rectified mcsm_mean_stability to avereage on raw values and then scale
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2 changed files with 94 additions and 59 deletions
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@ -6,41 +6,29 @@ getwd()
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# TASK:
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#########################################################
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source("Header_TT.R")
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require(data.table)
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require(dplyr)
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#source("Header_TT.R")
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#require(data.table)
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#require(dplyr)
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source("plotting_data.R")
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# should return
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#my_df
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#my_df_u
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#dup_muts
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# cmd parse arguments
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#require('getopt', quietly = TRUE)
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#========================================================
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#========================================================
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# Read file: call script for combining df for PS
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#source("../combining_two_df.R")
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#========================================================
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#%% variable assignment: input and output paths & filenames
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drug = "pyrazinamide"
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gene = "pncA"
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gene_match = paste0(gene,"_p.")
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cat(gene_match)
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#=============
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# directories
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#=============
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datadir = paste0("~/git/Data")
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indir = paste0(datadir, "/", drug, "/input")
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outdir = paste0("~/git/Data", "/", drug, "/output")
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#======
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# input
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#======
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#in_filename = "mcsm_complex1_normalised.csv"
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in_filename_params = paste0(tolower(gene), "_all_params.csv")
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infile_params = paste0(outdir, "/", in_filename_params)
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cat(paste0("Input file 1:", infile_params) )
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# plotting_data.R imports all the dir names, etc
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#=======
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# output
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@ -60,61 +48,108 @@ rm(my_df)
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###########################
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# Data for bfactor figure
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# PS average
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# Lig average
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# PS (duet) average
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# Ligand affinity average
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###########################
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head(df$position); head(df$mutationinformation)
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head(df$duet_scaled)
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head(df$duet_stability_change)
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# order data frame
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#df = df[order(df$position),] #already done
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head(df$position); head(df$mutationinformation)
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head(df$duet_scaled)
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#head(df$position); head(df$mutationinformation)
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#head(df$duet_stability_change)
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#***********
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# PS: average by position
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# PS(duet): average by position and then scale b/w -1 and 1
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# column to average: duet_stability_change (NOT scaled!)
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#***********
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mean_duet_by_position <- df %>%
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group_by(position) %>%
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summarize(averaged.duet = mean(duet_scaled))
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summarize(averaged_duet = mean(duet_stability_change))
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# scale b/w -1 and 1
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duet_min = min(mean_duet_by_position['averaged_duet'])
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duet_max = max(mean_duet_by_position['averaged_duet'])
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# scale the averaged_duet values
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mean_duet_by_position['averaged_duet_scaled'] = lapply(mean_duet_by_position['averaged_duet']
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, function(x) ifelse(x < 0, x/abs(duet_min), x/duet_max))
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cat(paste0('Average duet scores:\n', head(mean_duet_by_position['averaged_duet'])
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, '\n---------------------------------------------------------------'
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, '\nScaled duet scores:\n', head(mean_duet_by_position['averaged_duet_scaled'])))
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# sanity checks
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l_bound_duet = min(mean_duet_by_position['averaged_duet_scaled'])
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u_bound_duet = max(mean_duet_by_position['averaged_duet_scaled'])
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if ( (l_bound_duet == -1) && (u_bound_duet == 1) ){
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cat(paste0("PASS: duet scores averaged by position and then scaled"
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, "\nmin averaged duet: ", l_bound_duet
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, "\nmax averaged duet: ", u_bound_duet))
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}else{
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cat(paste0("FAIL: avergaed duet scores could not be scaled b/w -1 and 1"
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, "\nmin averaged duet: ", l_bound_duet
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, "\nmax averaged duet: ", u_bound_duet))
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quit()
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}
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#***********
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# Lig: average by position
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# Lig: average by position and then scale b/w -1 and 1
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# column: ligand_affinity_change (NOT scaled!)
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#***********
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mean_affinity_by_position <- df %>%
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group_by(position) %>%
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summarize(averaged.affinity = mean(affinity_scaled))
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summarize(averaged_affinity = mean(ligand_affinity_change))
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# scale b/w -1 and 1
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affinity_min = min(mean_affinity_by_position['averaged_affinity'])
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affinity_max = max(mean_affinity_by_position['averaged_affinity'])
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# scale the averaged_affinity values
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mean_affinity_by_position['averaged_affinity_scaled'] = lapply(mean_affinity_by_position['averaged_affinity']
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, function(x) ifelse(x < 0, x/abs(affinity_min), x/affinity_max))
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cat(paste0('Average affinity scores:\n', head(mean_affinity_by_position['averaged_affinity'])
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, '\n---------------------------------------------------------------'
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, '\nScaled affinity scores:\n', head(mean_affinity_by_position['averaged_affinity_scaled'])))
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# sanity checks
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l_bound_affinity = min(mean_affinity_by_position['averaged_affinity_scaled'])
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u_bound_affinity = max(mean_affinity_by_position['averaged_affinity_scaled'])
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if ( (l_bound_affinity == -1) && (u_bound_affinity == 1) ){
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cat(paste0("PASS: affinity scores averaged by position and then scaled"
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, "\nmin averaged affintiy: ", l_bound_affinity
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, "\nmax averaged affintiy: ", u_bound_affinity))
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}else{
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cat(paste0("FAIL: avergaed affinity scores could not be scaled b/w -1 and 1"
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, "\nmin averaged affintiy: ", l_bound_affinity
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, "\nmax averaged affintiy: ", u_bound_affinity))
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quit()
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}
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#***********
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# cbind:mean_duet_by_position and mean_affinity_by_position
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# merge: mean_duet_by_position and mean_affinity_by_position
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#***********
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common_cols = intersect(colnames(mean_duet_by_position), colnames(mean_affinity_by_position))
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combined = as.data.frame(cbind(mean_duet_by_position, mean_affinity_by_position ))
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if (dim(mean_duet_by_position) && dim(mean_affinity_by_position)){
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print(paste0("PASS: dim's match, mering dfs by column :", common_cols))
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#combined = as.data.frame(cbind(mean_duet_by_position, mean_affinity_by_position ))
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combined_df = as.data.frame(merge(mean_duet_by_position
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, mean_affinity_by_position
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, by = common_cols
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, all = T))
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# sanity check
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# mean_PS_affinity_Bfactor
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colnames(combined)
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colnames(combined) = c("position"
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, "average_duet_scaled"
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, "position2"
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, "average_affinity_scaled")
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colnames(combined)
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identical(combined$position, combined$position2)
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n = which(colnames(combined) == "position2"); n
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combined_df = combined[,-n]
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max(combined_df$average_duet_scaled) ; min(combined_df$average_duet_scaled)
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max(combined_df$average_affinity_scaled) ; min(combined_df$average_affinity_scaled)
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head(combined_df$position); tail(combined_df$position)
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cat(paste0("\nnrows combined_df:", nrow(combined_df)
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, "\nnrows combined_df:", ncol(combined_df)))
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}else{
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cat(paste0("FAIL: dim's mismatch, aborting cbind!"
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, "\nnrows df1:", nrow(mean_duet_by_position)
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, "\nnrows df2:", nrow(mean_affinity_by_position)))
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quit()
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}
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#%%============================================================
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# output
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write.csv(combined_df, outfile_mean_stability
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@ -79,7 +79,7 @@ if ( length(unique(my_df$mutationinformation)) != length(my_df$mutationinformati
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upos = unique(my_df_u$position)
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cat("Dim of clean df:"); cat(dim(my_df_u))
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cat("\nNo. of unique mutational positions:"); cat(length(upos))
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cat("\nNo. of unique mutational positions:"); cat(length(upos), "\n")
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########################################################################
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# end of data extraction and cleaning for plots #
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