ps$foldx_scaled); my_max
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# assign foldx
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#ddg<0 = "Stabilising" (-ve)
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if (my_min == -1 && my_max == 1){
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cat("PASS: foldx ddg successfully scaled b/w -1 and 1"
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, "\nProceeding with assigning foldx outcome category")
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}else{
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cat("FAIL: could not scale foldx ddg values"
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, "Aborting!")
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}
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#================================
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# adding foldx outcome category
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# ddg<0 = "Stabilising" (-ve)
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#=================================
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c1 = table(df_ps$ddg < 0)
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df_ps$foldx_outcome = ifelse(df_ps$ddg < 0, "Stabilising", "Destabilising")
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c2 = table(df_ps$ddg < 0)
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255
scripts/plotting/output_tables.R
Normal file
255
scripts/plotting/output_tables.R
Normal file
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@ -0,0 +1,255 @@
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: producing boxplots for dr and other muts
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#########################################################
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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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#=========
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# Input
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#=========
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source("combining_dfs_plotting.R")
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rm(merged_df2, merged_df2_comp, merged_df2_lig, merged_df2_comp_lig
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, merged_df3_comp, merged_df3_comp_lig, merged_df3_lig
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, my_df_u, my_df_u_lig)
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#=========
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# Output
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#=========
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mut_landscape_data = paste0(tolower(gene), "_merged_df3_short.csv")
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outfile_mut_landscape_data = paste0(outdir, "/" , mut_landscape_data)
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outfile_mut_landscape_data
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all_muts_table = paste0(tolower(gene), "_table_all_muts.csv")
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outfile_all_muts_table = paste0(outdir, "/" , all_muts_table)
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outfile_all_muts_table
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#########################################################################
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# {RESULT PS: count of dr and other muts}
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table(merged_df3$mutation_info)
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# sanity checks: Ensure dr and other muts are indeed unique
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dr_muts = merged_df3[merged_df3$mutation_info == dr_muts_col,]
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dr_muts_names = unique(dr_muts$mutation)
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other_muts = merged_df3[merged_df3$mutation_info == other_muts_col,]
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other_muts_names = unique(other_muts$mutation)
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if (table(dr_muts_names%in%other_muts_names)[[1]] == length(dr_muts_names) &&
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table(other_muts_names%in%dr_muts_names)[[1]] == length(other_muts_names)){
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cat("PASS: dr and other muts are indeed unique")
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}else{
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cat("FAIL: dr and others muts are NOT unique!")
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quit()
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}
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# {RESULT LIG: count of dr and other muts}
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table(merged_df3$mutation_info[merged_df3$ligand_distance<10])
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#===========
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# Outfile 1: mut_landscape_data
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#===========
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# select columns
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cols_mut_landscape = c("mutation", "mutationinformation"
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, "wild_type", "position", "mutant_type"
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, "mutation_info")
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merged_df3_short = merged_df3[,cols_mut_landscape]
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#-----------
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# write file 1
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#-----------
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#write.csv(merged_df3_short, outfile_mut_landscape_data)
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cat("Mutational landscape csv written:", outfile_mut_landscape_data
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, "\nnrows:", nrow(merged_df3_short)
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, "\nncols:", ncol(merged_df3_short))
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#########################################################################
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#===========
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# Outfile 2: all_muts_table
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#===========
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#%%%%%%%%%%%%%%%%%
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# REASSIGNMENT
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df = merged_df3
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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(df) == "ddg"); n
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my_min = min(df[,n]); my_min
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my_max = max(df[,n]); my_max
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df$foldx_scaled = ifelse(df[,n] < 0
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, df[,n]/abs(my_min)
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, df[,n]/my_max)
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# sanity check
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my_min = min(df$foldx_scaled); my_min
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my_max = max(df$foldx_scaled); my_max
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if (my_min == -1 && my_max == 1){
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cat("PASS: foldx ddg successfully scaled b/w -1 and 1"
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, "\nProceeding with assigning foldx outcome category")
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}else{
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cat("FAIL: could not scale foldx ddg values"
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, "Aborting!")
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}
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#================================
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# adding foldx outcome category
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# ddg<0 = "Stabilising" (-ve)
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#=================================
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c1 = table(df$ddg < 0)
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df$foldx_outcome = ifelse(df$ddg < 0, "Stabilising", "Destabilising")
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c2 = table(df$ddg < 0)
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if ( all(c1 == c2) ){
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cat("PASS: foldx outcome successfully created")
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}else{
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cat("FAIL: foldx outcome could not be created. Aborting!")
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exit()
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}
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#=================================
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# change labels of mut_info column
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#=================================
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df$mutation_info = as.factor(df$mutation_info)
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levels(df$mutation_info);table(df$mutation_info)
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levels(df$mutation_info)[levels(df$mutation_info) == dr_muts_col] <- "drug associated"
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levels(df$mutation_info)[levels(df$mutation_info) == other_muts_col] <- "others"
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levels(df$mutation_info);table(df$mutation_info)
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#================================
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# adding negative log p values and % af
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# pval_fisher AND pwald_kin
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#=================================
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df$af_percent = df$af*100
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df$neglog10_pval_fisher = -log10(df$pval_fisher)
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df$neglog10_pwald_kin = -log10(df$pwald_kin)
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# Order df: by position
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df$position; head(df$mutationinformation)
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df_ordered = df[order(df$position),]
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df_ordered$position;head(df_ordered$mutationinformation)
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# select cols in desired order
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cols_all_muts_table = c("mutationinformation"
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, "mutation_info"
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#, "af"
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, "af_percent"
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, "or_mychisq"
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#, "neglog10_pval_fisher"
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, "pval_fisher"
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, "or_kin"
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#, "neglog10_pwald_kin"
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, "pwald_kin"
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, "duet_stability_change"
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, "duet_outcome"
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, "ligand_distance"
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, "ligand_affinity_change"
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, "ligand_outcome"
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, "ddg"
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, "foldx_outcome"
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, "asa"
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, "rsa"
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, "kd_values"
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, "rd_values")
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cat("\nSelecting", length(cols_all_muts_table), "columns for output"
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, "\nThese are ordered by position")
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if(all(cols_all_muts_table%in% colnames(df_ordered))){
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cat("PASS: columns selected are present"
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, "\nProceeding to subset")
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}else{
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cat("\nFAIL: columns selected are not present in original df"
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, "Aborting!")
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quit()
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}
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df_output = df_ordered[,cols_all_muts_table]
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# Assign pretty column names
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#angstroms_symbol = "\u212b" # already imported but just in case
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delta_symbol = "\u0394"; delta_symbol
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ligand_dist_colname = paste0("Distance to ligand (", angstroms_symbol, ")")
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ligand_dist_colname
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foldx_colname = paste0("Foldx ", delta_symbol, delta_symbol, "G" )
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foldx_colname
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#duet_colname = "DUET (Kcal/Mol)"
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duet_colname = paste0("DUET ", delta_symbol, delta_symbol, "G" )
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duet_colname
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colnames(df_output)
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my_pretty_colnames = c("Mutation"
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, "Mutation class"
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, "AF(%)"
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, "OR"
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, "P-value"
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, "OR adjusted"
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, "P-wald"
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, duet_colname
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, "DUET outcome"
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, ligand_dist_colname
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, "mCSM-lig (log affinity)"
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, "Ligand outcome"
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, foldx_colname
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, "Foldx outcome"
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, "ASA"
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, "RSA"
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, "Hydrophobicity"
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, "Residue depth")
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# MANUAL checkpoint...
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check_colnames = as.data.frame(cbind(colnames(df_output), my_pretty_colnames))
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if (length(my_pretty_colnames) == length(df_output)){
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cat("PASS: pretty colnames generated for output columns"
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, "\nAssigning pretty colnames to output df")
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colnames(df_output) = my_pretty_colnames
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}else{
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cat("FAIL:length of colnames mismatch"
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, "Expected length:", length(df_output)
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, "\nGot:", length(my_pretty_colnames))
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}
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colnames(df_output)
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#-----------
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# write file 2
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#-----------
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#write.csv(df_output, outfile_all_muts_table, row.names = FALSE)
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cat("\nOutput table (csv) written:", outfile_all_muts_table
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, "\nnrows:", nrow(df_output)
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, "\nncols:", ncol(df_output))
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############################################################################
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# clear excess variables
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