separating mcsm_mean_stability_ensemble from combined script
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06e5363112
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2 changed files with 99 additions and 180 deletions
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@ -1,4 +1,9 @@
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source("~/git/LSHTM_analysis/config/pnca.R")
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#source("~/git/LSHTM_analysis/config/alr.R")
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#source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/rpob.R")
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source("/home/tanu/git/LSHTM_analysis/my_header.R")
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#########################################################
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@ -11,10 +16,8 @@ source("/home/tanu/git/LSHTM_analysis/my_header.R")
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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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outfile_mean_ens_st_aff = paste0(outdir_images, "/", tolower(gene)
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, "_mean_ens_stab_aff.csv")
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, "_mean_ens_stability.csv")
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print(paste0("Output file:", outfile_mean_ens_st_aff))
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#%%===============================================================
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@ -24,6 +27,7 @@ print(paste0("Output file:", outfile_mean_ens_st_aff))
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#=============
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df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
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df3 = read.csv(df3_filename)
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length(df3$mutationinformation)
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# mut_info checks
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table(df3$mutation_info)
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@ -57,12 +61,12 @@ common_cols = c("mutationinformation"
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#optional_cols = c()
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all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
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#TODO: affinity_cols
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scaled_cols = c("duet_scaled" , "duet_stability_change"
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,"deepddg_scaled" , "deepddg"
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,"ddg_dynamut2_scaled" , "ddg_dynamut2"
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,"foldx_scaled" , "ddg_foldx"
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, "mcsm_ppi2_scaled" , "mcsm_ppi2_affinity"
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, "mcsm_na_scaled" , "mcsm_na_affinity"
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#,"consurf_scaled" , "consurf_score"
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#,"snap2_scaled" , "snap2_score"
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#,"provean_scaled" , "provean_score"
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@ -81,27 +85,26 @@ outcome_cols = c("duet_outcome"
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#,"snap2_outcome"
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#,"ligand_outcome"
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#,"mmcsm_lig_outcome"
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#, "mcsm_ppi2_outcome"
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#, "mcsm_na_outcome"
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)
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outcome_cols_affinity = c("ligand_outcome"
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,"mmcsm_lig_outcome")
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cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols, scaled_cols, outcome_cols, outcome_cols_affinity)]
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cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols, scaled_cols,outcome_cols)]
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cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols, outcome_cols)]
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foo = df3[, cols_to_consider]
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df3_plot_orig = df3[, cols_to_extract]
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##############################################################
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#####################
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# Ensemble stability
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#####################
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# extract outcome cols and map numeric values to the categories
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# Destabilising == 1, and stabilising == 0
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# Destabilising == 0, and stabilising == 1, so rescaling can let -1 be destabilising
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df3_plot = df3[, cols_to_extract]
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# assign numeric values to outcome
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df3_plot[, outcome_cols] <- sapply(df3_plot[, outcome_cols]
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, function(x){ifelse(x == "Destabilising", 1, 0)})
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, function(x){ifelse(x == "Destabilising", 0, 1)})
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table(df3$duet_outcome)
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table(df3_plot$duet_outcome)
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#=====================================
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# Stability (4 cols): average the scores
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# across predictors ==> average by
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@ -162,119 +165,15 @@ mean_ens_stability_by_position = as.data.frame(mean_ens_stability_by_position)
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# quit()
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# }
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##################################################################
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############################
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# Ensemble affinity: ligand
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############################
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# extract ligand affinity outcome cols and map numeric values to the categories
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# Destabilising == 1, and stabilising == 0
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cols_to_extract_affinity = cols_to_consider[cols_to_consider%in%c(common_cols
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, outcome_cols_affinity)]
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df3_plot_affinity = df3[, cols_to_extract_affinity]
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names(df3_plot_affinity)
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df3_plot_affinity[, outcome_cols_affinity] <- sapply(df3_plot_affinity[, outcome_cols_affinity]
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, function(x){ifelse(x == "Destabilising", 1, 0)})
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#=====================================
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# Affintiy (2 cols): average the scores
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# across predictors ==> average by
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# position ==> scale b/w -1 and 1
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# column to average: ens_affinity
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#=====================================
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cols_to_average_affinity = which(colnames(df3_plot_affinity)%in%outcome_cols_affinity)
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cols_to_average_affinity
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# ensemble average across predictors
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df3_plot_affinity$ens_affinity = rowMeans(df3_plot_affinity[,cols_to_average_affinity])
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head(df3_plot_affinity$position); head(df3_plot_affinity$mutationinformation)
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head(df3_plot_affinity$ens_affinity)
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table(df3_plot_affinity$ens_affinity)
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# ensemble average of predictors by position
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mean_ens_affinity_by_position <- df3_plot_affinity %>%
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dplyr::group_by(position) %>%
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dplyr::summarize(avg_ens_affinity = mean(ens_affinity))
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# REscale b/w -1 and 1
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#en_aff_min = min(mean_ens_affinity_by_position['ens_affinity'])
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#en_aff_max = max(mean_ens_affinity_by_position['ens_affinity'])
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# scale the average affintiy value between -1 and 1
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# mean_ens_affinity_by_position['avg_ens_affinity_scaled'] = lapply(mean_ens_affinity_by_position['avg_ens_affinity']
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# , function(x) ifelse(x < 0, x/abs(en_aff_min), x/en_aff_max))
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mean_ens_affinity_by_position['avg_ens_affinity_scaled'] = lapply(mean_ens_affinity_by_position['avg_ens_affinity']
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, function(x) {
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scales::rescale(x, to = c(-1,1)
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#, from = c(en_aff_min,en_aff_max))
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, from = c(0,1))
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})
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cat(paste0('Average affintiy scores:\n'
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, head(mean_ens_affinity_by_position['avg_ens_affinity'])
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, '\n---------------------------------------------------------------'
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, '\nAverage affintiy scaled scores:\n'
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, head(mean_ens_affinity_by_position['avg_ens_affinity_scaled'])))
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#convert to a df
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mean_ens_affinity_by_position = as.data.frame(mean_ens_affinity_by_position)
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#FIXME: sanity checks
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# TODO: predetermine the bounds
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# l_bound_ens_aff = min(mean_ens_affintiy_by_position['avg_ens_affinity_scaled'])
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# u_bound_ens_aff = max(mean_ens_affintiy_by_position['avg_ens_affinity_scaled'])
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#
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# if ( (l_bound_ens_aff == -1) && (u_bound_ens_aff == 1) ){
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# cat(paste0("PASS: ensemble affinity scores averaged by position and then scaled"
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# , "\nmin ensemble averaged affinity: ", l_bound_ens_aff
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# , "\nmax ensemble averaged affinity: ", u_bound_ens_aff))
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# }else{
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# cat(paste0("FAIL: ensemble affinity scores could not be scaled b/w -1 and 1"
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# , "\nmin ensemble averaged affinity: ", l_bound_ens_aff
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# , "\nmax ensemble averaged affinity: ", u_bound_ens_aff))
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# quit()
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# }
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######################################################################
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##################
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# merge: mean ensemble stability and affinity by_position
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####################
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# if ( class(mean_ens_stability_by_position) && class(mean_ens_affinity_by_position) != "data.frame"){
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# cat("Y")
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# }
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common_cols = intersect(colnames(mean_ens_stability_by_position), colnames(mean_ens_affinity_by_position))
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if (dim(mean_ens_stability_by_position) && dim(mean_ens_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_ens_stability_by_position
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, mean_ens_affinity_by_position
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, by = common_cols
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, all = T))
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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_ens_st_aff
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#write.csv(combined_df, outfile_mean_ens_st_aff
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write.csv(mean_ens_stability_by_position
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, outfile_mean_ens_st_aff
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, row.names = F)
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cat("Finished writing file:\n"
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, outfile_mean_ens_st_aff
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, "\nNo. of rows:", nrow(combined_df)
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, "\nNo. of cols:", ncol(combined_df))
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, "\nNo. of rows:", nrow(mean_ens_stability_by_position)
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, "\nNo. of cols:", ncol(mean_ens_stability_by_position))
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# end of script
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#===============================================================
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@ -10,7 +10,7 @@
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# rendering on chimera
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# read mcsm mean stability value files
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# extract the respecitve mean values and assign to the
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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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@ -52,7 +52,8 @@ 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/Data", "/", drug, "/output/plots")
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outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
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#======
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# input
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@ -61,14 +62,19 @@ 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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in_filename_mean_stability = paste0(tolower(gene), "_mean_stability.csv")
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infile_mean_stability = paste0(outdir, "/", in_filename_mean_stability)
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#in_filename_mean_stability = paste0(tolower(gene), "_mean_stability.csv")
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#infile_mean_stability = paste0(outdir, "/", in_filename_mean_stability)
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in_filename_mean_stability = paste0(tolower(gene), "_mean_ens_stab_aff.csv")
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infile_mean_stability = paste0(outdir_plots, "/", in_filename_mean_stability)
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cat(paste0("Input file:", infile_mean_stability) )
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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_duet_mspdb = paste0(tolower(gene), "_complex_bduet_ms.pdb")
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out_filename_duet_mspdb = paste0(tolower(gene), "_complex_b_stab_ms.pdb")
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outfile_duet_mspdb = paste0(outdir_plots, "/", out_filename_duet_mspdb)
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print(paste0("Output file:", outfile_duet_mspdb))
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@ -77,6 +83,8 @@ outfile_lig_mspdb = paste0(outdir_plots, "/", out_filename_lig_mspdb)
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print(paste0("Output file:", outfile_lig_mspdb))
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#%%===============================================================
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#NOTE: duet here refers to the ensemble stability values
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###########################
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# Read file: average stability values
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# or mcsm_normalised file
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@ -133,17 +141,17 @@ par(oma = c(3,2,3,0)
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#, mfrow = c(3,2)
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, mfrow = c(3,4))
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#************
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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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#************
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#=============
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hist(df_duet$b
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, xlab = ""
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, main = "Bfactor duet")
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, main = "Bfactor stability")
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plot(density(df_duet$b)
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, xlab = ""
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, main = "Bfactor duet")
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, main = "Bfactor stability")
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hist(df_lig$b
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@ -154,32 +162,36 @@ plot(density(df_lig$b)
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, xlab = ""
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, main = "Bfactor affinity")
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#************
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#=============
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# Row 2 plots: original mean stability values
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# duet and affinity
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#************
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hist(my_df$averaged_duet
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#=============
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#hist(my_df$averaged_duet
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hist(my_df$avg_ens_stability_scaled
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, xlab = ""
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, main = "mean duet values")
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, main = "mean stability values")
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plot(density(my_df$averaged_duet)
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#plot(density(my_df$averaged_duet)
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plot(density(my_df$avg_ens_stability_scaled)
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, xlab = ""
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, main = "mean duet values")
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, main = "mean stability values")
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hist(my_df$averaged_affinity
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#hist(my_df$averaged_affinity
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hist(my_df$avg_ens_affinity_scaled
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, xlab = ""
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, main = "mean affinity values")
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plot(density(my_df$averaged_affinity)
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#plot(density(my_df$averaged_affinity)
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plot(density(my_df$avg_ens_affinity_scaled)
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, xlab = ""
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, main = "mean affinity values")
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#************
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#==============
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# Row 3 plots: replaced B-factors with mean stability values
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# After actual replacement in the b factor column
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#*************
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#=========================================================
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#===============
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################################################################
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#=========
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# step 0_P1: DONT RUN once you have double checked the matched output
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#=========
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@ -192,8 +204,12 @@ plot(density(my_df$averaged_affinity)
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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_lig$b = my_df$averaged_affinity_scaled[match(df_lig$resno, my_df$position)]
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#df_duet$b = my_df$averaged_duet_scaled[match(df_duet$resno, my_df$position)]
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#df_lig$b = my_df$averaged_affinity_scaled[match(df_lig$resno, my_df$position)]
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df_duet$b = my_df$avg_ens_stability_scaled[match(df_duet$resno, my_df$position)]
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df_lig$b = my_df$avg_ens_affinity_scaled[match(df_lig$resno, my_df$position)]
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#=========
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# step 2_P1
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@ -207,34 +223,35 @@ sum(df_duet$b == 0)
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sum(df_lig$b == 0)
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# replace all NA in b factor with 0
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df_duet$b[is.na(df_duet$b)] = 0
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df_lig$b[is.na(df_lig$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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df_lig$b[is.na(df_lig$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$b == 0) == b_na_duet) && (sum(df_lig$b == 0) == 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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# # 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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max(df_duet$b); min(df_duet$b)
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# sanity checks: should be True
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if( (max(df_duet$b) == max(my_df$averaged_duet_scaled)) & (min(df_duet$b) == min(my_df$averaged_duet_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$b) == max(my_df$averaged_affinity_scaled)) & (min(df_lig$b) == min(my_df$averaged_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")
|
||||
quit()
|
||||
}
|
||||
# if( (max(df_lig$b) == max(my_df$avg_ens_affinity_scaled)) & (min(df_lig$b) == min(my_df$avg_ens_affinity_scaled)) ){
|
||||
# print("PASS: B-factors replaced correctly in df_lig")
|
||||
# } else {
|
||||
# print ("FAIL: To replace B-factors in df_lig")
|
||||
# quit()
|
||||
# }
|
||||
|
||||
#=========
|
||||
# step 3_P1
|
||||
|
|
@ -255,6 +272,8 @@ if ( (dim(df_duet)[1] == dim(d2_duet)[1]) & (dim(df_lig)[1] == dim(d2_lig)[1]) &
|
|||
# assign it back to the pdb file
|
||||
my_pdb_duet[['atom']] = df_duet
|
||||
max(df_duet$b); min(df_duet$b)
|
||||
table(df_duet$b)
|
||||
sum(is.na(df_duet$b))
|
||||
|
||||
my_pdb_lig[['atom']] = df_lig
|
||||
max(df_lig$b); min(df_lig$b)
|
||||
|
|
@ -268,9 +287,9 @@ write.pdb(my_pdb_duet, outfile_duet_mspdb)
|
|||
cat(paste0("output file ligand mean stability pdb:", outfile_lig_mspdb))
|
||||
write.pdb(my_pdb_lig, outfile_lig_mspdb)
|
||||
|
||||
#********************************
|
||||
#============================
|
||||
# Add the 3rd histogram and density plots for comparisons
|
||||
#********************************
|
||||
#============================
|
||||
# Plots continued...
|
||||
# Row 3 plots: hist and density of replaced B-factors with stability values
|
||||
hist(df_duet$b
|
||||
|
|
@ -296,16 +315,17 @@ mtext(text = "Frequency"
|
|||
, line = 0
|
||||
, outer = TRUE)
|
||||
|
||||
mtext(text = "Stability Distribution"
|
||||
mtext(text = paste0(tolower(gene), ": Stability Distribution")
|
||||
, side = 3
|
||||
, line = 0
|
||||
, outer = TRUE)
|
||||
#********************************
|
||||
#============================================
|
||||
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
# NOTE: This replaced B-factor distribution has the same
|
||||
# x-axis as the PredAff normalised values, but the distribution
|
||||
# is affected since 0 is overinflated. This is because all the positions
|
||||
# is affected since 0 is overinflated/or hs an additional blip because
|
||||
# of the positions not associated with resistance. This is because all the positions
|
||||
# where there are no SNPs have been assigned 0???
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue