#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" , "X5uhc_position" , "X5uhc_offset" , "position" , "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 , raw_cols_affinity , scaled_cols_affinity)] df3_plot = df3[, cols_to_extract] DistCutOff_colnames = c("ligand_distance", "interface_dist") DistCutOff = 10 df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 | df3_plot$interface_dist <10),] c0u = unique(df3_affinity_filtered$position) length(c0u) foo = df3_affinity_filtered[df3_affinity_filtered$ligand_distance<10,] bar = df3_affinity_filtered[df3_affinity_filtered$interface_dist<10,] wilcox.test(foo$mmcsm_lig_scaled~foo$sensitivity) wilcox.test(foo$mmcsm_lig~foo$sensitivity) wilcox.test(foo$affinity_scaled~foo$sensitivity) wilcox.test(foo$ligand_affinity_change~foo$sensitivity) wilcox.test(bar$mcsm_na_scaled~bar$sensitivity) wilcox.test(bar$mcsm_na_affinity~bar$sensitivity) wilcox.test(bar$mcsm_ppi2_scaled~bar$sensitivity) wilcox.test(bar$mcsm_ppi2_affinity~bar$sensitivity) ############################################################## df = df3_affinity_filtered sum(is.na(df)) df2 = na.omit(df) # Apply na.omit function a = df2[df2$position==37,] sel_cols = c("mutationinformation", "position", scaled_cols_affinity) a = a[, sel_cols] ############################################################## # 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") ###################################################################### ################## # merge: mean ensemble stability and affinity by_position #################### # if ( class(mean_ens_stability_by_position) && class(mean_ens_affinity_by_position) != "data.frame"){ # cat("Y") # } common_cols = intersect(colnames(mean_ens_stability_by_position), colnames(mean_ens_affinity_by_position)) if (dim(mean_ens_stability_by_position) && dim(mean_ens_affinity_by_position)){ print(paste0("PASS: dim's match, mering dfs by column :", common_cols)) #combined = as.data.frame(cbind(mean_duet_by_position, mean_affinity_by_position )) combined_df = as.data.frame(merge(mean_ens_stability_by_position , mean_ens_affinity_by_position , by = common_cols , all = T)) cat(paste0("\nnrows combined_df:", nrow(combined_df) , "\nnrows combined_df:", ncol(combined_df))) }else{ cat(paste0("FAIL: dim's mismatch, aborting cbind!" , "\nnrows df1:", nrow(mean_duet_by_position) , "\nnrows df2:", nrow(mean_affinity_by_position))) quit() } #%%============================================================ # output write.csv(combined_df, outfile_mean_ens_st_aff , row.names = F) cat("Finished writing file:\n" , outfile_mean_ens_st_aff , "\nNo. of rows:", nrow(combined_df) , "\nNo. of cols:", ncol(combined_df)) # end of script #===============================================================