#source("~/git/LSHTM_analysis/config/pnca.R") #source("~/git/LSHTM_analysis/config/alr.R") #source("~/git/LSHTM_analysis/config/gid.R") #source("~/git/LSHTM_analysis/config/embb.R") #source("~/git/LSHTM_analysis/config/katg.R") source("~/git/LSHTM_analysis/config/rpob.R") source("/home/tanu/git/LSHTM_analysis/my_header.R") ######################################################### # TASK: Generate averaged affinity values # across all affinity tools for a given structure # as applicable... ######################################################### #======= # output #======= outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene)) #OutFile1 outfile_mean_aff = paste0(outdir_images, "/", tolower(gene) , "_mean_affinity_all.csv") print(paste0("Output file:", outfile_mean_aff)) #OutFile2 outfile_mean_aff_priorty = paste0(outdir_images, "/", tolower(gene) , "_mean_affinity_priority.csv") print(paste0("Output file:", outfile_mean_aff_priorty)) #%%=============================================================== #============= # Input #============= df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv") df3 = read.csv(df3_filename) length(df3$mutationinformation) # mut_info checks table(df3$mutation_info) table(df3$mutation_info_orig) table(df3$mutation_info_labels_orig) # used in plots and analyses table(df3$mutation_info_labels) # different, and matches dst_mode table(df3$dst_mode) # create column based on dst mode with different colname table(is.na(df3$dst)) table(is.na(df3$dst_mode)) #=============== # Create column: sensitivity mapped to dst_mode #=============== df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S") table(df3$sensitivity) length(unique((df3$mutationinformation))) all_colnames = as.data.frame(colnames(df3)) common_cols = c("mutationinformation" , "position" , "X5uhc_position" , "X5uhc_offset" , "dst_mode" , "mutation_info_labels" , "sensitivity" , "ligand_distance" , "interface_dist") all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)] all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)] #=================== # stability cols #=================== raw_cols_stability = c("duet_stability_change" , "deepddg" , "ddg_dynamut2" , "ddg_foldx") scaled_cols_stability = c("duet_scaled" , "deepddg_scaled" , "ddg_dynamut2_scaled" , "foldx_scaled") outcome_cols_stability = c("duet_outcome" , "deepddg_outcome" , "ddg_dynamut2_outcome" , "foldx_outcome") #=================== # affinity cols #=================== raw_cols_affinity = c("ligand_affinity_change" , "mmcsm_lig" , "mcsm_ppi2_affinity" , "mcsm_na_affinity") scaled_cols_affinity = c("affinity_scaled" , "mmcsm_lig_scaled" , "mcsm_ppi2_scaled" , "mcsm_na_scaled" ) outcome_cols_affinity = c( "ligand_outcome" , "mmcsm_lig_outcome" , "mcsm_ppi2_outcome" , "mcsm_na_outcome") #=================== # conservation cols #=================== # raw_cols_conservation = c("consurf_score" # , "snap2_score" # , "provean_score") # # scaled_cols_conservation = c("consurf_scaled" # , "snap2_scaled" # , "provean_scaled") # # # CANNOT strictly be used, as categories are not identical with conssurf missing altogether # outcome_cols_conservation = c("provean_outcome" # , "snap2_outcome" # #consurf outcome doesn't exist # ) ###################################################################### cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols , raw_cols_affinity , scaled_cols_affinity , outcome_cols_affinity , raw_cols_stability , scaled_cols_stability , outcome_cols_stability )] cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols , scaled_cols_affinity)] df3_plot = df3[, cols_to_extract] ############################################################## # FIXME: ADD distance to NA when SP replies ##################### # Ensemble affinity: affinity_cols # mcsm_lig, mmcsm_lig and mcsm_na ##################### # extract outcome cols and map numeric values to the categories # Destabilising == 0, and stabilising == 1 so rescaling can let -1 be destabilising ######################################### #===================================== # Affintiy (2 cols): average the scores # across predictors ==> average by # position ==> scale b/w -1 and 1 # column to average: ens_affinity #===================================== cols_mcsm_lig = c("mutationinformation" , "position" , "sensitivity" , "X5uhc_position" , "X5uhc_offset" , "ligand_distance" , "ligand_outcome" , "mmcsm_lig_outcome") cols_mcsm_lig df3_lig_ens = df3[, cols_mcsm_lig] cols_to_numeric = c("ligand_outcome","mmcsm_lig_outcome") df3_lig_ens[, cols_to_numeric] <- sapply(df3_lig_ens[, cols_to_numeric] , function(x){ifelse(x == "Destabilising", 0, 1)}) cols_to_average_lig = which(colnames(df3_lig_ens)%in%cols_to_numeric) cols_to_average_lig # ensemble average across predictors df3_lig_ens$ens_lig = rowMeans(df3_lig_ens[,cols_to_average_lig]) head(df3_lig_ens$position); head(df3_lig_ens$mutationinformation) head(df3_lig_ens$ens_lig) table(df3_lig_ens$ens_lig) #=============================== # Filter ligand distance <10 # from globals else uncomment #LigDist_cutoff = 10 #LigDist_colname = "ligand_distance" #=============================== table(df3_lig_ens[LigDist_colname]% dplyr::group_by(position) %>% dplyr::summarize(avg_ens_lig = mean(ens_lig)) class(mean_ens_lig_by_position) # convert to a df mean_ens_lig_by_position = as.data.frame(mean_ens_lig_by_position) table(mean_ens_lig_by_position$avg_ens_lig) # REscale b/w -1 and 1 #en_aff_min = min(mean_ens_affinity_by_position['ens_affinity']) #en_aff_max = max(mean_ens_affinity_by_position['ens_affinity']) # scale the average affintiy value between -1 and 1 # mean_ens_affinity_by_position['avg_ens_affinity_scaled'] = lapply(mean_ens_affinity_by_position['avg_ens_affinity'] # , function(x) ifelse(x < 0, x/abs(en_aff_min), x/en_aff_max)) mean_ens_lig_by_position['avg_ens_lig_scaled'] = lapply(mean_ens_lig_by_position['avg_ens_lig'] , function(x) { scales::rescale(x, to = c(-1,1) #, from = c(en_aff_min,en_aff_max)) , from = c(0,1)) }) cat(paste0('Average (mcsm-lig+mmcsm-lig) scores:\n' , head(mean_ens_lig_by_position['avg_ens_lig']) , '\n---------------------------------------------------------------' , '\nAverage (mcsm-lig+mmcsm-lig) scaled scores:\n' , head(mean_ens_lig_by_position['avg_ens_lig_scaled']))) if ( nrow(mean_ens_lig_by_position) == expected_npos ){ cat("\nPASS: Generated ensemble average values for ligand affinity" ) }else{ stop(paste0("\nAbort: length mismatch for ligand affinity data")) } ################################################################# #===================================== # Affintiy (mCSM-ppi2): #D1148G for rpob DOES NOT EXIST for 5UHC #===================================== cols_mcsm_ppi2 = c("mutationinformation" , "position" , "X5uhc_position" , "X5uhc_offset" , "sensitivity" , "interface_dist" #, "mcsm_ppi2_affinity" #, "mcsm_ppi2_scaled" , "mcsm_ppi2_outcome" ) cols_mcsm_ppi2 df3_ppi2_raw = df3[, c(cols_mcsm_ppi2, "mcsm_ppi2_affinity", "mcsm_ppi2_scaled") ] table(df3_ppi2_raw$mcsm_ppi2_outcome) df3_ppi2 = df3[, cols_mcsm_ppi2] cols_to_numeric_ppi2 = c("mcsm_ppi2_outcome") df3_ppi2[, cols_to_numeric_ppi2] <- sapply(df3_ppi2[, cols_to_numeric_ppi2] , function(x){ifelse(x == "Descreasing", 0, 1)}) cols_to_average_ppi2 = which(colnames(df3_ppi2)%in%cols_to_numeric_ppi2) cols_to_average_ppi2 #=============================== # Filter interface <10 Dist_cutoff = 10 ppi2Dist_colname = "interface_dist" #=============================== table(df3_ppi2[ppi2Dist_colname]