244 lines
9.4 KiB
R
244 lines
9.4 KiB
R
#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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# TASK: Generate averaged affinity values
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# across all affinity tools for a given structure
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# as applicable...
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#########################################################
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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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#OutFile1
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outfile_mean_aff = paste0(outdir_images, "/", tolower(gene)
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, "_mean_affinity_all.csv")
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print(paste0("Output file:", outfile_mean_aff))
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#OutFile2
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outfile_mean_aff_priorty = paste0(outdir_images, "/", tolower(gene)
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, "_mean_affinity_priority.csv")
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print(paste0("Output file:", outfile_mean_aff_priorty))
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#%%===============================================================
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#=============
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# Input
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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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table(df3$mutation_info_orig)
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table(df3$mutation_info_labels_orig)
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# used in plots and analyses
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table(df3$mutation_info_labels) # different, and matches dst_mode
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table(df3$dst_mode)
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# create column based on dst mode with different colname
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table(is.na(df3$dst))
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table(is.na(df3$dst_mode))
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#===============
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# Create column: sensitivity mapped to dst_mode
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#===============
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df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
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table(df3$sensitivity)
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length(unique((df3$mutationinformation)))
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all_colnames = as.data.frame(colnames(df3))
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common_cols = c("mutationinformation"
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, "position"
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, "dst_mode"
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, "mutation_info_labels"
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, "sensitivity"
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, "ligand_distance")
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all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
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all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
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#===================
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# stability cols
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#===================
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raw_cols_stability = c("duet_stability_change"
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, "deepddg"
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, "ddg_dynamut2"
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, "ddg_foldx")
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scaled_cols_stability = c("duet_scaled"
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, "deepddg_scaled"
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, "ddg_dynamut2_scaled"
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, "foldx_scaled")
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outcome_cols_stability = c("duet_outcome"
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, "deepddg_outcome"
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, "ddg_dynamut2_outcome"
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, "foldx_outcome")
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#===================
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# affinity cols
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#===================
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raw_cols_affinity = c("ligand_affinity_change"
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, "mmcsm_lig"
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, "mcsm_ppi2_affinity"
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, "mcsm_na_affinity")
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scaled_cols_affinity = c("affinity_scaled"
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, "mmcsm_lig_scaled"
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, "mcsm_ppi2_scaled"
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, "mcsm_na_scaled" )
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outcome_cols_affinity = c( "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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# conservation cols
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#===================
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# raw_cols_conservation = c("consurf_score"
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# , "snap2_score"
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# , "provean_score")
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#
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# scaled_cols_conservation = c("consurf_scaled"
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# , "snap2_scaled"
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# , "provean_scaled")
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#
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# # CANNOT strictly be used, as categories are not identical with conssurf missing altogether
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# outcome_cols_conservation = c("provean_outcome"
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# , "snap2_outcome"
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# #consurf outcome doesn't exist
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# )
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######################################################################
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cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
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, raw_cols
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, scaled_cols
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, outcome_cols_affinity)]
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# cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols
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# , outcome_cols_affinity)]
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##############################################################
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#####################
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# Ensemble affinity: affinity_cols
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#####################
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# extract outcome cols and map numeric values to the categories
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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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#
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# df3_plot[, outcome_cols_affinity] <- sapply(df3_plot[, outcome_cols_affinity]
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# , function(x){ifelse(x == "Destabilising", 0, 1)})
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df3_plot = df3[, c(common_cols, scaled_cols)]
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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)%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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, 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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# end of script
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#===============================================================
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