added scripts
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10 changed files with 147 additions and 1014 deletions
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@ -1,46 +1,19 @@
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#!/usr/bin/env Rscript
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source("~/git/LSHTM_analysis/config/gid.R")
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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#########################################################
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# TASK: Replace B-factors in the pdb file with the mean
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# normalised stability values.
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# read pdb file
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# read mcsm mean stability value files
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# extract the respective mean values and assign to the
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# b-factor column within their respective pdbs
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# generate some distribution plots for inspection
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# normalised ppi2 values.
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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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cat(c(getwd(),"\n"))
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#source("~/git/LSHTM_analysis/scripts/Header_TT.R")
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library(bio3d)
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require("getopt", quietly = TRUE) # cmd parse arguments
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#========================================================
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#drug = "pyrazinamide"
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#gene = "pncA"
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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$drug
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gene = opt$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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gene_match = paste0(gene,"_p.")
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cat(gene)
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gene_match = paste0(gene,"_p."); cat(gene_match)
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cat(gene_match)
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#=============
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@ -49,9 +22,13 @@ cat(gene_match)
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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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#outdir_plots = paste0("~/git/Data", "/", drug, "/output/plots")
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outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
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#outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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cat("plots will output to:", outdir_images)
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#======
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# input
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#======
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@ -59,30 +36,33 @@ in_filename_pdb = paste0(tolower(gene), "_complex.pdb")
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infile_pdb = paste0(indir, "/", in_filename_pdb)
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cat(paste0("Input file:", infile_pdb) )
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# mean ppi2
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in_filename_mean_ppi2 = paste0(tolower(gene), "_mean_ppi2.csv")
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infile_mean_ppi2 = paste0(outdir_plots, "/", in_filename_mean_ppi2)
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cat(paste0("Input file:", infile_mean_ppi2) )
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#=======
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# output
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#=======
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#out_filename_duet_mspdb = paste0(tolower(gene), "_complex_bduet_ms.pdb")
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out_filename_ppi2_mspdb = paste0(tolower(gene), "_complex_b_ppi2_ms.pdb")
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outfile_ppi2_mspdb = paste0(outdir_plots, "/", out_filename_ppi2_mspdb)
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outfile_ppi2_mspdb = paste0(outdir_images,out_filename_ppi2_mspdb)
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print(paste0("Output file:", outfile_ppi2_mspdb))
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#%%===============================================================
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#NOTE: duet here refers to the ensemble stability values
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#NOTE: duet here refers to the ensemble ppi2 values
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###########################
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# Read file: average stability values
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# Read file: average ppi2 values
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# or mcsm_normalised file
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###########################
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my_df <- read.csv(infile_mean_ppi2, header = T)
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str(my_df)
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my_df_raw = merged_df3[, c("position", "mcsm_ppi2_scaled", "interface_dist")]
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head(my_df_raw)
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my_df_raw = my_df_raw[my_df_raw$interface_dist<10,]
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my_df_raw$position
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# avg by position on the SCALED values
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my_df <- my_df_raw %>%
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group_by(position) %>%
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summarize(avg_ppi2_sc_pos = mean(mcsm_ppi2_scaled))
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max(my_df$avg_ppi2_sc_pos)
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min(my_df$avg_ppi2_sc_pos)
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#============================================================
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#############
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# Read pdb
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#############
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@ -100,7 +80,7 @@ my_pdb = read.pdb(infile_pdb
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my_pdb_duet = my_pdb
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#=========================================================
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# Replacing B factor with mean stability scores
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# Replacing B factor with mean ppi2 scores
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# within the respective dfs
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#==========================================================
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# extract atom list into a variable
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@ -117,8 +97,8 @@ max(df_duet$b); min(df_duet$b)
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#==================================================
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# histograms and density plots for inspection
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# 1: original B-factors
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# 2: original mean stability values
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# 3: replaced B-factors with mean stability values
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# 2: original mean ppi2 values
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# 3: replaced B-factors with mean ppi2 values
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#==================================================
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# Set the margin on all sides
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par(oma = c(3,2,3,0)
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@ -129,7 +109,7 @@ par(oma = c(3,2,3,0)
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#=============
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# Row 1 plots: original B-factors
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# duet and affinity
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# duet and ppi2
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#=============
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hist(df_duet$b
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, xlab = ""
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@ -140,40 +120,24 @@ plot(density(df_duet$b)
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, main = "Bfactor ppi2")
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#=============
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# Row 2 plots: original mean stability values
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# duet and affinity
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# Row 2 plots: original mean ppi2 values
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# ppi2
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#=============
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#hist(my_df$averaged_duet
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hist(my_df$avg_ppi2_scaled
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hist(my_df$avg_ppi2_sc_pos
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, xlab = ""
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, main = "mean ppi2 values")
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#plot(density(my_df$averaged_duet)
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plot(density(my_df$avg_ppi2_scaled)
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plot(density(my_df$avg_ppi2_sc_pos)
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, xlab = ""
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, main = "mean ppi2 values")
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#==============
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# Row 3 plots: replaced B-factors with mean stability values
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# After actual replacement in the b factor column
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#===============
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################################################################
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#=========
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# step 0_P1: DONT RUN once you have double checked the matched output
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#=========
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# sanity check: match and assign to a separate column to double check
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# colnames(my_df)
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# df_duet$duet_scaled = my_df$averge_duet_scaled[match(df_duet$resno, my_df$position)]
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#=========
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# step 1_P1
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#=========
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# Be brave and replace in place now (don"t run sanity check)
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# this makes all the B-factor values in the non-matched positions as NA
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#df_duet$b = my_df$averaged_duet_scaled[match(df_duet$resno, my_df$position)]
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df_duet$b = my_df$avg_ppi2_scaled[match(df_duet$resno, my_df$position)]
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df_duet$b = my_df$avg_ppi2_sc_pos[match(df_duet$resno, my_df$position)]
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#=========
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# step 2_P1
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@ -188,32 +152,6 @@ sum(df_duet$b == 0)
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na_rep = 2
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df_duet$b[is.na(df_duet$b)] = na_rep
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# # sanity check: should be 0 and True
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# # duet and lig
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# if ( (sum(df_duet$b == na_rep) == b_na_duet) && (sum(df_lig$b == na_rep) == b_na_lig) ) {
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# print ("PASS: NA's replaced with 0s successfully in df_duet and df_lig")
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# } else {
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# print("FAIL: NA replacement in df_duet NOT successful")
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# quit()
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# }
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#
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# max(df_duet$b); min(df_duet$b)
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#
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# # sanity checks: should be True
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# if( (max(df_duet$b) == max(my_df$avg_ens_stability_scaled)) & (min(df_duet$b) == min(my_df$avg_ens_stability_scaled)) ){
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# print("PASS: B-factors replaced correctly in df_duet")
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# } else {
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# print ("FAIL: To replace B-factors in df_duet")
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# quit()
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# }
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# if( (max(df_lig$b) == max(my_df$avg_ens_affinity_scaled)) & (min(df_lig$b) == min(my_df$avg_ens_affinity_scaled)) ){
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# print("PASS: B-factors replaced correctly in df_lig")
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# } else {
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# print ("FAIL: To replace B-factors in df_lig")
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# quit()
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# }
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#=========
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# step 3_P1
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#=========
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@ -237,17 +175,23 @@ table(df_duet$b)
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sum(is.na(df_duet$b))
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#=========
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# step 5_P1
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# step 5_P1: OUTPUT
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#=========
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cat(paste0("output file mean ppi2 pdb:"
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, outfile_ppi2_mspdb))
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cat(paste0("output file duet mean ppi2 pdb:", outfile_ppi2_mspdb))
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write.pdb(my_pdb_duet, outfile_ppi2_mspdb)
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# OUTPUT: position file
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poscsvF = paste0(outdir_images, tolower(gene), "_ppi2_positions.csv")
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cat(paste0("output file duet mean ppi2 POSITIONS:", poscsvF))
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filtered_pos = toString(my_df$position)
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write.table(filtered_pos, poscsvF, row.names = F, col.names = F )
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#============================
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# Add the 3rd histogram and density plots for comparisons
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#============================
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# Plots continued...
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# Row 3 plots: hist and density of replaced B-factors with stability values
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# Row 3 plots: hist and density of replaced B-factors with ppi2 values
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hist(df_duet$b
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, xlab = ""
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, main = "repalcedB duet")
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@ -266,12 +210,4 @@ mtext(text = paste0(tolower(gene), ": ppi2 distribution")
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, side = 3
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, line = 0
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, outer = TRUE)
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#============================================
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# NOTE: This replaced B-factor distribution has the same
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# x-axis as the PredAff normalised values, but the distribution
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# is affected since 0 is overinflated/or hs an additional blip because
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# of the positions not associated with resistance. This is because all the positions
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# where there are no SNPs have been assigned 0???
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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#============================================
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